The Engineering Projects

Hi Guys! Hope you’re well today. I welcome you on board. In this post, I’ll walk you through Electronics DIY Projects to Improve Work from Home.

Electronic devices are not cheap and rightly so. Since you require advanced setup and technical skills to build something sophisticated and delicate. The good news is that you don’t have to spend a fortune on such devices since DIY electronic projects are the solution. You can make similar electronic projects you find online at home and save a lot of money. Some people prefer working on a breadboard while others prefer building on printed circuit boards. However, if you’re new to this field, we’ll suggest you start from the breadboard, before building your projects on the PCBs. The good thing is that you don’t require a big setup or advanced tools to work on breadboards. Basic computer knowledge and a few tools and electronic components would suffice.

Know that nearly all of these electronics projects can be developed in less than a day if you have the required tools and components. It won't be difficult to test out these creative ideas for electronics projects because, fortunately, you can search hot electronics parts from kynix for a low price.

I suggest you read this article all the way through, as I’ll be covering in detail electronics DIY projects to improve work from home.

Let’s get started.

Electronics DIY Projects to Improve Work From Home

The long, warm months ahead can only mean one thing for DIY enthusiasts: polishing up the skills over the project after project. For some people, that can include finishing a picture or creating original dishes in the kitchen. And for tech nerds, it is learning new software or building electronic projects at home. 

Looking for easy ways to spruce up your technical skills? These simple DIY Electronic Projects would help you to get your hands dirty in the electronic field without spending a lot of time and money.

1: Lead Acid Battery Charger

This is a simple electronic project used to charge your lead acid battery. It comes with LM 317 which is the main component of the circuit that serves as an operational amplifier mainly employed to deliver the exact charging voltage to the battery.

Components Used

• Operational Amplifier LM 317

• Transistor BC 548

• Transistors

• Capacitors

• Potentiometer

How It Works

• LM317 provides the correct voltage for the circuit and the transistor BC548 is employed to control the charging current delivered to the battery. 

• It is worth noting that one-tenth of the Ah value of the battery must be charged - the basic idea behind charging this circuit. The charging current can be adjusted using the potentiometer R5. While Q1, R1, R4, and R5 regulate the battery's charging current. The current flowing through R1 rises as the battery charges and it changes how Q1 conducts. The voltage at the LM 317's output rises because Q1's collector is connected to the IC LM 317's adjustment pin. 

• The charging current is reduced by the charger circuit after the battery is fully charged, and this mode is known as trickle charging.

2: FM Transmitter Circuit

Signal transmission is crucial when you want people to hear someone from a distance. Especially in factory and college settings to allow people to hear programs and speeches within range. This is a low-cost, simple electronic project used to create an FM transmitter circuit that has a 2-kilometer range for signal transmission.

Components Used

• Matching Antenna

• Transistors BC109, BC177, 2N2219

• Capacitors

• Resistors

• Battery 9 to 24 V 

How It Works

This is a simple DIY electronic project that you can easily develop at home. It comes with a 2 km range for transmitting the signals. 

• In this setup, a 9 to 24 DC power supply battery is used to power the circuit which not only ensures the optimum performance but also helps in reducing the noise. 

• The traditional high-sensitive preamplifier stage is formed with the transistors Q1 and Q2. Know that the audio signal required to be transmitted is connected to the base of Q1 using capacitor C2. 

• The oscillator, mixer, and final power amplifier functions are all carried out by transistor Q3. And the biasing resistors for the Q1 and Q2 preamplifier stage are R1, R3, R4, R6, R5, and R9. The tank circuit, which is formed by C9 and L1, is crucial for producing oscillations. 

• The FM signal is coupled to the antenna by inductor L2. Recognize that, the circuit frequency can be adjusted by varying C9 and R9 is employed to adjust the gain.

• Make sure you apply this circuit on good-quality PCBs, as poor-quality connections can hurt the overall performance of the circuit. 

