The Engineering Projects

No matter your company's industry, you need fleet maintenance software. It’s something you need if your engineering firm has a fleet of vehicles. Failing to get the necessary tools is a bit like driving blind -- not the best course of action by any stretch of the imagination.

If you’re not sold on the benefits of investing in fleet maintenance software, keep reading to learn more about four reasons you need it.

1. Preventative Maintenance

Fleet maintenance software will help your engineering firm with preventative maintenance. The software will make it easy to schedule maintenance so that your fleet of cars stays in good shape. 

One of the worst things you can do is delay or ignore preventative maintenance. That will ultimately cost you in terms of downtime stemming from repairs. Preventative maintenance won’t eliminate the possibility of repairs, but it will reduce the odds of breakdowns . And breakdowns can become expensive if you also find yourself unable to use the company vehicles for business. Unplanned downtime is a no-no.

2. Lower Repair Costs

When your business invests in fleet maintenance software, you’ll also enjoy lower vehicle repair costs. Putting off maintenance means that small problems can morph into major issues that cost an arm and a leg to fix. So, it’s more cost-effective to invest in preventive maintenance than to postpone doing so and having to spend many times more to fund major repairs. 

When considering the importance of your fleet, you can appreciate the need to ensure the vehicles are available and in good repair whenever they’re needed. You can increase uptime and reduce downtime by tackling issues as soon as they’re discovered so that they don’t worsen.

3. Better Fuel Efficiency

When you get one of the best fleet maintenance software applications, you’ll also benefit from better fuel efficiency. Proper maintenance and repairs will ensure your fleet of vehicles operates more efficiently, resulting in better fuel efficiency. You can also optimize routes so that drivers don’t take longer routes than necessary. And by keeping track of driver behavior, like speeding or hard braking, you can notify drivers of necessary changes. You can save a lot on fuel just by getting drivers to operate the fleet vehicles more responsibly.

4. Fewer Headaches

Who has time to worry about their fleet of vehicles? You have other things to worry about such as operating your engineering firm. When you’re taking good care of your cars, you won’t be left worrying about them. Instead of having to make frequent arrangements to have your inoperational cars towed to automotive facilities for repairs, you’ll be able to use them for business purposes. They’ll spend more time on the road and less time out of commission.

That means you’ll have fewer headaches with your fleet of vehicles. Using a fleet maintenance software solution, you’ll be able to stay on top of things. And if things don’t get out of control, you’re less likely to have headaches over something going horribly wrong.

If your engineering firm has a fleet of vehicles, it’s essential to consider the benefits of fleet maintenance software. Protecting your assets is a must if you want them to remain in good repair over the long haul. Getting a fleet of vehicles requires a tangible investment. If you want to protect your investment, then investing in fleet maintenance software is a good idea.

Before settling on a platform, you’ll want to do research to find something that meets your company’s needs. When you find the right software, you’ll see up close and personal what the benefits of fleet maintenance software are all about.

Hey peeps! Welcome to another tutorial on data types in Python. Our purpose in Python education is to get a grip on the basic concepts so that we may work on deep learning easily. In the previous lecture, we read a lot about lists as we are working on the subtypes of the sequence data type. In the present lecture, you are going to understand more types of sequences. If you know the list well, this lecture will be a piece of cake for you. We will start the introductions in just a bit, but before that, there must be a quick review of the topics that you are going to understand:

• How do you introduce the tuples in Python?

• What are some important characteristics of a tuple that must be kept in mind when we are dealing with it?

• How can you practically perform the tuples in TensorFlow?

• How do you associate tuples with the list?

• Can you delete, update, add, or delete the elements in a tuple? If yes, then how?

• Which is the way to delete the tuple entirely?

All of these are important interview questions about the tuple and if you are dealing with these data types, you must know and perform these by yourself in TensorFlow. All the information about tuples will be discussed with you and you have to practice more and more in the code to understand the concepts.

