The Engineering Projects

Hello learners! Welcome to the engineering projects where we are working on deep learning. In this series, we are at the part where Python is under our observation. In the last session, we saw the Python built-in functions and the practical implementation of some important pre-defined functions of Python. In the current lecture, you will learn about the fundamental concept of Python. It is not wrong to say that if you want to work in any high-level programming language, you have to understand its data types; otherwise, you will not be able to code complex or long programs using it. More details will be discussed in the next section, but before this, you should have a glance at the concepts you will learn in this tutorial.

• What are the data types?

• How are Python data types different from other programming languages?

• What is the syntax to use the data types in Python?

• What is the difference between floats and integers?

• How can you practice other concepts along with the complex number in TensorFlow?

• What is the casting process in the Python programming language and how can you use it for changing the type of data you enter?

• What is the “type” function in Python and how do you use it?

For your convenience and detailed learning, we are just discussing the numeric data types in this lecture, and you will learn more types in the next lecture because I want to discuss each type with the practical implementation of different operations on these data types.

What are Data Types?

Consider the case where we use different types of numbers in mathematics and apply the operations to these different types of numbers. We know that only numbers can be added, subtracted, multiplied, etc. The same is true for data types. When dealing with programming languages, we have to deal with different kinds of objects, and it is important to use them wisely because of the memory storage. Have a look at the basic definition of the data types in the programming languages:

"In programming languages, the data type is the basic concept that declares the type of object being used in a specific way, and based upon the data types, the memory space is occupied by the compiler."

In simple programs, such as the ones we used in our examples, we don't notice much of a difference when we occupy more space in the program, but this is not the best practice because, in long and complex programs, we need to use the exact data type. Hence, the compiler runs perfectly and we can apply the specific operations to the particular data type. 

The declaration of the data type is always required in most programming languages so that the compiler can fully understand the data type. So, you can declare the integer as a float and vice versa according to our will, but it causes problems in the compilation.  

Data Types in Python:

 As we always say that Python is an easy programming language, and here is one of the main reasons why. In Python, the compiler itself is intelligent enough to understand the type of data you are putting into it, so your program may run well if the code is well-written. It feels like a relief that you do not have to remember the names of different data types, and there is no need to memorize the space occupied by the data type so that you may compare and choose the perfect data type for your code. The name of the data types is almost the same as the other programming languages that we have mentioned before, but the way these work with different operations may seem a little bit different. The declaration of different data types in the Python programming language is given in the next section.

Syntax of Data Types in Python

The best way to learn any concept in programming is to learn the syntax first instead of going into the example and understanding how that concept works. When we talk about the syntax of data types in Python, this section is very important. You have to keep in mind that unlike some other high-level programming languages such as C++ and C#, you have only two items:

1. Variable name

2. Value of the data type

Now it's time to go over the various data types one by one. The syntax of all of these appears to be the same, but the examples will demonstrate the differences in how each of them works. For your convenience, the different data types are divided into six categories:

• Numeric

• String

• Sequence

• Mapping

• Boolean

• Set

These categories also have sub-categories, and the same category works in the same way, but you have to keep the differences in mind, especially if you are a beginner because it becomes easy to understand the code and the way you work with it. Other details will be clear when you see the implementation in the tensor flow. For this, I want to share the procedure for starting code in TensorFlow in a straightforward way:

• Fire up your Anaconda navigator.

• In the environment sector, you have to search for “Jupyter Lab” and hit the launch button there.

• A new local host will appear in your browser. Go to this local host. 

• In the new cell, begin coding. 

Numeric Data Types in Python

We all know what the numbers are, and there is no need to provide you with the details of the numbers, but to refresh the concept in your mind, we have to tell you that there are three classes of numeric data types that are introduced in Python:

1. Integer

2. Float

3. Complex

A short description of each of them is given next:

Integers in Python

The first and simplest data type that you will see in every tutorial is an integer. These are whole numbers and do not contain any extra parts. These are the signed integers that have a non-limited length. It means that on the positive side of the numbers, you can have the length of an integer as long as you want. One thing that has to be considered here is the memory of the system you are using, but when purely talking about the integer, it does not have any limit in length.  

Python Integers in TensorFlow

Are you ready with your TensorFlow to practice all the data types and apply the different operations on them? Ok, we hope your TensorFlow is launched, and have a look at the simple representation of the integer in it. 

integer=1257859304284756327

a=3824374269873874

print("integer= ", integer)

print("a = ", a)

add=integer+a

print("integer+a = " ,add)

Here, you can see that:

• We can name the variable anything we want, whether it is a special name or any combination of the alphabet, but it must follow the rules that we specified in the previous lecture. The results of the code are given next:

• The length of the integer does not matter, as integers can be long or short according to the requirement. 

• The addition can take place with the integers. Similarly, other operations such as subtraction, and multiplication is also possible. 

Float in Python

The next is the float, which in some cases looks like an integer, but if you are a science student, you must have the idea that integers and floats are not the same. The floats contain a decimal floating number just after the integer part. So, even if the number after the decimal point is zero, it will still be called the "float." Another thing to be noticed in the floats is, you can use the 15 digits in the float, and these are not unlimited as the integers are. 

Python Float in TensorFlow 

In TensorFlow, I want to perform the subtraction on the float this time, and I want to show you by code that when we do not add any decimal part to the float, the compiler itself adds the zero in the decimal part and provides us with the result. Moreover, you will observe that it takes the most significant number after the decimal part; therefore, in the code, when we add the zero as the last digit, it ignores it and the results are shown with the significant numbers.

Complex Numbers in the Python

We've all learned about complex numbers in math class, and according to the requirements, these complex numbers play an important role in programming. The representation of the complex number in Python is a little bit different than in mathematics class. Have a look at the code, and after that, we will discuss it in detail. 

Complex Numbers Using TensorFlow

The representation of the complex number is the same as what you were expecting. We have to use the i and j for the complex part, and the sign of addition or subtraction represents the difference between the real and complex parts. 

# Step 1

z = complex(a,b);

print(z)

# Step 

print ("The real part of complex number is : ",c.real)

#Step 3

print ("The imaginary part of the complex number is : ", c.imag)

• Here is the first step, we have assigned the duty to the compiler to make a complex number for us. For this, we have used the “complex” built-in function. We have discussed it in lecture number 10 of this series. 

• The step is completed when we print the complex number to show you how the functions are working here. 

• We are naming the complex number “c” and keep in mind, it consists of the real and imaginary parts. 

• In the next step, we are simply using the “real” and “img” keywords to separate these parts of the complex number. 

• The good thing is, we are using the same line to print the description and the result of the code. You can also do so by using the additional line of print, but I like simple and shortcodes. 

So, the overall result can be observed in the following image:

The result is shown in the form of floating numbers, and in our case, the real part is 0 by default. 

Casting in Python

Till now, we have seen simple declarations in the Python programming language, but now it's time to discuss another method of variable declaration, which is casting. Because of the various forms in which metals are cast, we've heard of this term before. The concept of casting is related to the same idea. In Python, this process is defined as:

"Casting in Python is a process in which the original type of the variable is changed to any other type by specifying it in the code in a particular way."

We have mentioned before that the Python compiler automatically detects the type of content, and therefore, in some special cases, we want to occupy the space from memory that does not match the value you are entering. If still it is confusing for you, do not worry because it will be clear in just a bit when you will look at a simple example to do so in TensorFlow.

a=23.56

b=type(a)

print("Here 'a' is a float", b)

c=int(23)

d=type(a)

print("After using the casting, the result is" , d)

Here is the output of this code, which tells you the details by itself.

The type function was covered in the previous lecture, so I hope you understand what it does. If not, you can review the previous lectures. Since the data types are so long that it becomes difficult to explain in a single lecture, you will get the details of other data types in the next lecture. Until then, you must continue to practice with these data types. We have seen the introduction of the data types and the ways to work with the data types in Python. We've also seen a comparison of other languages and Python on the same topic, and this lecture is heavy on numeric data types. I hope it was fruitful for you and that you will go to the next lecture for more data types.

Hey peeps! Welcome to an exciting tutorial on Python in which you will learn about the Python reverse list. We are on a series of deep learning phases where Python is under our observation. In the last tutorial, we saw the variables in Python and practised many codes in the TensorFlow. Today, we are interested to practice many interesting methods in an easy way. These are the built-in functions and you do not have to be an expert in the programming to perform them in TensorFlow. All you need is to read this tutorial and have the TensorFlow working fine. Before going deep into the topic, it is important to have a look at the list of content that you will learn today:

• How do you define the python reverse list?

• Why we should know about the python reverse list or keywords?

• How do you practice the codes of Python reverse list in TensorFlow?

• What is the core difference between some related keywords that seems to be work relatively?

• How do you run some loops in the Python while working on the TensorFlow?

All these concepts will be cleared by discussing them in detail and while using the codes, you must keep in mind, there are more than one way to run the program in your own way but the one that we have defined in this lecture are the precise one and these are enough to understand the concept that we want to share with you. So work smarter then to word harder.