3: 150 W Amplifier Circuit

Tired of your speaker’s low noise? Don’t panic! Since this is another easy-to-design electronic circuit that provides 150W to the four Ohm speakers – enough to provide you with a lasting, ruthless buzz to rock and roll. The basic component of the project is the pair of Darlington transistors TIP 142 and 147. 

Components Used

• Darlington transistors TIP 142 and 147

• Transistor BC558

• Resistors

• Diode 1N4007

• Electrolytic Capacitors rated at least 50V

How It Works

• This circuit is effective for those just starting in the electronic field. In this circuit, TIP 147 and 142 are complementary Darlington pair transistors known for their durability that can handle 5 A current and 100V. 

• Know that Q5 and Q4 of two BC 558 transistors are joined together as a pre-amplifier also called a differential amplifier. This is used for two main reasons: for providing negative feedback and for reducing the noise at the input stage, thus improving the overall productivity of the amplifier. 

• While TIP41 (Q1, Q2, Q3) and TIP 142, TIP 147 together are employed to drive the speaker. This circuit's construction is so robust that it can be put together by soldering directly to the pins. A dual power supply with a +/-45V, 5A output can power the circuit. 

4: Police Siren

A siren is a device that produces a usual louder sound to alert and/or attract people or vehicles. Typically, ambulances, police cars, fire trucks, and VIP cars are among the vehicles that use the siren.

Basic Component

The basic component of the project is the 555 timer which is one of the most adaptable chips that can be applied in practically all applications because of its multi-functionality. It is an 8-pin chip with a 200 mA direct current drive output that comes in a DIP or SOP packaging. This IC is a mixed-signal semiconductor since it has both analog and digital components. The IC's primary uses include producing time, clock waveforms, square wave oscillators, and numerous more functions.

Working Principle 

• Using two 555 timers, speakers, and a basic circuit, this breadboard project creates a police siren sound. As indicated in the diagram above, an 8 Ohm speaker is connected to two 555 timers. 

• Note that, one 555 timer is attached in an astable mode (it carries no stable state, instead comes with two quasi-stable states which quickly change from one state to another and then back to the original state) and the second 555 timer is connected in the monostable mode (one of the two states is stable, and the other is nearly stable. 

• When a trigger input is applied, it changes from a stable state to a quasi-state and then returns to the stable state on its own after a certain amount of time) to achieve the appropriate frequency. 

• This setting creates a siren with a frequency of about 1 kHz. Using the knob in the circuit, the siren sound frequency can be changed to match the police siren sound. The siren is used not just in automobiles but also in many businesses, mills, and other establishments to notify workers of their shift times.

5: Smart Fan Circuit

With the help of a few basic components, a cooling system to regulate a DC fan is designed in this simple breadboard project. The goal of this project is to build a cooling system by easily operating a DC fan without the need for microcontrollers or Arduino, but rather by using readily available and straightforward electronic components. Once the temperature hits a certain level, this fan will turn on.

Basic Components

• 5V DC Fan

• 5V battery

• NTC thermistor-1 kilo-ohm

• LM555 Timer

• NTC thermistor-1 kilo-ohm

• BC337 NPN Transistor

• diode 1N4007

• capacitors 0.1 uF & 200 uF

• LEDs

• Connecting Wires

• Resistors like 10k ohm, 4.7k ohm, 5k ohm

• Breadboard

Working Principle

• In this circuit, the DC fan can be controlled using a thermistor. The resistance of the thermistor, a particular type of resistor, is largely dependent on temperature. Thermistors come in two main types including NTC (Negative Temperature Coefficient) and PTC (Positive Temperature Coefficient).

• When an NTC is employed, the resistance decreases as the temperature rises. This is the opposite in the case of PTC where resistance and temperature are directly proportional to each other. 

• When the temperature reaches a certain threshold, the fan turns on. The first LED, "The green LED," which indicates rising temperature, will turn ON as the temperature rises. 