Tuple in Sequence 

For the concept of a list, we have given you the reference of an array. This time, we will be mentioning the name of the list for better understanding. If we talk about the topic of today, a tuple is also a group of items that are arranged when required in Python. The working and the definition of the tuple seem like a list because both of them are types of a single data type, which is the sequence. But there are some differences that make them ideal for different kinds of situations. The following points will make this more clear:

• The tuple is represented by enclosing the data in parentheses. 

• A tuple is immutable, which means that once you declare the items in the tuple, you can not change them as you can in a list. 

• When you try to change the value of the element in the tuple, an error is shown on the screen, so while declaring the new tuple, you have to be very clear about what is on your mind and what you want to do with the tuple. 

• In a tuple, you can also use the function in the tuple and it becomes easy to perform different tasks through codes.

• A single element in the tuple is necessary to build the tuple. In other words, unlike the strings, the tuple must contain at least one element in it.

• In the tuple, any data type can be saved whether it is boolean, int, or string. Moreover, there is a possibility to add different types of data in a single tuple. So we can conclude that we can have a homogeneous or heterogeneous tuple according to our choice. 

Order of The Tuple

As we have mentioned, when using the tuple, we get the ordered list of objects, which means we get the specific order, and this order, once mentioned, can not be changed, unlike other data types of the sequence. 

Unchangeable Tuple

Not just the order, but the size, sequence, and entries are unchangeable, so during the initialization and declaration of the tuple, the concept must be clear about what you want from the particular tuple. 

Duplication in Tuple

Another thing that must be kept in mind is, the tuple has the index form, and therefore, every element has a specific number of indexes. This is the reason, the elements are easily accessible, and hence, you can also have duplication in the tuple. The elements are recognized by the index number, and therefore, the compiler knows when which element is being called.  

Length of the Tuple

The length is the number of elements in the tuple, and we have read about the length function in the previous lecture. Similar to the list, a tuple can also be used in the length function, and the programmer gets the length of the tuple. Right now, this function is not looking very attractive because we are dealing with small tuples. But take the case in your mind when the tuple contains hundreds or thousands of elements and get the length of the tuple in just a few moments. 

Tuple in TensorFlow

It is now time to go over some basic tuple-related code using TensorFlow. The steps for starting it are the same as those we always follow; have a look at these steps:

• Open your Anaconda Navigator by browsing it from your window panel. 

• Search for the Jupyter lab in the environment section. 

• Wait for the PC to open the new tab in your browser. 

• Go to the new cell.

• Start coding.

With the help of code, we will try to create a tuple, and then, by using the function on it we will try to retrieve the data from the tuple in different ways. So, have a look at the code given next and then guess the output in your mind.

print('Make a tuple with stationaries item')

myTuple=('pen', 'paper', 'eraser', 'pencil', 'sharpener', 'notebooks')

print('The tuple has following items: ',myTuple)

print()

print('print only the third item of the tuple:', myTuple[3])

print()

print('print the item by using the index: ',myTuple[1])

Now, have a look at the output. Is it exactly the same as you were expecting?

You can see how simple it is to get the item from the tuple when it is declared in the tuple. This is the same as what we did with the list. But what if we want more than one element from the tuple at the same time?

print('Make a tuple with fruits and prices in dollars')

myTuple=('strawberry','kiwi','banana','orange','apple', 2.5,3,12, 4,6)

print('The following items in the fruit shop are available: ',myTuple)

print()

print('print only the forth fruit of the fruit shop:', myTuple[-7])

print()

print('print the name of only fruits: ',myTuple[0:5])

Looking at the code, you will observe that the negative number is used in the index. This is the way to tell the compiler that we are counting from the end of the index. Moreover, the index on the end side starts at 1 instead of zero. Another point to notice is that the start and end limits are specified by separating them with the colon, and as a result, we get the whole tuple in the output. 