Python Reverse List

We know that there are some rules that have to be followed when you are naming your variables in python. Usually, these rules are applied to almost all the present programming languages but we are specifying Python because we want to connect this discussion with the Python reverse list. There are some restricted words that cannot be used as variable names. These are pre-defined in each language. You can define it in the following way:

"The Python reverse list (also known as keywords) is the complete list of pre-defined functions that are already stored in Python and these particular names are not allowed to be used as the name of variables while coding."

If you go into the detail, there are hundreds of reverse words in Python but this will be out of the scope of this course. For your information, we have added some very common keywords that you have to learn and practice to keep in mind. IT WILL HELP YOU A LOT WHEN YOU WILL WORK WITH THE COMPLEX AND LONG CODES. In Python, the following table is useful to understand what kinds of names are not allowed.

The list does not end here, but I think it is enough to understand what types of variable names must not be used when you are practising deep learning with the help of TensorFlow (in our case). 

Python Reverse List in TensorFlow

Once you have read about the reverse list, you must practice it for the practice. Here, we are using some of the variables from the list given above. To practice it on TensorFlow, you have to follow the steps given next:

• Open your Anaconda navigator to use TensorFlow.

• Navigate to the environments and start the Jupyter lab.

• The screen here will show you the cells where you can write the codes. 

• Start writing the codes using the keywords from the list mentioned in the above table. 

Def in Python

The declaration of a function is done by using the “def” keyword. There is no need to tell the compiler the type of variable. After that, you can use this variable in other operations. Have a look at the code.

def welcome(name):

print (f"{name}, Welcome to theEngineeringProjects")

welcome ("Student")

The output of this program is given next:

Hence, you can see that we have declared the variable “welcome” and then provided the string in which we are using the function that we specified for ourselves. In the next line, we simply provide the value of the variable for that given string. You can simply change the value of a function in the last line to change the name with the same string. 

False in Python

This is a different type of keyword than in the previous case. The value “false” is declared by the compiler if we are providing information that is universally wrong. The best example in this regard is the one in which we are trying to equilibrate two different values of numbers.

The compiler is intelligent enough to clarify that the command given by you is not right. 

true in Python

This is the other simple keyword that is shown by the compiler itself, and if you are familiar with the keyword “false” given above, then it is obvious that if a universal truth or condition is fulfilled, the compiler will provide the answer in the form of the “true” keyword, as can be seen in the next image:

Another thing to notice here is, the string, or other data types may also be used with teh equality operation but as we are mentioning again and again, the Python is the case sensitive therefore, if the alphabets are the same but the case is different, the result will be contrary to the one given above:

The “L” of the second name is capitalized and therefore, the compiler is recognizing it as a different letter. It is the reason we are getting the “false” keyword as result. 

del in Python

 As you can see, del is the short form of the “Delete” operation, and therefore, you can use this to delete any specific entry, value, or object from the list or the code. Here is an example of how to do so:

subjects = ['Physics', 'Chemistry', 'Biology']

print(subjects)

    #applying the delete option

del subjects[1]

print (subjects)

When we put this code in our cells, you will get the results as expected:

At the start of the code, we declared an array of named subjects. You will learn more about these concepts in detail, but for now, just keep in mind that we have a group of sciences in our array, the index of which starts from 0. So, when you delete the first entry that is under the second name, you will get the list with only two entries.

None in Python

Here is another keyword that is used in Python for your convenience. You can think of the “None” keyword as a blank space or an empty container. Take the example of the case when the user does not put the information required on a survey that is necessary to answer, then he/she gets the error from the website that this is a required field and therefore, the user has to put the information in it to move forward. This is best understood by the code given next:

Here, you can see the error message that appears when the required field is empty and the user wants to proceed. 

If statement in Python

The “if” is a special keyword in Python, and you cannot name it as a variable because it is against the rules. This is the name of a loop, and you must know that loops will be discussed in detail when you proceed in this series; therefore, we will not explain too much about this keyword. But for now, have a look at the code given next to get an idea about the workings of this reverse word

number = 300

if number >= 18:

 print("You are eligible to become an Engineer")

Output of this code is given as:

raise in Python

Here is an interesting keyword that uses words in a very useful way. We have seen the error indication in different websites and other platforms where the user input the data and if it is not according to the rules of the input data then the screen shows the warning about the error. It can be done with the help of the raise keyword in Python. Let us take the case in mind where you have to provide your name in the form, and if the user provides the numbers rather than the name, then it will throw an error at you. 

name = 123

if not type(name) is str:

       raise TypeError("Only strings are allowed.")

So, it is clear that you have to put the name in the form of a combination of alphabets. By looking at this program deeply, we have seen the following points:

• It is important to specify the type of content that you want to allow.

• The “if statement” is also used in this program so that we can use more than one keyword in one line. 

• You can also change the type of content by specifying it in the first line. 

• The name of the variable does not matter. 

• By deleting the word “not” in the second line, you can invert the whole program. 

• In advanced programs, you can add more than one condition to apply all the necessary information so that any illegal way to type the name can be detected and the error may be shown. 

return in Python

The return keyword looks like the print function in the code, but both of them are not the same. The return function combines two or more results, and then control is given to the print function, which displays the results on the screen. Here is the program to do so. 

def sum(x, y,z):

       return x + y + z

print (sum(55,7,34))

The TensorFlow output is as follows:

Here, we have written a program that calculates the sum of three numbers. The start is done by using the define function, and here, we define a function that defines the pattern. This pattern will be used in the future. In the second step, the “return" function specifies the way the three components will work. In the third and last step, the values are put into the print function so that it may show us the result. This can be done in another way if we initialize another variable in which the result of summation is stored, and then we put that particular variable into the print function. The second way is more practical, but it occupies more space. 

In addition to this, you must know that you can specify any number of elements in the formula in the first step. If the pattern contains more than three elements and you do not have a large number of elements to test, you can enter "0" in place of the extra elements, but you must follow the pattern and cannot enter fewer than the specified number of elements.

elif in Python

Here is the last keyword to explain in this lecture. It is the combination of two words, “else” and "if,” and it is used in the loops. You will practice a lot about the loops in the coming sessions; therefore, I am not explaining it much. But for now, you must know that when you want to add more than two conditions to a program, you use this keyword. In the language of C++, we use the else if the keyword for the same purpose. So, have a look at this program:

So, the user with knowledge of artificial intelligence is more likely to get the skills of deep learning, and we have made this program to show you this.

So, it was a helpful tutorial to learn a lot about the Python reverse list. These are the keywords that are pre-defined in Python, so it is not advisable to name your variable exactly like these. If you do not follow this rule, the compiler will be confused between your defined variable and the keyword saved in it, and therefore, it will throw the error. You must know that there are more words in the list, but for now, it is enough to understand and practice the words mentioned here.

Hey learners! Welcome to the new lecture on deep learning, where we are using TensorFlow to learn it with the help of Python. Previously, we worked on the syntax of Python, and now it's time to discuss the variables in detail. There are some variables that you will learn about as well as get hands-on experience within TensorFlow. These are important concepts that will help you throughout your coding career. If you are new to programming, this is a crucial concept for you, and if you know it already, you can use this tutorial to polish your concepts. We will move forward after looking at the list of content for this lecture:

• What are the variables in Python?

• How do you assign the value to the name of the variable in Python?

• What are some rules to define the name of the variables?

• How do you declare or initialize the variables in Python?

• Can you get the type of variable that is declared before?

• What is the difference between statically and dynamically typed programs?

• What is the purpose of re-declaration in Python?

Introduction to Variables in Python

In the previous lecture, we mentioned the name string, and the difference between the string and character is just the amount of storage these two occupy. The reason these are important to understand is to use an accurate way to store our data. This will be clearer when you see the introduction of the variables in Python:

"In Python, the variables are the containers that store the information in them and are defined as the name given to the location in the memory."

We all know that when a program is saved in the compiler, it allocates a specific amount of memory. Variables in Python work differently than variables in other programming languages such as C++ and C#. In these languages, the programmer has to define the type of the variable and allocate the specified amount of memory required to store that variable. Still, in Python, the program statically types. The other type of program in this regard is dynamically typed programming. For your convenience, I have made a comparison between these two types:

So, Python is easy to understand, and you do not have to work hard for the declaration of variables of a different kind; most of the time, the declaration is super easy. You will see this when we work on the examples of the variables in just a bit. 

Assigning the Values to the Variables

When declaring the variables, you have to be very careful about the data you are placing in different containers. The reason behind this is, there are hundreds or more than it variables in the code when we start professional coding in Python. In such cases, it is important to memorize the name and working of the variables instantly. It is not advisable to name the variable without following any logic to memorise the exact information about that particular variable. The list of the variable is shown on the side of the TensorFlow but the working must be shown with the name of your variable.

For example, if you are declaring the variable that calculates the sum of numbers then the name of that particular variable must be “sum” or “addition”, or any other word that describes the function otherwise if you are naming it as “x”, or “var1” then you have to think for some moment that why you had initialized these variables. This rule is also beneficial to the case when the code is read by the other person or if you are sharing your code with the other person to work further on it.

Here, you must know, the code that we are practising is tiny and these are easy to understand to make the points clear to the beginners as well but at a professional level, the coding is different and you have to make sure that you are writing a clear, clear, and easy to understand code as the code in such cases are too long and contain many concepts in a single line sometimes.