• The second LED will turn ON when the temperature reaches the second threshold, and the fan will run as long as the temperature is over the second threshold. The fan will continue to run for a set amount of time once the temperature returns to an acceptable level.

6: LED Chaser Circuit

This simple LED chaser circuit is developed using a 555 timer and 4017 IC. Together, the two ICs in this project run the LEDs in a sequence to create the illusion that they are chasing each other.

Basic Components

• 555 timers

• CD 4017 IC

• Resistors 470R, 1K & 47K

• 1uF capacitor

• Connecting Wires

• 5 to 15 V power supply

• Breadboard

Working Principle

• Before you start working on the project, you must visit the pin diagrams of both ICs. It will help you to identify the correct pins to be used in the project. 

• When an IC – a 555 timer – is used in an astable mode (that produces a square wave), its output fluctuates continually between high and low supply voltages. For instance, an LED will continuously blink if it is connected between 555 timers and the ground.

• The CLK input of a decade counter is connected to the output of a 555-timer IC. This IC has ten output pins, each of which is wired to an LED. The remaining output pins will all be switched OFF once the first pin is turned ON.

7: Model Traffic Lights

This simple, low-cost traffic lights model circuit is designed using two 555 timers and some other basic components. 

This circuit comprises three LEDs for the indication of RED, Yellow, and Green traffic light signals. First, it will turn ON a green LED, maintains it on for a while, then briefly turns ON a yellow LED before turning on a red LED that remains ON for almost the same amount of time as the green LED.

Basic Components

• 555 Timers

• Resistors of 100K, 47K, 2 x 330R, 180R

• LEDs – Yellow, Red, and Green

• Connecting Wires

• Power Supply 5-12 V

• 2 Capacitors of 100uF

• Breadboard

Working Principle

• The circuit comes with two astable circuits where the first astable circuit will power the other. Therefore, only if the first 555 timer IC's output is ON will the second 555 timer IC be powered.

• When the output of the first timer remains at 0V, it will turn ON the red LED. The green LED turns ON anytime the output of the second 555 timer IC is at a positive voltage, and the yellow LED will turn ON when the second 555 IC is in discharge mode.

• The yellow and green LEDs would turn on at the same time. However, even before the voltage across the capacitor of the first 555 timer IC reaches two-thirds of the supply voltage, the output of the first 555 IC goes off, which will allow the red LED to turn ON and the yellow LED to turn OFF.

Conclusion

Hope you’ve got a brief idea of how to get started with electronic projects.

Getting hands-on experience will not only improve your technical skills but also help you to develop critical thinking to get familiar with advanced electronics. 

It’s okay to become acquainted with PCBs, but if you don’t know how to solder properly or how to design a good PCB layout, it’s preferred to start working on the breadboard to keep your project up and running.  

That’s all for today. Hope you’ve enjoyed reading this article. If you’re unsure or have any questions, you are welcome to reach out in the section below. I’d love to help you the best way I can. Thank you for reading this post.

Hi Guys! Hope you’re well today. I welcome you on board. In this post, I’ll walk you through How a Hobbyist Can Work on Electronic Projects in America.

Working on electronic projects is a bit inundating.

From selecting the topic to research work and development to execution, you need to hustle, grind and drill to keep your final product up and running.

When you are new to the electronic field, you must not be afraid to get your hands dirty in diving deep into the nitty-gritty of the project. This means that no matter what kind of technical project you pick, you need to spend a significant amount of your time and money to reach your final goal. It's not just about making sure that whatever it is you're looking for is done well—it's also about making sure that your project is done right from the start.

I suggest you read this post all the way through as I’ll be covering everything you need to know to make electronic projects as a hobbyist. 

How a Hobbyist Can Work On Electronic Projects in America?

Development of electronic projects is tricky especially when you lack direction or you’re overwhelmed by the options available online. You can pick from a range of projects but the main goal is execution. If you fail to produce something that you proposed initially, it’s not worth it. NOPE. It’s not a good idea to pick the most difficult project to impress your instructors. Choose what resonates well with your expertise and helps you grow and excel in your field. 