A new concept that is to be shared is that when you are providing the limits to the tuple, you have to take care that the right numbers are being used because it will then not work properly. You must be thinking that the compiler will throw the error when you feed the wrong limits in the tuple, but instead, the output will be empty, and you will get the parentheses with nothing in them.

Another thing that must be mentioned here is that you can check whether the tuple has a specific element or not. For this, we get the help of a statement. We know that, until now, we have not learned about the statements, and therefore, we suggest just looking at the code and output to understand the concept. 

print('Make a tuple with fruits and prices in dollars')

myTuple=('strawberry','kiwi','banana','orange','apple', 2.5,3,12, 4,6)

print('The following items in the fruit shop are available: ',myTuple)

if "apple" in myTuple:

  print("Yes, 'apple' is in the present in the fruit shop")

This program searches for the elements specified in the “if condition” and then prints the result on the screen. 

How to Make Changes in A Tuple

 If you are reading this lecture from the beginning, you must be thinking we have mentioned again and again that a tuple is immutable and unchangeable. Yet, programmers have the logic and solutions to every problem, and if the programmers have specified the tuple and want some changes made to it, they can do so with the help of the list. It is one of the reasons why we have discussed the list before in this series. Have a look at the following code and output, and then we will discuss it in detail.

print('Make a tuple with fruits and prices in dollars')

myTuple=('strawberry','kiwi','banana','orange','apple', 2.5,3,12, 4,6)

print('The following items in the fruit shop are available: ',myTuple)

myList=list(myTuple)

print(myList)

myList[2]='pear'

print(myList)

myTuple=tuple(myList)

print(myTuple)

Copy this code and run it on your TensorFlow, you will get the following output:

First, look at the brackets carefully and check the output in a sequence with the points given next:

• At the start, the string message is shown, which shows the main title of the tuple. 

• The name of the tuple here is “myTuple” and it contains the fruits’ names and prices.

• To make the changes in the tuple, we have to use another approach, and we know the reason why. For this, we are using the list. It is possible to convert the list into a tuple and vice versa, and we are doing the same in this. We are just using the name of the data type as a function and inputting the name of the data type to be changed. So, we changed the tuple into a list and then made the changes according to our choice. 

• By using the index number and feeding the value into the list, the list is updated. This can be observed with the help of square brackets.

• Once we have seen the updated list, we can now easily convert it into a tuple again. 

• The conversion is done with the same procedure, and we get the updated tuple. 

This was a simple example, other operations such as the addition of the new element, deleting the elements from the tuple, removing the single elements from the tuple, etc. are done with the help of lists. 

Deleting the Tuple Entirely

Now that you know all the ways to initiate, use, and update the tuple in detail, a last method for the tuple is ready for you. In some cases, when you do not want to use a particular tuple for any reason, such as if you are no longer using it, you can do so in just a simple step. This is used in the programs when long calculations and collections of data types are needed for a particular time and then there is no need for them anymore. So for this, we use the delete operation. The programmers have to simply use the del keyword before the name of the tuple to be, and the compiler has to do its job well. Have a look at the example given next:

Tuple = ("Artificial Intelligence", "Machine Learning", "Deep Learning")

print(Tuple)

del(Tuple)

print(Tuple)

So, when the compiler was on the second line, it showed us the results, but on the next line, when we deleted the declared tuple, the compiler was not able to show us the result because it had been removed from its memory.

So, it was an informative data types lecture in which we clarified many concepts about tuples. These are the data types that belong to the class of sequences. We read a lot about it and started the discussion with the introduction. The tuple characteristics were thoroughly discussed, and then, with these in mind, we tested the functions of the tuple using TensorFlow examples. We attempted similar examples and carefully observed the operation of tuples, which also involved the list. In the next lecture, you will know more about the data types in Python, so stay connected with us.