Rules For the Variables in Python 

For the declaration of the variables in the Python programming language, you have to follow certain rules, and for the convenience of the reader, we have made the points, and there is no need to memorize them; you just have to read them first.

• The name of the variable must always start with the alphabet or the underscore character; otherwise, it is against the rules. 

• The variable name does not start with the number. You can use the number in between the alphabets of the variable name, but not at the beginning. 

• Symbols are not allowed to be used in the variable name. In other words, you can use only alphabets (A to z, in which the case of the alphabet does not matter), numbers from 0 to 9, and the underscore only. As a result, you can't use symbols like @, #, and $.

• Variables name is case sensitive. This can be understood with the example that the variables Type, Type, and Type are totally different, and these are the names of three variables. 

• The declaration of the variable may be done with the help of a single alphabet as well such as x, j, y, etc. But it can't be a single number such as 1, 2 3, and so on.

These are the universal rules, and usually, the programmer creates his or her own code of conduct in order to ensure that he or she always follows the same route to write the code, making it easier to understand the old codes written by him.

Variable Declaration in Python vs. Other Programming Languages

Another reason why we say that Python is an easy and convenient programming language is that the declaration of different types of objects is easy in it. As we are studying the variables right now, I must tell you that in other programming languages, you have to declare the type of the variable first and then give it a name. You can set the variable's value either at the declaration or on the following line by providing the name and value. An example of the variable declaration in C++ is given below:

Int x=78;

or int x;

x=78

In contrast, when working with variables in Python, you do not need to define the type of the variable and can simply give it a name and a value. As a result, the program's work is simplified because the Python compiler is intelligent enough to recognize the type of the variable on its own and does not require the user to specify it. Here is the example that verifies the information given here:

Similarly, when you are declaring the string or character, you will simply declare it with the name and value without specifying the type. 

#Declaring the Character

character='Python'

print('Character is "', character,'"')

#Declaring the String

string="We want to work with deep learning through Python."

print('string is "', string,'"')

Just look at the output, and then I'll discuss the details of the code.

The following points are proven with this code:

• The name of a variable may be anything, and the value determines the type of variable in Python. It can be understood with the fact that even if we name of a string is declared as a character but detail the value has double quotation marks around the values, the compiler will read it as the string and it will occupy the space in the memory accordingly. 

• Comments in the code are totally ignored by the compiler, and these are helpful to understand the block of code defined by it. 

• In the print function, if you want to show the values as they are, you write them in single quotation marks, and if you want to declare the value stored in the variable, the variable is written as it is. 

• To separate the printed message and the name of the variable, we use a comma between them. 

• Using the single "print" function, you can print multiple variables or messages. 

• While printing more than one output on the screen, you do not have to use indentation these print functions are non-consecutive. 

• In Python, the single quotation marks are equal to the double quotation marks but as we have seen that both of these are different in other programming languages, to illustrate the type, we have used both examples. 

The types of data will be given in the next session, where you will learn a lot about them.

Getting the Type of The Variable in Python

If you do not know the type of variable and want to get the related information for different types of operations, Python has a special pre-defined function that works in a simple way. You just have to put the value you want into the “type” function. The syntax of the “type” function is given next:

print(type(variable name))

Hence, the type of the variable will be printed on the screen. This can be best understood when you see some examples in TensorFlow. To do this, write the following code in the TensorFlow cell:

a=56

b="Deep Learning is easy with TheEngineeringProjects.com"

print(type(a))

print(type(b))

As soon as you will pop the play button, you will get the results as follow:

The reason why “class” words are used here is that Python is an Object-Oriented programming language, and it makes the classes perform the operations; therefore, it has presented the results in the form of classes. 

Re-declaration of Variable in Python

Once you have learned about the declaration of the variable, you might be thinking that the single value may be assigned to the single name only, but it is not true all the time. Suppose if you are using a large number of values in a program that will be used only once and you do not want to suggest single name to a single value, you can declare the variable again and this method is called the re-declaration. Have a look at the example to do so:

a=45

print("The value of the variable is", a)

a=90

print("The value of the same variable is now", a)

So, you have simply masked the first value and given the same name of the variable to any other value. But in such cases, you cannot get the previous value back until you provide the first value to the name again. This is the reason why we call them "variables,” which means the non-fixed process. The values in the variable keep changing over time according to the needs of the program and the code written by the programmer.

Hence, it was the day when we learned a lot about the variables in the Python programming language. We have seen a lot of information about the variables and seen how you can introduce and work with the Python variables in different ways. We have compared the declaration of the Python variables with the other programming languages and also got information about the statistically typed programs versus the dynamically typed programs. Moreover, it was interesting to know about the type function and the declaration of the variables in Python. I hope it was an informative tutorial for you and that you will practice more to get experience coding in Python.

Hey learners! Welcome to another deep learning tutorial, in which we are beginning the practical implementation of Python on the TensorFlow library. We installed and checked TensorFlow in detail while we were in the previous lecture, and today we are going to use it for our practice. We have checked the presence of a perfectly installed library of TensorFlow in our tutorials and seen the basic structure of this library. As a result, we will skip the details and jump right into learning Python. In this tutorial, the main focus will be on Python instead of learning the workings of TensorFlow. You have to remember one thing: all the discussion will be from the point of view of deep learning, and it is not a general tutorial in which you will learn to develop apps or have a discussion about the details of Python; we will learn all the basics in detail, and after that, you will see Python in the field of deep learning. But first of all, we are showing you the list of content that you will learn in this lecture:

• How do you introduce the syntax of Python?

• What is the syntax error, and how do you define the other types of errors?

• How can you execute the instructions of Python in TensorFlow?

• How do we print the message in different ways while using Python?

• What is an indentation in Python, and why is it important?

• How can you use comments in Python?

These all concepts will be cleared as we are going to discuss all of them one after the other with the practical implementation and no step will be missing so let us discuss the detail of each of them.

Syntax of Python

As we all know, the syntax is the most important thing that you must know, even if you are typing the simplest program in any programming language. All these programming languages are recognized by their syntax, and even a single mistake with a semicolon matters a lot. When talking about Python, we have mentioned many times that it is a simple and easy programming language that is easy to understand. Before going into detail, I want to show you the definition of the syntax, and after that, we will link this to Python.

"In programming languages, the syntax is the set of predefined rules that communicate with the computer and tell it how to read the code."

 This is the combination of alphabets, numbers, and symbols in a specific way and the programmer always has to follow these rules otherwise the computer will throw errors. Most of the time, the compiler shows the suggestions if the code is slightly different from the one that must be followed. But it is not true all the time. There are two types of errors:

• Syntax error

• Logical error

• Runtime error

The details of each of them are given next:

Syntax Error in Python

In the compiler, syntax errors occur when the programmer does not follow the syntax of the language exact, and the compiler is not able to understand the exact operation. Therefore, it shows the error and is not able to perform the required function. As the instructions are pre-defined, they show the list of errors and the possible solutions to the problem. This solution may be in the form of an instruction, a statement, or any other clear indication of where the error is present and how you deal with it. 

Examples of Syntax error in Python

There are many ways to show you the exact information, but I am choosing the basic way to do so because many beginner-level programmers are also there and they need the information from scratch. The discussion above will be clearer with the help of the example given below.

Here you can see that the compiler does not know the correct spelling of the command given to it, but the point to observe is that you get the suggestions and other statements to solve the error.

Other Types of Errors

The other types, which are the logical and runtime errors, are important to discuss here because, in this way, we can differentiate them better. When talking about logical error, you have to be very clear about your instructions and have to provide the best path to the compiler so that it may solve your code and present the right output. If there is a mistake in the logic, or, in other words, if you are telling it to compare illogical things or do a task that is not possible in real-time, it will not accept it and will do exactly as per your instructions, but the required results will not be obtained. This situation worsens when the programmers' concepts for solving the given problem are unclear, making it difficult to identify the error in the code.

Contrary to this situation, we observe another type of error that is a run-time error. A pure run-time error is one in which the logic and syntax are completely correct but you still do not get the desired results because the program does not receive the error during compilation but is unable to retrieve the required information from the code at runtime. The difference between logical and runtime errors is that in logical errors, the program is not compiled well because of illogical or incomplete information, whereas in runtime errors, the compiler is able to compile or gather all the information but the program is not complete because of the missing information that is to be gathered from the other piece of code. At this time, I do not want to discuss more these two error types more; my focus is on the syntax error. Truss, have a look at the table below that describes all these errors at a glance.

Executing The Python Instructions

The Python instructions are clean to execute, and you have to be very clear about the syntax before getting started. We believe that some of the instructions are well known to you, but for the sake of practice and to prevent any gaps in learning, we are discussing all the basic information here. 

Printing the Message in Python

The first program that is obvious to practice while programming is the Hello World program, which is always practised when learning any programming language. But I want to do something different. Instead of writing the "hello world" program, I would like to write any other message. For this, you just have to follow the instructions given next:

• Search for the installed software of “Anaconda Navigator” and run it on your PC. 

• Go to Jupyter Lab and launch it.

• You have to write the code in the cells given on the screen. 

• The syntax for the printing of a message in the form of the string is given as

print(“You message”)

So, you have to follow some important rules all the time when you want to print something. 

• Write the keyword “print” to show any message.

• The spelling must be exactly the same; otherwise, you will get a syntax error. 