Newcomers have so many questions when they are about to get hands-on experience on the project. They don’t know how to start without lacking enthusiasm throughout the entire process. Don’t panic! We’ve streamlined a few steps in this post that will help you to complete your electronic project from start to finish with a proper strategy in place. 

Brainstorm Ideas

Whether you’re working alone or in a group, it all starts with brainstorming a few ideas. If you’re working in a group, make sure you work on concepts with shared interests and common grounds. Having a fruitful conversation before picking up the topic will help you figure out everyone’s weaknesses and strengths. What you lack in one area may well be covered up by someone good in that field. And if you’re aiming to develop something amazing for your final year project, this is a great opportunity to leave some sort of legacy for your juniors.

The following are the key considerations while brainstorming ideas for your electronic project.

• Must be doable. You must have abilities to turn your thoughts into reality.

• Start with something new. With the recent advent of technologies, there is a scope for covering something that has not been discussed before. Try incorporating microcontrollers and Arduino boards into your projects with new peripherals. 

• The best idea could be where you address the problem and provide a solution. 

• Cover both hardware and software. This is important. Covering both aspects of the project will not only polish your skills but also leave room for improvement for your juniors to work on.

• Within price range. Yep, it should be well within your budget. Though you can ask for sponsorships if you want to produce something from a commercial aspect, still it’s wise to pick something that you can easily afford. 

• Should be completed in due time. That’s true. Deadline is important. Of course, you wouldn’t want to spend your money, time, blood and sweat that you can’t submit within time. 

Plan your project

Once you’ve finalized the project idea, it’s time to play… Yeahhhhhh! Yep, it’s time to plan your project. 

Say, if you have six months to complete your project, then divide the whole duration into dedicating each aspect of the project to a specific time limit. For instance, spare two months for research purposes, the next two months for purchasing the components and development of the project, and the final two months for the testing and execution of the project.

It is observed most engineering don’t plan their project according to the time limit and in the last month, they will be scratching their heads and doing everything to run the project. Even I did this mistake in our final year project. And we had to ask for extra days from our instructor to complete our project. So, I suggest you… please don’t do this mistake and plan your project accordingly. 

Start Research

Until now, you’ve selected the topic and planned the project. Now comes the research part. This is the backbone of the entire project. Start your research with what’s required to be included in both hardware and software. 

• Your time and energy are wasted when you rely on an inaccurate source of information. To research a subject with confidence and to cite websites as support for your writing, you should streamline your research to gain a clear understanding of the subject.

• Make sure the hardware components you select are available in the market. And even if you have to buy them from outside the country, you spare enough time to incorporate them into your project. 

• Thoroughly go through the datasheets and pin diagrams of the components and look for possible substitutes. Why use an expensive part when a cheap substitute would suffice? 

• Apart from finding the components from the local market, there are scores of places online where you can get the right products. Some are better than others. But how do you differentiate them when all claim to be the best? Don’t fret! You can use Utmel Electronic Online Store which gives you quality electronic parts and components at reasonable prices to support your electronic project. From batteries, audio products, and connectors to capacitors, transistors, and evaluation boards, this place is a haven for tech nerds. 

Hardware Development

Hardware development is not a linear process. Sometimes you’ll witness going two steps backward for every step ahead. Don’t fear when this occurs since it’s what is required to turn your imagination into reality. Making hardware includes both: creation of mechanical structures and electrical circuit development.

• The first step in developing the mechanical structure is making the 3D model on the computer. You can use “SolidWorks” to create the overall exterior of the project. Once you design the 3D model, turn it into a physical prototype. You can only create the model in the software but most probably, you will require someone in the market adept in understating the complexities of injection molding. Since this process is a bit tricky with many rules and regulations to follow.

• If you’re a beginner and are not familiar with the nuts and bolts of developing PCBs, it’s wise to first create your hardware on the breadboard. This will help you identify all the possible mistakes before installing all these components on the printed circuit boards. Moreover, breadboards are user-friendly and you don’t require advanced technical skills to run your project. 