Hello my friends! I hope your doing very good. Today we are going to practice what we have learnt through the ladder logic tutorial series. We bring a new .project which is mostly exist in our daily life that is electrical door control. in garage for domestic and public garage, you should found automatic garage gate or door that is controlled by the PLC ladder logic program. So today we are going to implement the project including determining the input and output components, design the logic, programming and testing using the simulator.

Project details

Figure 1 shows the project’s components, inputs and outputs that are included in controlling the garage door. As you can see my friends, there are inputs like open, close, and stop requested by push buttons. Also, you can see sensors like the two limit switches on the bottom and on the top to prevent overload that might have occurred on the motors. Moving to the outputs, there are motor up and down directions, open, close, and ajar status indicators lamps. So what is the next step in our plan to achieve such job? Of course we need to list these inputs and outputs and give them addresses. Then we design the logic based on the user requirements and the safe operation.

Inputs and Outputs list

Table 1 lists the inputs and outputs showing the name in the first column, the description In the second column, while the addresses are listed in the last column.

The logic operation

After receiving the requirements of the client that tell how the door will be operated, managed, and controlled, we need to add the restrictions and safety conditions that secure the equipment like the motor, supply, sensing components. So the following lines state the operation and restrictions that need to be followed to achieve the designated task as follows:

• The door opening and or closing shall be stopped at any time user / operator hit stop push button

• The door shall open by requesting opening operation using the designated push button

• The door shall close by requesting shut down operation using the designated push button

• The indicator lamp of opening operation shall be lit while opening operation is in progress

• The indicator lamp of closing operation shall be lit while shut down operation is in progress

• The ajar status lamp shall be lit at any time the door is not fully opened or totally closed

Restrictions:

• The opening operation can not be requested while shut down process is in progress

• The shut down operation can not be requested while opening operation is in progress

• The opening operation can not proceed any further beyond the designated limit to avoid any overload on the motor

• The shut down operation can not proceed any further beyond the designated limit to avoid any overload on the motor.

Ladder Logic Code for Garage Door Project

The ladder logic program of the project is shown by figure 2. It shows two rungs, one for opening process and second for the shut down operation. The program can be more lengthy but we professionally resumed it in only two main rungs. The first rung represents the door opening operation in which, open push button initiates the opening process and the latching is there to let the opening process continue till the limit switch LS1 contacted or the stop is requested by hitting the stop push button. In addition, security of the process has been considered by including the condition of not having a shutdown process in progress represented by O:2/1. On the other hand, the shut down process is represented by the second rung. The shut down process initiated by the shut down push button and latching the door closing relay for continuing the shut down process unless one of the restriction conditions is met. The restriction conditions of shut down are the limit switch LS2, the door opening in progress, or a stop has been requested by operator using the stop push button. Also, indicator of the process status are included to show the opening operation thanks to the door opening indicator and the shutdown indicator. But we missed the ajar status of the door so let us add one rung for that.

As you see guys, the third rung has been added to implement the ajar status. It is simply clear that when the opening is in progress without reaching to the fully open indicator, or the shut down process is in progress without reaching to the totally closed, the ajar lamp is energized. Now let us go testing what we have coded so far and see if it is correct or not. But first you guys should sit and list the test cases that you should try to make sure the system is going to perform correctly and safely as well.

Testing the project

Door Opening test

In this test , we hit the opening door push button to request opening the door. As you see my friends, the door is opening as in figure 3 and the opening indicator is lit. also you can see the ajar indicator is lighted because the door is not reached the final opening position.

Figure 5 shows the state of the process after reaching the final position for opening the door. It is clear the process safely completed and the limit switch LS1 is gone green. Also the ajar status and opening indicators have been turned off.

Door closing test

In this test , we hit the door shut down push button to request closing the door. As you see my friends, the door is closing as in figure 6 and the closing indicator is lit. also you can see the ajar indicator is lighted because the door has not reached the final shut down position.