• The text should be wrapped in parentheses. 

• The right data type should be mentioned. (You will learn a lot about the data types in the coming lectures, so do not bother about it.) You must know that you wanted to print the string message; therefore, you have used inverted commas.

• There is no restriction on printing the specific message between the commas; in other words, if you make a spelling mistake or make another type of statement, the message will be printed as is and the compiler will not throw an error. Yet, the syntax should always be followed strictly. 

Once you have followed all the rules, the result obtained on the screen will be like this:

Here, you can see that the message is printed as it is in the next line, which is the output line. 

In programming, there are different ways to perform the same task, and you can choose any of them. Now, the printing of the message can also be done in another way if you do not want to print the string. You can do so with the help of variables.

We hope it is clear to you, but if you have any ambiguity, you can learn this in our upcoming lectures. For now, just look at the fact that we have stored the message in the variable and then fed this message into the print command.

Rule of Indention in Python

While we say that coding in Python is simple, you may be surprised to learn that there is an indentation rule in Python. We all know that indention is the way of writing or typing something in with a gap at the start. Here is an example of the code without indentation.

You can see that the syntax and all other parameters are okay, yet you have to write the blacks with indentations to identify their separations. So, we are just making these simple changes and trying to run the program. Let’s see what happens.

Important Tip 

As a programmer, you must know that you can specify the number of spaces of your choice, but the range is between one and four spaces. Moreover, if you have a bigger code than this and provide more than the required number of spaces, you will again get the error of too many spaces. So the number of spaces must be precise. Moreover, for such codes, the number of spaces must be equal. For example, if you are using more than one print function, then these two must be equally indented; otherwise, you will get the error.

Comments in Python

Most of programming languages have the "comment" option. These prove convenient and are also interesting to know about. The comments are defined as:

"The comments in the programming languages are the additional notes and instructions that are saved by the programmer itself and ignored by the compiler."

The comments start with special symbols to tell the compiler that the line after this symbol is to be ignored. All the programming languages have different symbols to do so, but in Python, the hashtag sign is used, and the line after this hashtag is totally ignored so that you may store the notes in that line.

Observe that your message is ignored fully, and the compiler has just printed the message without any error. Comments make the code more readable and easier to understand. The comments are usually italicized and have a different color than the usual code.

Therefore, we have learned a lot about the syntax of the programming language and how important it is to follow the syntax. We have seen the different types of errors and have learned the print command, indentation, and comments in detail. It was an interesting lecture and be with us for the advanced learning.

Hey students! Welcome to the fantastic tutorial of this series, where we are talking about deep learning. Till now, the discussion has been about artificial intelligence, deep learning, and TensorFlow, but today’s lecture will change the type of discussion from the previous one. You will see that we will now talk a lot about the Python programming language and will connect all the discussions with TensorFlow. You will see the reason for both of these choices in just a bit, but before that, I want to show you the list of the concepts that will be cleared today:

• What is the Python programming language when we are talking about deep learning?

• Why did we select Python for deep learning when there were other options?

• What is the importance of TensorFlow when we are learning deep learning by keeping Python as our programming language?

• What is the Tensor Processing Unit?

• Do we have the projects on GitHub for inspiration for the learning of this course?

Introduction to Python for Deep Learning

The general introduction to the Python programming language differs. You can see the introduction of Python in a different way. Still, as we are learning it for the sake of deep learning, I am keeping that perspective in mind and introducing it as:

"Python is the programming language that seems to be ideal for deep learning because it has special libraries and an AI-based structure of working that makes it perfect for AI and related fields."

We have some other options for the working of deep learning subjects, but Python is one of the most convenient languages if we are talking about the syntax, working, or cleanliness of the programming language. You will see more points that prove the importance of Python for the working and learning of the subject and the applications of the deep learning subject in the next section.

Why Python for the Deep Learning?

Artificial intelligence is a different subject from other programming languages. For the implementation of these unique concepts, it is important to have a programming language that provides easy working and other different facilities for the best performance. Python is near all these requirements, and our discussion will be clear with the evidence of some points about Python given next:

Simplicity of Python

The first and most important point that every Python lover mentions is the simplicity of this programming language, which is a plus among other options for programming languages. Python provides us with clean and simple syntax in which simple lines perform complex operations, and you do not have to take care of difficult syntax such as semicolons at the end of every line, etc. In this way, developers have to focus on machine learning and deep learning instead of solving the difficult and small eros that are difficult to detect by the user. This language is close to the human language, and Python is one of the most popular programming languages because programmers are attracted to its ease, especially beginners.

Consistency in Python Language

When it comes to deep learning, the first thing to remember is consistency. It is because of the complex and unique concepts involved in the training process that every learner faces while practicing artificial intelligence and related subjects. As a result, Python is the programmers' first choice because it is less error-prone and has the fewest lines of code for the various functions used in deep learning. 

People who use Python are the most consistent in deep learning while working with it because it is a general-purpose language, and thus various types of training and work can easily be done with the help of this language; you do not have to work on different programming languages and get almost all of the work done with Python. 

Interesting Frameworks and Libraries of Python

Working with machine learning is not an easy task, and if you start from scratch without the proper working environment, it may be difficult for you to understand and then train the neural networks in a better way. The Python frameworks and environment greatly assist programmers in this regard, and the majority of the work is ready for use by learners. In this way, they can have the easiest way to understand the concepts and then apply them with the help of Python frameworks in a better way.

To understand the importance of libraries in deep learning, it is better to understand the true meaning of libraries in programming languages:

"The software library in the programming language is defined as the set of pre-defined codes presented already for the users that perform the common tasks in just a few simple steps, and programmers do not have to write the same code again and again to perform these common tasks."

Python has fantastic libraries that are not only useful in deep learning but are proving efficient in different fields. When you move forward in this course, you will see this in action. There are several libraries in Python, but those that are closely related to machine learning are the source of attraction for us. Some of these libraries are listed below:

For machine learning and related fields such as deep learning, we can use Keras, TensorFlow, and Scikit-Learn. We have had a great discussion about these libraries in the past, and I believe you understand why they are important to us. Yet, if you are interested, you must know that scikit-learn has many algorithms related to clustering that include random forest, k-means, gradient boosting, and many others that are working best in this field. 

Numpy is used in scientific research and data analysis.

SciPy is also used for advanced computing, and as you might expect, the "Py" denotes that it is a Python library. Moreover, Panda is used for the data analysis. 

Platform Independence for Deep Learning

When talking about programming languages, the independence of the platform is important because the ease of the platform does not work if you feel uncomfortable learning these languages. Python provides us with independence because of its multipurpose libraries and ease of working. No matter if you are a Windows user or have macOS, you can use this language for your learning and its applications. For all types of operating systems, you can create standalone codes with the help of Python. In other words, no interpreter is required, and your code can be used on multiple operating systems.

Other features are also discussed in the previous lectures, such as the great community and the help of professional developers in those communities that are best for a learner to grow in a professional way. Usually, the problem that the programmer faces is unbearable because a simple code is enough to stop your work. In such cases, it feels like a blessing to have a large community that has faced, experienced, solved, and tackled the same technical issue and has the experience to solve your problem in an efficient way.

 Here, let us again review the process of working in the deep learning applications and then make up our minds as to how all the features of the Python programming language are helping us at every step of the deep learning applications.

TensorFlow and Deep Learning

Till now, we have seen the features of TensorFlow and read a lot about the perfect match of this library with deep learning, but now I am going to start working on it, so it is important to know why and how this library was introduced to the market and what the features are that make it perfect for this course.

In 2015, Google launched TensorFlow under an Apache license, and at that time, it was a unique and interesting library that attracted people towards it without any delay. This was the reason why, in 2019, Google represented its updated version with the name "TensorFlow 2.0." If you are wondering why people use it with this interest, then you must know that it provides flexibility, and therefore, several applications are available for the users of TensorFlow in the market.

TensorFlow is one of the most popular libraries in the fields of research and commercial use. The ability to run with the help of more than one CPU and GPU provides us with tremendous speed and fantastic applications that are not possible in all libraries. In other words, we can say the architecture of TensorFlow makes it popular. 

Tensor Processing Unit

This may be a new term for you, and we have not put much light on this topic before; it will not be used in our course, but today I found it important to discuss it with you because we should know each and every important point about our focused library. In 2016, Google announced the application-specific integration circuit, or ASIC, that was specifically designed for machine learning and was customized with TensorFlow.

The proper introduction to this ASIC can be given as follows:

"The Tensor Processing Unit is a programmable artificial intelligence accelerator designed to provide high throughput for low-level precision, such as 8 bits, and to use the running mode rather than training the system."

The updated versions of the Tensor Processing Unit have more performance, and the detail can be seen in the given table:

The fourth generation was also introduced, with performance that was twice as good as the previous versions. When announcing the TPU for the first time, Google revealed that it has been using it for more than one year and is getting perfect results and better performance from its system. 