• PCBs are the cornerstone of many electronic and electrical units that provide a pathway to reduce their technological size. A PCB is often made of laminated materials like epoxy, fiberglass, or a variety of other materials that provide an essential framework for organizing electronic circuits.

• You need to design your PCB to create PCB layouts. Print out your PCB layout on the glossy paper and transfer that print onto the required size copper plate. 

• Make sure you place the main IC into the center of the board to allow even connections with all the electronic components. 

Software Development

You have developed the required hardware for your project. Now is the time to use your programming skills to run your hardware. 

• If you’re using a microcontroller or Arduino, you might need to learn C++ since Arduino code is written in C++ with the inclusion of special functions. The code you build on the software is called a ‘sketch’ that is compiled and processed to machine language to run your hardware by the instructions given by the human input. 

• Similarly, if you aim to create development boards, MATLAB software is used which is quite handy for Data analytics.

• A PCB and electronic design automation software suite for printed circuit boards is Altium Designer. 

• All circuit design activities can be completed using the tools in Altium Designer, including schematic and HDL design capture, circuit simulation, signal integrity analysis, PCB design, and the design and development of embedded systems based on FPGAs. The Altium Designer environment can also be modified to suit the needs of a wide range of users.

• I don’t highly recommend this trick but if you find yourself stuck in some part of the hardware or software development, you can outsource that part to freelancers. But this is highly risky since if your instructor finds out that you were not the one who did that part, you may get into hot waters. Make sure you get your instructors on board before outsourcing the most complex part of the project. 

Test Your Project

You might have done everything right from the start, but it’s unlikely that your project starts in one go. You might need to run your project through a series of test and trial methods to identify errors and glitches in both hardware and software.

Always create a backup plan. Make your hardware in such a way that if you require some modification in the process, you can do so. 

For instance, you can make a plastic casing for your mechanical structure before going for the hard metal enclosure. Ensure that the end product resonates with what you initially proposed in your proposal.

Articulation and Presentation 

Once your project is completed and carefully tested, next comes the writing process. 

• Anyone can write but good writing needs practice. Make sure you dedicate this part to someone good at jotting down ideas in a clear and meaningful way. 

• Since the audience will get to see the end product. They don’t care how many struggles you withstood and how many sleepless nights you went through, they care about how your project can be beneficial for them and how it can solve their problems. 

• Additionally, it’s all about presentation. If you don’t know how to skillfully present your project, you fail to convince the audience that your project is worth spending time and money on. 

• It will be helpful to use data visualization in your presentation to present your project clearly and concisely. Throughout your presentation, be ready to respond to the panel's queries with care and attention. 

• And finally, don’t forget to make a video of your running project. Sometimes, even though the project runs smoothly, it doesn’t execute well in front of the instructors. So it’s wise to be on the safe side and record the video of your project. 

Some Electronic Project Ideas 

• You can make a home automation IoT project to remotely control the appliances of your home.

• You can build an automatic security system that informs you whenever someone trespasses your home boundaries. 

• Develop an advanced light system that can be used to turn on the light loads whenever they detect human presence within range. 

• You may also create a robotic metal detector system that can find metals in the ground, and inside food products with the help of radiofrequency technology.

• Build automatic solar tracker. To make sure your panel receives the most radiation possible throughout the day, you can construct trackers that follow the sun's path from sunrise to sunset. 

• Make Wireless Lock System Through OTP that provides a smart security solution. 

Conclusion

Still reading? Perfect.

It means you’ve learned some valuable insights into how to develop your electronic project from start to execution. 

Just follow these steps and you’ll be well ahead in turning your idea from ideation to execution. 

Start with a simple doable idea. Some ideas may look best initially, but when you start working on them they become unrealistic. 

Don’t forget to ask for help if you get stuck in the process. Since when you never ask for it, the answer is always NO. 