Figure 7 shows the state of the process after reaching to the final position for closing the door. It is clear the process safely completed and the limit switch LS2 is gone red. Also the ajar status and shut down indicators have been turned off.

Security and Safety Test

In this test case we want to request opening .the door while it is shuting down and try to request shut down the door while it is opening to see is there any issue or not. Figure 8 shows what is going on when we requested opening the door while a shuting down process is inprogress. You can see guys in rung number one, the opening push button ,is pressed as circled and highlighted see I:1/0. However, the requested process has not performed due to the restriction that the shutdown process should not be in progress. Same thing when opening door is in progress, shut down requests are forbidden.

Hey, peep! This is a connected tutorial from the previous one where we saw the detail of numeric data types. This time, we are moving forward with the other data types in Python. We are understanding all these concepts with the help of examples and practising the little but understandable codes in TensorFlow. Different types of operations are also performed on these data types so that you may have an idea of why we are differentiating all these data types and how we can categorize all of them into different groups. Keep in mind that all of these concepts are for deep learning, and we want to make sure that we will not face any problems in the complex work of deep learning; therefore, we are moving slowly and steadily in Python.  So, have a look at the content you are learning in this tutorial, and after that, we’ll start the introduction. 

• What are the strings in Python?

• How do you declare the string in different ways while working in Python?

• What are escape sequences in Python?

• How can you use the triple quotation in Python and why it is useful?

Each concept will be discussed with the help of simple and easy codes and the description of each of them is discussed in detail in this lecture. This is a connected part of the lecture that was discussed in the last lecture and other data types will be mentioned in the next lecture. 

Strings in Python

A string is nothing but the combination of different alphabets in a specific sequence. In programming, the concepts are a little bit different from those in the real world. The way we speak in English is said to be "string" in the programming language, and it is an important data type in any programming language for non-programmers, anything that comes on the screen must be easy to understand, and string is the best way to print the message on the screen. Let us define the string in simple words.

"The string is the sequence of characters or alphabets that specify the message on the screen and it is denoted by 'str' in Python."

We always say that python is simpler and easier than other programming languages, and it is true for the concept of string as well. In Python, the string can be denoted by using single or double quotation marks and the usage of commas is according to the choice of the programmer. In other words, you can use the single or double inverted commas around the alphabet to represent the string. Moreover, you must know that string has no limited length same as in the case of integers. The only thing that limits the length of the string is the memory space of the system you are using. Have a look at the syntax of the string while you are working on Python. 

Python String in TensorFlow

First of all, you have to look at the syntax of the string. The syntax of all the data types is the same; therefore, we are mentioning just this one, and after that, you will have an idea of how to implement the other data types. We have mentioned in the previous lectures that you just need the name of the variable and then the value of the variable in the form of any data type you want, so the syntax is given as

string = "I am learning Python for Deep learning."

The name may be anything, but it is important to use inverted commas (either single or double). TensorFlow will make this clearer, but there is a short procedure to get TensorFlow up and running.

• Search for the “Anaconda Navigator” on your PC. 

• In the environment section, click on the Jupyter lab and launch TensorFlow. 

• The new tab will open in your browser with the name “Local host.”

• Go to the new cell and start coding there. 

Now, you have to write the following code in the new cell and run the program to get the output.

string="I am learning at TheEngineeringProjects"

print(string)

a=" "

print(a)

b='Python is best for Deep learning'

print(b)

The output of this code is given as:

From the code and output, we can conclude with the following point:

• The name of the variable may be anything. 

• We can use single or double inverted commas for the string and the result will be the same.

• A string may be an empty space so we can say that length of the string is zero to positive infinity.

• The output of each print function is always shown in the next line in normal conditions. 

So, the best way to show any message to non-programmers is in the form of a string. 

Escape Sequence in Python

In most high-level programming languages, there are certain words that are chosen to be used for a special task in Python with the help of their special sequence. These are known as the escape sequence. These are defined as:

"The escape sequence in Python is the combination of characters that, when used inside a string or character, does not show itself but does the specific task according to its functionality."