TensorFlow Projects on Github

If you are a professional programmer, then you must know the importance of GitHub in different fields of computer science. Usually, it is simple to describe the significance of a code or piece of software based on its popularity among GitHub programmers because it allows developers and programmers to practice their codes with the help of this fantastic community. When it comes to TensorFlow, you will be surprised to learn that there are over 1500+ projects available for you to practice and learn TensorFlow, as well as learn about the practical applications of Python with TensorFlow. One of the best things about this library is the constant updating that makes it the real cherry on top. Thus, if you are a user of TensorFlow, you will get more and more interesting features all the time, and your skills will never get old. Not only that, but the older versions have the best working features, so if you are not ready to try the updated features, you can easily use the older ones and add creative ideas in the perfect way, so the choice is entirely in your hands.

Hence, it was an interesting and fantastic lecture in which we learned a lot about the Python programming language and TensorFlow. We had previously read about both of these, but this time we were going to start the practical implementation, so we learned it thoroughly while keeping previous knowledge in mind. We started with a basic introduction to Python and read a lot about it with the goal of deep learning. Moreover, after that, we have seen the reasons why we are choosing TensorFlow for the practical implementation because we have other options as well, but TensorFlow is our best choice. Today, the interesting thing was the information about the Tensor Processing Unit which is an ASIC designed with TensorFlow maybe, you will see its practical working in the future in this tutorial but right now, we have the focus on the TensorFlow basics and you will learn it in the coming tutorials. Till ten, you have to practice more and more about the concepts that we are describing in our sessions.

 The cloud software industry has begun developing new working models that have been especially needed during the pandemic. However, even though the pandemic is almost neutralized, the cloud market continues to grow. According to a new Forrester report, global software spending will continue to rise, reaching a CAGR of 10.3% from 2021 to 2023.

Therefore, in this article, we will learn more about cloud technologies and their trends.

What is SaaS?

Let's start with the definition of saas development . This is a special software model that is provided on a subscription basis. And most often it is a cloud solution. Such a concept as software as a service is deciphered.

Top Cloud SaaS Trends

The cloud can be a very secure storage solution if implemented correctly, which is why there has been rapid growth in recent years. Today, more and more companies use cloud computing for data storage. This is an expanding technology market that has changed a lot over the years. For this reason, it is important to keep an eye on trends to keep up with the competition.

FinOps

In 2022, the need for cloud cost management services is one of the main cloud initiatives, and further growth of these services is expected in the next year.

Cloud operations are typically decentralized, making costs difficult to predict or control. For this reason, although one of the goals of companies when implementing cloud services is to reduce costs, they are willing to invest in a service that can manage costs in the cloud.

Every day, investments in SaaS services are increasing. This growth is driven by the standardization of remote work environments and the emergence of SaaS tools for hybrid and multi-cloud environments.

Worker training

For the first time, the lack of knowledge of the company's staff has become the main obstacle to the implementation of a SaaS solution in the cloud. A large number of companies claim to train their employees at a basic and advanced level.

Cloud SaaS providers should take advantage of this context and offer their customers a higher level of training in the use of their tools so that the company can consolidate and use all the solutions offered by cloud software. This is an opportunity to stand out from the competition and provide special value to the SaaS product. Software demos can be a good option when it comes to communicating this advantage as a vendor.

The data path begins to consolidate

With the recent consolidation of new technologies such as 5G, a new approach to analytics and artificial intelligence is becoming more accessible to companies. Because of this, the percentage of companies that intend to implement artificial intelligence, machine learning, and deep learning solutions between 2023 and 2024 has grown significantly.

More than half of companies currently store less than 25% of their data in the cloud. Although the data move is starting to gain momentum, companies are expressing a desire to move their data to the cloud. Cloud service providers will see their customers' budgets increase and their industry expands, but they will also face an increasingly regulated context and will need to do what they can to ease this transition.

Multicloud

Multicloud is currently the preferred option for customers. Companies prefer a platform that integrates multiple clouds, both public and private, and traditional on-premise infrastructures to get the most value for services and pricing. Therefore, companies want to move to a multi-cloud environment to maximize the potential of software and reduce dependence on a single vendor.

Cyber security

Globally, cybersecurity continues to be one of the biggest concerns of customers.

Concern about cybersecurity when implementing cloud solutions is in line with the global trend and has become one of the main concerns of companies. Software vendors should be aware of this SaaS market trend and make cybersecurity a top priority for customers.

Therefore, when using such technologies, pay attention to the main trends and analyze the cloud technology you choose for SaaS.

Hi pals! Welcome to the next deep learning tutorial, where we are at the exciting stage of TensorFlow. In the last tutorial, we just installed the TensorFlow library with the help of Anaconda, and we saw all the procedures step by step. We saw all the prerequisites and understood how you can follow the best procedure to download and install TensorFlow successfully without any trouble. If you have done all the steps, then you might be interested in knowing the basics of TensorFlow. No matter if you are a beginner or have knowledge about TensorFlow, this lecture will be equally beneficial for all of you because there is some important and interesting information that not all people know. So, have a look at the topics that will be discussed with you in just a bit.

• What is a tensor?

• What are some important types of tensors that we use all the time while using TensorFlow?

• How can we start programming in TensorFlow?

• What are the different operations on the TensorFlow?

• How can you print the multi-dimensional array int he TensorFlow?

Moreover, you will see some important notes related to the practice that we are going to perform so, you can say, no point will be missing and we will recall our previous concepts all the time when we need them so that the beginners may also get the clear concepts that what is going on in the course.

What is a Tensor?

There are different meanings of tensors in different fields of study, but we are ignoring others and focusing on the field with which we are most concerned: the mathematical definition of the tensor. This term is used most of the time when dealing with the data structure. We believe you have a background in programming languages and know the basics, such as what a data structure is, so I will just discuss the basic definition of tensors.

"The term "tensor" is the generalization of the metrics of nth dimensions and is the mathematical representation of a scaler, vector, dyad, triad, and other dimensions."

Keep in mind, in tensor, all values are identical in the data type with a known shape. Moreover, this shape can also be unknown. There are different ways to introduce the new tensor in TensorFlow while you are programming in it. If it is not clear at the moment, leave that because you will see the practical implementation in the next section. By the same token, you will see more of the additional concepts in just a bit, but before this, let me remind you of something:

Types of TensorFlow

Before going deep into the practical work, I want to clarify some important points in the learning of TensorFlow. Moreover, in this tutorial, we will try to divide the lecture into the practical implementation and the theoretical part of the tutorial. We will see some terms often in this tutorial, and I have not discussed them before. So, have a look at the following descriptions.

The Shape of Tensor

The length of the tensor is called its shape, and we can understand the term "shape" in a way that it is defined with the help of the total number of rows and columns. While declaring the tensor, we have to provide its shape.

The Rank of Tensors

The rank defines the dimensions of the tensor. So, in other words, rank defines the order of the dimensions that starts at 1 and ends on the nth dimension.

Type of Tensor

When talking about the tensor, the “type” means the data type of that particular tensor. Just as we consider the data type in other programming languages, when talking about the language of TensorFlow, the type provides the same information about the tensor.

Moreover, in order to learn more about the type of the tensors, we examine them with respect to different operations. In this way, we found the following types of tensors:

1. tf.Variable

2. tf.constant

3. tf.placeholder

4. tf.SparseTensor

Basics of TensorFlow Programming Language

Before we get into the practical application of the information we discussed above, we'll go over the fundamentals of programming with TensorFlow. These are not the only concepts, but how you apply them in TensorFlow may be unfamiliar to you. So, have a look at the information given below:

Comment in the TensorFlow

When you want your compiler to ignore some lines that you have written as notes, you use the “sign of hash” before those lines. In other words, the compiler ignores every line that you start with this sign. In other programming languages, we use different types of signs for the same purpose, such as, // is used in C++ to start the comment line when we are using compilers such as Visual Studio and Dev C++.

Printing the Message in TensorFlow

When we want to print the results or the input to show on the screen, we use the following command:

print(message)

Where the message may be the input, output, or any other value that we want to print. Yet, we have to follow the proper pattern for this. 

Applying Operations in TensorFlow

To apply the operations that we have discussed above, we have to first launch TensorFlow. We covered this in our previous session. Yet, every time, you have to follow some specific steps. Have a look at the details of these steps:

• Fire up your Anaconda navigator from the search bar. 

• Go to the Home tab, where you can find the “Jupyter notebook."

• Click on the launch button. 

• Select “Python” from the drop-down menu on the right side of the screen. 

• A new localhost will appear on your Google browser, where you have to write the following command:

import tensorflow as tf

from tensorflow import keras

Make sure you are using the same pattern and take care of the case of the alphabets in each word.

Here, you can see, we have provided that information about the type of tensor we want, and as an output, it has provided us with the information about the output. There is no need to provide you with the meaning of the int16 here as we all know that there are different types of integers and we have used the one that occupies the int16 space in the memory. You can change the data type for the practice. It is obvious that you are just feeding the input value, and the compiler is showing the output of the same kind as you were expecting. Here, the shape was empty because we have not input its value. So, we have understood that there is no need to provide the shape all the time. But, in the next programs, we will surely use the shape to tell you the importance of this type. 

Printing Multi-Dimensional Arrays in TensorFlow

Before the practical implementation, you have seen the information about the dimensions of the Tensors. Now, we are moving forward with these types, and here is the practical way to produce these tensors in TensorFlow.