That’s all for today. Hope you’ve enjoyed reading about how a hobbyist can work on electronic projects in America. If you are unsure or have any questions, you can ask me in the section below. I’d love to help you the best way I can. Thank you for reading the article.

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

• What is a tensor?

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

• How can we start programming in TensorFlow?

• What are the different operations on the TensorFlow?

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

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

What is a Tensor?

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

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

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

Types of TensorFlow

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

The Shape of Tensor

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

The Rank of Tensors

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

Type of Tensor

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

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

1. tf.Variable

2. tf.constant

3. tf.placeholder

4. tf.SparseTensor

Basics of TensorFlow Programming Language

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

Comment in the TensorFlow

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

Printing the Message in TensorFlow

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

print(message)

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

Applying Operations in TensorFlow

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

• Fire up your Anaconda navigator from the search bar. 

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

• Click on the launch button. 

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

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

import tensorflow as tf

from tensorflow import keras

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

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

Printing Multi-Dimensional Arrays in TensorFlow

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

Printing the Two Dimensions Tensor

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

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

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

print(a)

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

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

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

a=tf.ones((3,3)

print(a)

The result should look like this:

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

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

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

print(a)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Is Installation of TensorFlow Difficult?

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

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

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

• How can we install TensorFlow with Jupyter?

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

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

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

Pre-requisites for TensorFlow

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

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

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

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

Choosing the Best Installation Method for Anaconda

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

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

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

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

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

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

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

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

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

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

Installing the TensorFlow Framework

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

• Go to the start menu of your window. 

• Search for the “Anaconda command prompt."

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

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

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

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

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

Import TensorFlow as tf

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

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

Installing the TensorFlow Environment with Jupyter Navigator

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

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

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

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

Installing the Keras with Jupyter

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

• Open the Jupyter navigator.

• Click on the "create" button. 

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

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

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

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

Checking for the Installation of TensorFlow

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

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

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

Import tensorflow as tf

From tensorflow import keras

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

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

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

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

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

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

• Why do we use TensorFlow with deep learning?

• What are some helpful features of this library?

• How can you understand the mechanism of TensorFlow?

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

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

• How the data is represented in the TensorFlow?

Why TensorFlow for Deep Learning

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

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

• Flexibility

• Easy to train

• Ability to train the parallel neural network training

• Modular nature

• Best match with the Python programming language

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

Helpful Features of TensorFlow

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

APIs of TensorFlow

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

1. C++ 

2. Python

3. Java (Integration)

4. R (Integration)

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

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

• To configure the neuron

• Work with the neuron

• Prepare the neural network

TensorFlow Supports Different Computing Devices

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

1. Central processing unit

2. Graphical processing unit

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

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

Working Mechanism of TensorFlow

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

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

• Android apps

• iOS

• Cluster

• Local machines

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

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

The Architecture of TensorFlow

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

• Data Processing

• Model Building

• Training of the data

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

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

Phases of the TensorFlow Projects 

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

Development Phase: 

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

Run Phase

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

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

Components in TensorFlow

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

Tensor

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

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

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

Graph

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

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

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

Dataflow Graphs in TensorFlow

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

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

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

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

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

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

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

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

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

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

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

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

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

Introduction to the Python Coding Language

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

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

Readable Code

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

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

Multiple Programming Paradigms

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

• Object-oriented Programming

• Structured programming

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

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

Flexibility with Other Platforms

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

• It supports multiple operating systems.

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

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

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

Robust Libraries of Python

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

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

Opensource Frameworks

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

• Django

• Flask

• Pyramid

• Bottle

• Cherrypy.

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

• PyQT

• PyJs

• PyGUI

• Kivy

• PyGTK

• WxPython

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

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

Test Driven Approach

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

Consistency

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

Massive Community Support

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

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

Other Programming Options for The Deep Learning

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

• JavaScript

• Swift

• Ruby

• R.