It is important to notice that these are important concepts in Python because it is very difficult and in some cases, impossible to do the same task without using the escape sequence. Now, have a look at some of these in the next section and you will get the detail of some important escape sequences. 

New Line in Text Using TensorFlow

As you can see, in the previous code, we made a space between two lines with the help of an empty string. But, what if we want to print a new line in the message? For this, we use a special operator in the string message and it is used in places when the output is long or there is the need for more than one line to represent the message more clearly. The operator is a backslash with an “n” that can be used at any place in the text. Have a look at one example to do so.

print('The backslash with "n" is used to \nprint a new line')

The output of this single-line program is interesting.

It is important to notice that if you want to work with this operator, you have to use it properly. You can not use any additional space between the text and this new line operator; otherwise, you will get an error message from the compiler.

Tab Operator in Python

We all use the tab on the keyboard but what if you want a space between the text? You can do so by applying the space between the text while you are printing the message but it is not the professional way. To do this with convenience, just like some other programming languages, Python has a special operator. Same as we use the n in the new line operator, if you use the t with the backslash, you can print the space between the text that is equal to eight space bars.

print('The backslash with "t" is used to \tprint a tab space in the line')

Let’s see what we have in the output of this code.

Same as these operators, we also have some other commands that do the work similar to this and the syntax of all of these is the same. For the convenience of the reader, we have made a table that contains all the information about these operators. Have a look at the table and after that, we will run all of these at once in our code in TensorFlow.

To test each of the commands discussed before, we are rushing towards the TensorFlow, where we are using all of these in a similar ways to show the difference between all of these. Keep in mind, the syntax of each of them is the same. These are not the proper functions but are the pre-defined commands that are used less commonly in Python. Here is the homework task for you. You have to look at the code we are giving below and without cheating will guess the output.

print('We use \ncups for tea')

print('The tab create \teight spaces in the text')

print('\aBullets can be made using this operator in Python')

print('If you want to delete the \bspace, you can use the operator in Python')

print('This part will be ignore \ronly this will be printed on the screen')

print('\vThis small arrow looks cute')

Once you have guessed the output, now check this in your compiler, and then match the output with the one that is given next in the image.

The last arrow can also be used as the bullet in the text you want to show on the screen. Another task for you is use the different text and practice the code and string with your own message. It is the simplest and interesting task that you must try on your TensorFlow. 

Triple Quotation String in Python

Here is another way to indicate that you want to declare the string. This is a less common way to represent the string, but if you are studying the string, you must know this. You can use double and single inverted commas around the text at the same time, and this will not cause any errors. Let me tell you about the workings of this kind of string. After that, I’ll show you how you can use it for different purposes.

print('''Deep learning is the subclass of the artificial intelligance''')

So, we are using three quotation marks around our text and getting the same output as for the single and double inverted commas. Here is the output:

The advantage of using this method is, you do not need any new line operators to start the text on a new line, but you will write the code as you want the output.

This is a more interesting and convenient way to write your text. Right now, you must be wondering why we are highlighting such uses and creating this hype. Yet, you must know, the aforementioned ways of writing the codes will help you a lot when you will go to the complex and long codes for the deep learning. One of the application of this way to declare the string is in this lecture. We can use this way to declare the string and can perform all the escape sequence command by declaring a single string in different lines so that we may not have to write “print command” every time when we are working with a new escape sequence.

Hence, we have read a lot about strings today. It was an interesting lecture where we saw what strings are and how we can use them in our coding in different ways. We say the representation, working, and the escape sequence between the string. The syntax of each case was clarified with the help of examples in TensorFlow. You will get information about more data types in the next lecture.