Printing the Two Dimensions Tensor

Here, you can print the two-dimensional array with the help of some additional commands. I'll go over everything in detail one by one. In the case discussed before, we have provided information about the tensor without any shape value. Now, have a look at the case given next;

Copy the following code and insert it into your TensorFlow compiler:

a=tf.constant([[3,5,7],[3,9,1]])

print(a)

Here comes the interesting point. In this case, we are just declaring the array with two dimensions, and TensorFlow will provide you with the shape (information of the dimension) and the memory this tensor name “a" is occupying by itself. As a result, you now know that this array has two rows and three columns.

By the same token, you can use any number of dimensions, and the information will be provided to you without any issues. Let us add the other dimensions.

Let’s have another example to initialize the matrix in TensorFlow, and you will see the shortcut form of the declaration of a unit matrix of your own choice. We know that a unit matrix has all the elements equal to one. So, you can have it by writing the following code:

a=tf.ones((3,3)

print(a)

The result should look like this:

Other examples of matrices that can be generated in such a way are the identity matrix and zero matrices. The identity matrix and the zero matrices are two other matrices that can be generated in this manner. For a zero and identity matrix, we will use “zero” and “eye” respectively in place of “ones” in the code given above. 

The next step is to practice creating a matrix containing random numbers between the ranges that are specified by us. For this, we are using the random operation, and the code for this is given in the next line:

a=tf.random.uniform((1,5),minval=1, maxval=3)

print(a)

When we observe these lines, we will understand that first of all, we are using the random number where the numbers of

 Rows and columns are given by us. I have given the single-row and five-column matrix in which I am providing the compiler with the maximum and minimum values. Now, the compiler will generate random values between these numbers and display the matrix in the order you specify.

So, from the programs above, we have learned some important points:

• Just like in the formation of matrices in MATLAB, you have to put the values of rows in square brackets. 

• Each element is separated from the others with the help of a comma.

• Each row is separated by applying the comma between the rows and enclosing the rows in the brackets.

• All the rows are enclosed in the additional square bracket.

• You have to use the commas properly, or even if you have a single additional space, you can get an error from the compiler while running it.

• It is convenient to give the name of the matrix you are declaring so that you can feed the name into the print operation.

• If you do not name the matrix, you can also use the whole matrix in the print operation, but it is confusing and can cause errors.

• For the special kinds of matrices that we have learned in our early concepts of matrices, we do not have to use the square brackets, but instead, we will use the parentheses along with the number of elements so that compiler may have the information about the numbers of rows and columns, and by reading the name of the specific type of the matrix, it will automatically generate the special matrices such as the unit matrix, the null matrix, etc.

• The special kind of matrices can also be performed in the TensorFlow but you have to follow the syntax and have to get the clear concept for the performance.

So, in this tutorial, we have started using the TensorFlow that we had installed in the previous lecture. Some of the steps to launch TensorFlow are the same and you will practice them every day in this course. We have seen how can we apply the basic functions in the TensorFlow related to the matrices. We have seen the types of tensors that are, more or less, similar to the matrices. You will see the advanced information about the same concepts in the tutorials given next as we are moving from the basics to the advance so stay with us for more tutorials.

Hello Peeps! Welcome to the next lecture on deep learning, where we are discussing TensorFlow in detail. You have seen why we have chosen TensorFlow for this course, and we have read a lot about the working mechanism, programming languages, and advantages of using TensorFlow instead of other libraries. Instead of using the other options for the same purpose, we have seen several reasons to use TensorFlow. Because of the latest work on the library for more improvement and better results, it's now time to learn the specifics of TensorFlow installation. But before this, you have to check the list of the concepts that will be cleared today:

Is Installation of TensorFlow Difficult?

The simple and to-the-point answer to this question is, the installation is easy and usually does not require any practice. If you are new to the technical world or have not experienced the installation of any software, do not worry because we will not skip any steps. Moreover, we have chosen the perfect way to install TensorFlow by telling you the all necessary information about the installation process so you start only if all the parameters are completed. So, first of all, let us share the prerequisites with you. 

• What are the minimum requirements for Tensorflow to be installed on your PC?

• How can we choose the best method for the installation of TensorFlow?

• How can we install TensorFlow with Jupyter?

• What is the process for the Keras to be installed?

• How can you launch the TensorFlow and Keras with the help of Jupyter Notebook?

• What is the significance to use Jupyter, Keras, and TensorFlow?

Pre-requisites for TensorFlow

To install TensorFlow without difficulty, you must keep all types of requirements in mind. We have categorised each type of requirement and you just have to check whether you are ready to download it or not.

 By the same token, there are some hardware requirements and below these values, the hardware does not support the TensorFlow.

Here, it is important to notice that the requirements given in all the tables are the minimum requirement s and you can go for the higher versions of all of these for the better results and quality work.

Moreover, to enhance the latency and performance, TesnorFlowRT is recommended. All the requirements given above are authentic and you must never skip any of these if you want to get 100 efficiencies. Moreover, for the course we are working on, there is no need for any GPU as we are moving towards the basic course and we can install GPU in future if required.

Choosing the Best Installation Method for Anaconda

Now it's time to make a decision about the type of installation you want. This is the step that makes TensorFlow different from the other simple installations. If you're going to install it on your PC, you have to get help from another software. There is certain software through which you can install the library software with the help of Anaconda software. For this, we are going to the official website and installing the anaconda software.

• As soon as you will click on the download option, the loading will start and the software of Anaconda with the size 600+MBs will start downloading. It will take a few moments to be downloaded. 

• Once the process discussed above is completed, you have to click on the installation button and the window will be pop-up on your screen where the installation steps have to be completed by you.  

The installation process is so simple that many of you have known them before but the purpose to tell you about each and every step is, some people do not know much about the installation or have the habit to match the steps with the tutorials so they may know that they are at the right path.

• In the next step, you have to provide the path for the installation place of the anaconda. By default, as you expect, the C drive is set but I am going to change the directory. You can choose the path according to your choice. 

• Now, you will see that it is asking for the settings according to your choice. By default, the second option is ticked and I am installing the Anaconda as it is and click on the installation process. 

• Now, the installation process is starting and it will take some time.

• While this step is taking a little time, you can read about the documentation of the TensorFlow. 

• Once the installation is complete, the next button will direct you towards this window:

In this way, Anaconda will be installed on your PC. You must know that it has multiple libraries, functions, and software within it and there is no need to check them all. For our practice, we just have to know about the Jupyter notebook. It will be cleared in just a bit How can we start and work with this notebook? 

Installing the TensorFlow Framework

It seems that you have successfully installed Anaconda and are now moving towards the installation of your required library. It is a simple and interesting process that requires no technical skills. You just have to follow the simple steps given next:

• Go to the start menu of your window. 

• Search for the “Anaconda command prompt."

• Click on it, and a command prompt window will appear on your screen. 

• You just have to write the following command and the Anaconda will automatically install this amazing library for you. 

• As you can see, it mentions that the download of TensorFlow requires 266.3 MBs. Once this command is entered, the installation of the TensorFlow will carry out and you have to wait for some moments.

To confirm the installation process, I am providing you with some important commands. You just have to type “python” in the command prompt, and Anaconda will confirm the presence of the python information on your PC. 

• In the next step, to ensure that you have installed Tensorflow successfully, you can write the following command:

Import TensorFlow as tf

• If nothing happens in the command prompt, it means your library was successfully installed; otherwise, it throws an error.
  

Hence, the TensorFlow library is successfully installed on our PCs. The same purpose is also completed with the help of Jupyter Navigator, and you will see it in detail.

Installing the TensorFlow Environment with Jupyter Navigator

Finally, for the perfect installation, we will search, follow the path Home>Search>Anaconda Navigator, and press enter. The following screen will appear here.

You have to choose the “Environment” button and click on the “Create” button to create a new environment. A small window will appear, and you have to name your environment. I am going to name it "TensorFlow.”

There is a possibility that it recommends the updated version if it is available. We recommend you have the latest version, but it is not necessary. As soon as you click on the "Create" button, in the lower right corner, you will see that your project is being loaded. 

This step takes some time; in the meantime, you can check the other packages in the Anaconda software. 

Installing the Keras with Jupyter

There is a need for Keras API as you have seen in our previous lectures. But as a reminder, we must tell you that Keras is a high-level application programming interface that is specially designed for deep learning and machine learning by Google, and with the help of this PAI, TensorFlow provides you with perfect performance and efficient work. So, here are the steps to install Keras on your PC.

• Open the Jupyter navigator.

• Click on the "create" button. 

• Write the name of your new environment, I am giving it the name "Keras,” as you were expecting. 

• The next step is to load the environment, as you have seen in the case of TensorFlow as well. 

These steps are identical to the creation of an environment for TensorFlow. It is not necessary to discuss why we are doing this and what the alternatives are. For now, you have to understand the straightforward procedure and follow it for practice. 

 Keep in mind that, till now, you have just installed the library and API, but for the working of both of these, you have to run them, and we will earn this in just a bit. 

Checking for the Installation of TensorFlow

The installation process does not end here. After the installation process, you have to check if everything is working properly or not. For this, go to the home page and then search for “Jupyter notebook." You must notice that there is a launch button at the bottom of this notebook’s section. If you found something else here, such as "Install,” then you have to first install the notebook by simply clicking on it, and then launch the notebook.