Hey fellow! Welcome to the next episode of the Python series, where we are learning the basics of Python to implement them in deep learning. In the previous lecture, our focus was on string data types. With the practical implementation of TensorFlow, many interesting points were discussed in depth. I hope you completed the home task that I assigned you during that lecture. Today, we are moving forward with the next data type, which is a sequence. You will know the different sub-groups of this data type as well in the next lecture, but today, the focus will be totally on the list because, once you understand them well, other data types of the sequence will be at your fingertips. Yet before starting, it's time to look at the content that you will learn today:

• What is a sequence?

• How do you classify the sequence into different types?

• How do you understand the list?

• What are some characteristics of this list that make it useful and different from the others?

The workings and characteristics of the list are interesting, and these will be well understood when we start our TensorFlow for different examples taken from our daily lives. We hope you have a strong grip on the integers, strings, and floats because, in our example, we will use them and will try to make changes in the list to show the difference in detail. So, without using your time, I am going to start the learning phase in Python.

Introduction to Sequence in Python

In programming languages, we use a lot of items, objects, classes, and related concepts, and when we talk about the sequence, that is a data type that provides us with the concept of a collection of things. These are useful concepts that allow us to work with them in a useful way. We define the sequence in the following way:

"The sequence in Python is a generic term that defines the ordered set of items in such a way that any of the items can be easily referred to."

Here, the word “ordered set” is important to notice. We’ll talk about it in detail in just a bit, but before that, let me tell you that the string is also considered to be the sequence because it contains the sequence of the characters. We have read a lot about the string, and therefore, we know that the ordered sequence of the characters, or alphabets, is called the string. Yet, I had a lot of data to tell you about these concepts; therefore, I have discussed them separately. There are two natures of the sequence that are listed below:

1. Homogeneous sequence

2. Heterogeneous sequence

The difference between these two is that a homogeneous sequence contains a group of items of the same kind.

It is important to know this difference because, on the basis of it, you will get the further types of the sequence. 

Types of Sequence in Python

The collection of the data can be represented in different ways, and on the basis of these types, we can divide the sequence into six major types. All of these are important to understand because we use the sequence often while programming and when dealing with complex subjects such as deep learning (in which we are interested), and the sequence plays a vital role in organizing the data in an understandable way. So, have a look at the types of sequences:

1. String

2. List

3. Tuple

4. Byte sequence

5. Byte array

6. Range objects

Out of them, the strings have been discussed in detail in the previous lecture. So, we are skipping that for now. You will be familiar with the remaining ones in depth, and things will become clearer to you with the help of examples. Thus, have a look at them: 

List in Sequence

In some other programming languages, such as C++ and C#, there is the concept of arrays that we study a lot. Yet, in the Python programming language, this concept is not used; instead, lists are used. The list is indicated by the square brackets just like the arrays, but it is a little bit different from them. The list is the type of sequence that contains an ordered group of different items. More information about the list can be found in the table below:

Here, it is important to notice the representation of the list with the square bracket because the bracket is the only way to differentiate it from the other types of sequence that you will learn in the coming lectures. The operation of the list will be explained in simple steps in a moment, but first, let us open the Python workspace for practical implementation. For this, you simply have to follow the steps again that we always mention:

• Search for the Anaconda Navigator on your personal computer. 

• Go to the environment section and look for the “Jupyter lab” or “Jupyter notebook." For this tutorial, we are using the Jupyter lab. 

• Go to the new cell and get ready for the coding.

The list's information does not end with the table mentioned above. Certain characteristics make the lists useful, and you will learn about them in the following section.

Mutable list in Python

One of the most significant features of Python is its mutable nature. To know the meaning of the line we have just said, you must know that when we declare a list, we define the size of that particular list by mentioning the number or directly feeding the elements in it. The list's advantage is that the programmer can change the elements at any time to meet the needs of the situation. In addition to this, if you want to change the individual value of the list, you can do so easily. Not only this, but you can also change the order of the elements while working on the code. It means if you want to swipe or change the position of elements 1 and 3, you can do so easily. In short, you can say that, while working with the list, all the controls are in your hands. 