As soon as you launch the Jupyter notebook, you will be directed to your browser, where the notebook is launched on the local host of your computer. Here, it's time to write the commands to check the presence and working of the TensorFlow. You have to go to the upper right side of the screen and choose the Python3 (ipykernel) mode. 

Now, as you can see, you are directed towards the screen where a code may be run. So you have to write the following command here:

Import tensorflow as tf

From tensorflow import keras

This may look the same as the previous way to install tensorflow, but it is a little bit different because now, at Jupyter, you can easily run your code and work more and more on it. It is more user-friendly and has the perfect working environment for the students and learners because of the efficient results all the time.

Keras is imported along with TensorFlow, and it is so easy to deal with deep learning with the help of this library and API. 

If you do not remember these steps, do not worry because you will practice them again and again in this course, and after that, you will become an expert in TensorFlow. Another thing to mention is that you can easily launch Keras and TensorFlow together; you do not have to do them one after the other. But sometimes, it shows an error because of the difference in the Python version or other related issues. So it is a good practice to install them one after the other because, for both, the procedure is identical and is not long. 

So, it was an informative and interesting lecture today. We have utilized the information from the previous lectures and tried to install and understand TensorFlow in detail. Not only this, but we also discussed the installation process of Keras, which has a helpful API, and understood the importance of using them together. Once you have started TensorFlow, you are now ready to use and work with it within Jupyter. Obviously, there are also other ways to do the same work as we have done here, but all of them are a little bit complex, and I found these procedures to be the best. If you have better options, let us know in the comment section or contact us directly through the website. 

In the next session, we will work on TensorFlow and learn the basics of this amazing library. We will start from the basics and understand the workings and other procedures of this library. Till then, stay with us.

Hey learners! Welcome to the new tutorial on deep learning, where we are going deep into the learning of the best platform for deep learning, which is TensorFlow. Let me give you a reminder that we have studied the need for libraries of deep learning. There are several that work well when we want to work with amazing deep-learning procedures. In today’s lecture, you are going to know the exact reasons why we chose TensorFlow for our tutorial. Yet, first of all, it is better to present the list of topics that you will learn today:

• Why do we use TensorFlow with deep learning?

• What are some helpful features of this library?

• How can you understand the mechanism of TensorFlow?

• Show the light towards the architecture, and components of the TensorFlow.

• In how many phases you can complete the work in the TensorFlow and what are the details of each step?

• How the data is represented in the TensorFlow?

Why TensorFlow for Deep Learning

In this era of technology, where artificial intelligence has taken charge of many industries, there is a high demand for platforms that, with the help of their fantastic features, can make deep learning easier and more effective. We have seen many libraries for deep learning and tested them personally. As a result of our research, we found TensorFlow the best among them according to the requirements of this course. 

There are many reasons behind this choice that we have already discussed in our previous sessions but here, for a reminder, here is a small summary of the features of TensorFlow

• Flexibility

• Easy to train

• Ability to train the parallel neural network training

• Modular nature

• Best match with the Python programming language

As we have chosen Python for the training process, therefore, we are comfortable with the TensorFlow. It also works with traditional machine learning and has the specialty of solving complex numerical computations easily without the requirement of minor details. TensorFlow proved itself one of the best ways to learn deep learning, therefore, google open-sourced it for all types of users, especially for students and learners. 

Helpful Features of TensorFlow

The features that we have discussed before we very generalized and you must know more about the specific features that are important to know before getting started with TensorFlow.

APIs of TensorFlow

Before you start to take an interest in any software or library, you must have knowledge about the specific programming languages in which you operate that library. Not all programmers are experts in all coding languages; therefore, they go with the specific libraries of their matching APIs. TensorFlow can be operated in two programming languages via APIs:

1. C++ 

2. Python

3. Java (Integration)

4. R (Integration)

The reason we love TensorFlow is that the coding mechanism for deep learning is much more complicated. It is a difficult job to learn and then work with these coding mechanisms.

TensorFlow provides the APIs in comparatively simple and easy-to-understand programming languages. So, with the help of C++ or Python, you can do the following jobs in TensorFlow:

• To configure the neuron

• Work with the neuron

• Prepare the neural network

TensorFlow Supports Different Computing Devices

As we have said multiple times, deep learning is a complex field with applications in several forms. The training process of the neural network with deep learning is not a piece of the cake. The training process of neural networks requires a lot of patience. The computations, multiplication of the matrices, complex calculations of mathematical functions, and much more require a lot of time to be consumed, even after experience and perfect preparations. At this point, you must know clearly about two types of processing units:

1. Central processing unit

2. Graphical processing unit

The central processing units are the normal computer units that we use in our daily lives. We've all heard of them. There are several types of CPUs, but we'll start with the most basic to highlight the differences between the other types of processing units. The GPUs, on the other hand, are better than the CPUs. Here is a comparison between these two:

The good thing about TensorFlow is, it can work with both of them, and the main purpose behind mentioning the difference between CPU and GPU was to tell you about the perfect match for the type of neural network you are using. TensorFlow can work with both of these to make deep learning algorithms. The feature of working with the GPU makes it better for compilation than other libraries such as Torch and Keras. 

Working Mechanism of TensorFlow

It is interesting to note that Python has made the workings of TensorFlow easier and more efficient. This easy-to-learn programming language has made high-level abstraction easier. It makes the working relationship between the nodes and tensors more efficient. 

The versatility of TensorFlow makes the work easy and effective. TensorFlow modules can be used in a variety of applications, including

• Android apps

• iOS

• Cluster

• Local machines

Hence, you can run the modules on different types of devices, and there is no need to design or develop the same application for different devices.

The history of deep learning is not unknown to us. We have seen the relationship between artificial intelligence and machine learning. Usually, the libraries are limited to specific fields, and for all of them, you have to install and learn different types of software. But TensorFlow makes your work easy, and in this way, you can run conventional neural networks and the fields of AI, ML, and deep learning on the same library if you want. 

The Architecture of TensorFlow

The architecture of the TensorFlow depends upon the working of the library. You can divide the whole architecture into the three main parts given next:

• Data Processing

• Model Building

• Training of the data

The data processing involves structuring the data in a uniform manner to perform different operations on it. In this way, it becomes easy to group the data under one limiting value. The data is then fed into different levels of models to make the work clear and clean.

In the third part, you will see that the models created are now ready to be trained, and this training process is done in different phases depending on the complexity of the project. 

Phases of the TensorFlow Projects 

While you are running your project on TensorFlow, you will be required to pass it through different phases. The details of each phase will be discussed in the coming lectures, but for now, you must have an overview of each phase to understand the information shared with you.

Development Phase: 

The development phase is done on the PC or other types of a computer when the models are trained in different ways. The neural networks vary in the number of layers, and in return, the development phase also depends upon the complexity of the model.

Run Phase

The run phase is also sometimes referred to as the inference phase. In this phase, you will test the training results or the models by running them on different machines. There are multiple options for a user to run the model for this purpose. One of them is the desktop, which may contain any operating system, whether it is Windows, macOS, or Linux. No matter which of the options you choose, it does not affect the running procedure.

Moreover, the ability of TensorFlow to be run on the CPU and GPU helps you test your model according to your resources. People usually prefer GPU because it produces better results in less time; however, if you don't have a GPU, you can do the same task with a CPU, which is obviously slower; however, people who are just getting started with deep learning training prefer CPU because it avoids complexities and is less expensive. 

Components in TensorFlow

Finally, we are at the part where we can learn a lot about the TensorFlow components. In this part, you are going to learn some very basic but important definitions of the components that work magically in the TensorFlow library. 

Tensor

Have you ever considered the significance of this library's name? If not, then think again, because the process of performance is hidden in the name of this library. The tensor is defined as:

"A tensor in TensorFlow is the vector or the n-dimensional data matrix that is used to transfer data from one place to another during TensorFlow procedures."

The tensor may be formed as a result of the computation during these procedures. You must also know that these tensors contain identical datatypes, and the number of dimensions in these matrices is known as the shape.

Graph

During the process of training, the operations taking place in the network are called graphs. These operations are connected with each other, and individually, you can call them "ops nodes." The point to notice here is that the graphs do not show the value of the data fed into them; they just show the connections between the nodes. There are certain reasons why I found the graphs useful. Some of them are written next:

• These can be run or tested on any type of device or operating system. You have the versatility to run them on the GPU, OS, or mobile devices according to your resources.

• The graphs can be saved for future use if you do not want to use them at the current time or want to reuse them in the future for other projects or for the same project at any other time, just like a simple file or folder. This portable nature allows different people sharing the same project to use the same graph without having any issues. 

Dataflow Graphs in TensorFlow

TensorFlow works differently than other programming languages because the flow of data is in the form of nodes. In traditional programming languages, code is executed in the form of a sequence, but we have observed that in TensorFlow, the data is executed in the form of different sessions. When the graph is created, no code is executed; it is just saved in its place. The only way to execute the data is to create the session. You will see this in action in our coming lectures. 

• Each node in the TensorFlow graph, the mathematical operation such as addition, subtraction, multiplication, etc, is represented as the node. By the same token, the multidimensional arrays (or tensors) are shown by the nodes. 

• In the memory of TensorFlow, the graph of programming languages is known as a "computational graph." 

• With the help of CPUs and GPUs, large-scale neural networks are easy to create and use in TensorFlow.

By default, a graph is made when you start the Tensorflow object. When you move forward, you can create your own graphs that work according to your requirements. These external data sets are fed into the graph in the form of placeholders, variables, and constants. Once these graphs are made and you want to run your project, the CPUs and GPUs of TensorFlow make it easy to run and execute efficiently. 

Hence, the discussion of TensorFlow is ended here. We have read a lot about TensorFlow today and we hope it is enough for you to understand the importance of TensorFlow and to know why we have chosen TensorFlow among the several options. In the beginning, we have read what is TensorFlow and what are some helpful features of TensorFlow. In addition to this, we have seen some important APIs and programming languages of this library. Moreover, the working mechanism and the architecture of TensorFlow were discussed here in addition to the phases and components. We hope you found this article useful, stay with us for more tutorials.

Hey buddies! Welcome to the next tutorial on deep learning, in which you are about to acquire knowledge related to Python. This is going to be very interesting because the connection between these two is easy and useful. In the last lecture, we had an eye on the latest and trendiest deep learning algorithms, and therefore, I think you are ready to take the next step towards the implementation of the information that I shared with you. To help you make up your mind about the topics of today, I have made a list for you that will surely be useful for you to understand what we are going to do today. 

• How do you introduce the Python programming language to a deep learning developer?

• How is Python useful for deep learning training in different ways?

• Do Python provide the useful frameworks for the depe learning?

• What are some libraries of Python that are useful for the deep learning?

• Why do programmers prefer Python over other options when working with deep learning?

• What are some other options besides Python to be used with deep learning?

Introduction to the Python Coding Language

Over the years, the hot topic in the world of programming languages has been Python because of many reasons that you will learn soon. It is critical to understand that when selecting a coding language, you must always be confident in its efficiency and functionality. Python is the most popular because of its fantastic performance, and therefore, I have chosen it for this course. From 2017 to the present, calculations and estimations of popularity show that Python is in the top ten in the interests of both common users and professionals due to its ease of installation and unrivaled efficiency.

Now, recall that deep learning is a popular topic in the industry of science and technology, and people are working hard to achieve their goals with the help of deep learning because of its jaw-dropping results. When talking about complexity, you will find that deep learning is a difficult yet useful field, and therefore, to minimize the complexity, experts recommend python as the programming language. All the points discussed below are an extraction of my personal experience, and I chose the best points that every developer must know. The following is a list of the points that will be discussed next:

Readable Code

I am discussing this point at the start of the discussion because I think it is one of the most important points that make programming better and more effective. If the code is clean and easy to read, you will definitely be able to pay attention to the programming in a better way. Usually, the programming is done in the grouping phase, and for the testing and other phases of successful programming, it is important to understand the code written by the others. Hence, coding in Python is easy to read and understand, and by the same token, you will be able to share and practice more and more with this interesting coding language.

The syntax and rules of the Python programming language allow you to present your code without mentioning many details. People realize that it is very close to the human language, and therefore, there is no need to have a lot of practice or knowledge to start practising. These are the important points that prove the importance of the Python language for writing more useful code. As a result, you can conclude that for complex and time taking processes such as deep learning, Python is one of the ideal languages because you do not have to spend a lot of time coding but will be able to use this energy to understand the concepts of deep learning and its applications. 

Multiple Programming Paradigms

Python, like other modern programming languages, supports a variety of programming paradigms. It fully supports:

• Object-oriented Programming

• Structured programming

Furthermore, its language features support a wide range of concepts in functional and aspect-oriented programming. Another point that is important to notice is that Python also includes a dynamic type system and automatic memory management.

Python's programming paradigms and language features enable you to create large and complex software applications. Therefore, it is a great language to use with deep learning. 

Flexibility with Other Platforms

If you are a programmer, you will have the idea that for different programming languages, you have to download and install other platforms for proper working. It becomes hectic to learn, buy, and use other platforms for the working of a single language. But when talking about Python, the flexibility can be proven by looking at the following points:

• It supports multiple operating systems.

• It is an interpreted programming language. That means you can run the Python code on several platforms without the headache of recompilation for the other platforms. 

• The testing of the Python code is easier than in some other programming languages.

All these points are enough to understand the best combination of deep learning with the Python programming language because deep learning requires the training and testing process, and there may be a need to test the same code or the network on different platforms. 

Robust Libraries of Python

Want to know why Python is better than other programming languages? One of the major reasons is the fantastic and gigantic library of the Python language. It is a programming tip that programmers should always check the programming language's library if they want to know its efficiency and ability to work instantly. One thing to notice is that you will get a large number of modules, and it allows you to choose the modules of your choice. So, you can ignore the unnecessary modules. This feature is also present in other popular programming languages. Moreover, you can also add more code according to your needs. For the experts, it is a blessing because they can use their creativity by using the already-saved modules.

Deep Elarnigna only contains algorithms, and it requires a programming language that allows for simple and quick module creation. Python is therefore ideal for deep embedding in context. 

Opensource Frameworks

In the past lectures, we have seen the frameworks of deep learning, and therefore, for the best compatibility, the programming language in which the deep learning is being processed must also have open-source frameworks; otherwise, this advantage of deep learning will not be useful. Most of the time, the tools and frameworks are not only open source but also easily accessible, which makes your work easier. I believe that having more coding options makes your work easier because coding is a time-consuming process that requires you to have as much ease as possible for better practice. Here is the list of some frameworks that are used with the Python programming language:

• Django

• Flask

• Pyramid

• Bottle

• Cherrypy.

Another reason why experts recommend Python for deep learning is the Python frameworks related to graphical user interfaces. In the previous lectures, you have seen that deep learning has a major application in image and video processing, and therefore, it is a good match for deep learning with Python coding. The GUI frameworks of Python include:

• PyQT

• PyJs

• PyGUI

• Kivy

• PyGTK

• WxPython

Observe that the keyword "Py" with all these frameworks indicates the specification of the Python programming language with these frameworks. At this point, it is not important to understand all of them. But as an example, I want to tell you that Kivy is used for the front end of Android apps with the help of Python. 

This category makes it important to notice the connection between the Python programming language and deep learning because, when working with deep learning, a greater variety of frameworks results in an easier working and better training process. 

Test Driven Approach

If you are following our previous tutorials, you will be aware of the importance of testing in deep learning. But allow me to tell you the connection between Python and the test-driven approach. In deep learning, all efficiency depends upon the testing process. More and more training and testing means better performance, which the network can recognize better. Python provides for the rapid creation of prototype applications, and similarly, it also provides the best test driven approach when connected to networks.

Consistency

The first rule to learning programming languages is to have consistency in your nature. Yet, for the more difficult programming languages, where the absence of a single semicolon can be confusing for the compiler, consistency is difficult to attain. On the contrary, an easier and more readable programming language, such as Python, helps to pay more attention to the code, and thus the user is more drawn to its work. Deep learning can only be performed in such an environment. So, for peace of mind, always choose Python. 

Massive Community Support

Have you ever been stuck in a problem while coding and could not find the help you needed? I've seen this many times, and it's a miserable situation because the code contains your hard work from hours or even days, but you still have to leave it. Yet, because of the popularity and saturation of this field, Python developers are not alone. Python is a comparatively easy language, and normally people do not face any major issues. Yet, for the help of the developers, there is a large community related to Python where you can find the solution of your problems, check the trends, have a chit chat with other developers, etc.

When working on deep learning projects, it's fun to be a part of a community with other people who are working on similar projects. It is the perfect way to learn from the seniors and grow in a  productive environment. Moreover, while you are solving the problems of the juniors, you will cultivate creativity in your mind, and deep learning will become interesting for you. 

Other Programming Options for The Deep Learning

At this point, where I am discussing a lot about Python, it must be clarified that it is not the only option for deep learning. Deep learning subjects are always wasteful, and users always have more than one option. However, we prefer Python for a variety of reasons, and now I'd like to tell you about some other options that appear useful but are, in fact, less useful than Python. The other programming languages are:

• JavaScript

• Swift

• Ruby

• R.

• C

•  C++

• Julia

• PHP

No doubt, people are showing amazing results when they combine one or more of these programming languages with deep learning, but usually, I prefer to work more with Python. It totally depends on the type of project you have or other parameters such as the algorithm, frameworks, hardware the user has, etc. to effectively choose the best programming language for deep learning. An expert always has an eye on all the parameters and then chooses the perfect way to solve the deep learning problems, no matter what the difficulty level of the language is.

Hence, we have discussed a lot about the Python today. Before all this discussion, our focus was on the deep learning and its working so you amy have the idea what actually si going on. In this article, we have seen the compatibility of the Python programming language with deep learning. We knew about the parameters of the deep learning and therefore were able to understand the reason of choosing the Python for our work. Throughout this article, we have seen different reasons why we have chosen TensorFlow and related libraries for our work. It is important to notice that Python works best with the TensorFlow and Keras APIs, and therefore, from day one, we have focused on both of these. In the next lecture, you will see some more important information about deep learning, and we are moving towards the practical implementation of this information. Once we have performed the experiment, all the points will be crystal clear in your mind. So until then, learn with us and grow your knowledge.