MyIntList=[1,56,8,12,56,90,3,67]

print('The homogenous integer list is : ', MyIntList)

MyFloatList=[1.2,56.2,8.2,12.2,56.2,90.2,3.2,67.2]

print('The homogenous float list is : ', MyFloatList)

You can see that all the elements belong to the same data type, so we are calling it the homogeneous list. In the second code, we want to do something different, so have a look at the code.

list=['eggs', 2.0, 34, 'plates']

print('The elements of the list are : ', list)

print('The element at position 2 is : ', list[2])

print('The elements before the second position are : ', list[:3])

list=[ 34,2.0, 'eggs', 'plates']

print('Now the new list is: ', list)

By looking at the code given above, we can conclude the following points:

• To declare more than one type in a single list, the programmer simply has to use the accurate way to declare them in the list, and nothing special has to be done during the declaration of the list. 

• To get a specific element from the list, simply the position of the element is mentioned. 

• The position in the elements start from the 0 just like the arrays and you have to declare the element accordingly. 

• The order of the elements can be changed easily and to do this, the programmer have to merely change the elements according to the will with the same number. Here we have used two types of lists with the same name but by changing the order of the elements and the code is working well.

• To get the elements of the list to a certain limit, there is a need of mentioning that number of position along with a colon. Keep in mind, here we specify the position 2 and all the elements before the third position are shown to us. Same can be done to get the elements after the mentioned number and it is your homework to know how can you do so. 

Dynamic Nature of List in Python

When you compare the list to the array, you can better understand Python's dynamic nature. Let me remind you that arrays have a fixed size and cannot be changed once declared in your code. But in the case of the list, once you start working on it and need to change its size, you can increase or decrease its size by merely mentioning it in just one line. In other words, your list can grow or shrink according to your choice. To understand this you must know about a built-in function in Python.

List Length Function 

The length is a built-in function in Python that provides the length of the functions made in the code. You simply have to input the name of the function in it and it will show you the number of elements specified in that particular function. The syntax of the length function is:

len(name)

By using this function in the list mentioned above, the number of elements can be obtained with the help of this code:

list=['eggs', 2.0, 34, 'plates']

print('The length of list is : ',len(list))

list=[34,2.0, 'eggs']

print('The new length of list is : ',len(list))

The output of the code is given next:

Heterogeneous List in Python

Keep this in mind the next time you go grocery shopping and want to buy something else; make a list for it. It will contain vegetables, meat, cloth, spices, and other things of the natural world. Because of its heterogeneous nature, the list is the same. It feels like a relief that you can make a list that contains strings, integers, and any other data types in one place. In this way, you can deal with multiple types of data at once. In arrays, this is not possible. You can also add functions to the list that contain elements of different types. You have understood it well with the previous examples in TensorFlow, but I want to show you this by mentioning a built-in function in it. 

Mylist=[23,'decoration', False ]

print(Mylist)

Yet, there is a bit of difference between the keywords and loops; therefore, you can not mention the loops in the list; otherwise, you will face the same error as given next:

Here, the error is demanding that you write the code in the valid syntax. It is because the compiler is not able to understand that the for loop is an element of the list, but it demands the proper syntax for the for loop and is expecting an iterative procedure. Another thing to notice here is the color of the word “False” in the list. All the keywords, while coding, are shown in green, showing that it is a built-in function or keyword and that the compiler has understood the default functionality of that particular keyword.

Hence, it was an interesting tutorial, through which we learned a lot about the list data type. We initially understood what list data types were and why we were learning them. After that, a detailed overview of the list was discussed, through which we found the characteristics of the list. It was easy to understand as we practically performed each step of the TensorFlow with the help of different examples that were related to the general examples, so we were able to understand it well. If you have a clear understanding of the list, the following lectures will be simple to grasp because they are related concepts. So, stay tuned with us to get more information about Python.

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.