• C

•  C++

• Julia

• PHP

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

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

Hello pupils! Welcome to the following lecture on deep learning. As we move forward, we are learning about many of the latest and trendiest tools and techniques, and this course is becoming more interesting. In the previous lecture, you saw some important frameworks in deep learning, and this time, I am here to introduce you to some fantastic algorithms of deep learning that are not only important to understand before going into the practical implementation of the deep learning frameworks but are also interesting to understand the applications of deep learning and related fields. So, get ready to learn the magical algorithms that are making deep learning so effective and cool. Yet before going into details, let me discuss the questions for which we are trying to find answers.

• How does deep learning algorithms are introduced?

• What is the working of deep learning algorithms?

• What are some types of DL algorithms?

• How do deep learning algorithms work?

• How these algorithms are different from each other?

Deep learning plays an important role in the recognition of objects and therefore, people use this feature in image, video and voice recognition where the objects are not only detected but can be changed, removed, edited, or altered using different techniques. The purpose to discuss these algorithms with you is, to have more and more knowledge and practice to choose the perfect algorithm for you and to have the concept of the efficiency and working of each algorithm. Moreover, we will discuss the application to provide you with the idea to make new projects by merging two or more algorithms together or creating your own algorithm.

What is a Deep Learning Algorithm?

Throughout this course, you are learning that with the help of the implementation of deep learning, computers are trained in such a way that they can take human-like decisions and can have the ability to act like humans with the help of their own intelligence. Yet, it is time to learn about how they are doing this and what the core reason is behind the success of these intelligent computers. 

First of all, keep in mind that deep learning is done in different layers, and these layers are run with the help of the algorithm. We introduce the deep learning algorithm as:

“Deep learning algorithms are the set of instructions that are designed dynamically to run on the several layers of neural networks (depending upon the complexity of the neural networks) and are responsible for running all the data on the pre-trained decision-making neural networks.”

One must know that, usually, in machine learning, there is tough training to work with complex datasets that have hundreds of columns or features. This becomes difficult with the classic deep learning algorithm, so the developers are constantly designing a more powerful algorithm with the help of experimentation and research.

How Does Deep Learning Algorithm Work?

When people are using different types of neural networks with the help of deep learning, they have to learn several algorithms to understand the working of each layer of the algorithm. Basically, these algorithms depend upon the ANNs (artificial neural networks) that follow the principles of human brains to train the network.

While the training of the neural network is carried out, these algorithms take the unknown data as input and use it for the following purposes:

• To group the objects

• To extract the required features

• To find out the usage patterns of data

The basic purpose of these algorithms is to build different types of models. There are several algorithms for neural networks, and it is considered that no algorithm is perfect for all types of tasks. All of them have their own pros and cons, and to have mastery over the deep learning algorithm, you have to study more and more and test several algorithms in different ways. 

Types of Deep Learning Algorithms

Do you remember that in the previous lectures we discussed the types of deep learning networks? Now you will observe that, while discussing the deep learning algorithms, you will utilize your concepts of neural networks. With the advancement of deep learning concepts, several algorithms are being introduced every year. So, have a look at the list of algorithms.

1. Convolutional Neural Networks (CNNs)

2. Long Short-Term Memory Networks (LSTMs)

3. Deep Belief Networks (DBNs)

4. Generative Adversarial Networks (GANs)

5. Autoencoders

6. Radial Basis Function Networks (RBFNs)

7. Multilayer Perceptrons (MLPs)

8. Restricted Boltzmann Machines( RBMs)

9. Recurrent Neural Networks (RNNs)

10. Self-Organizing Maps (SOMs)

Do not worry because we are not going to discuss all of them at a time but will discuss only the important ones to give you an overview of the networks.

Convolutional Neural Networks (CNNs)

Convolutional neural networks are also known as "ConvNets," and the main applications of these networks are in image processing and related fields. If we look back at its history, we find that it was first introduced in 1998. Yan LeCun initially referred to it as LeNet. At that time, it was introduced to recognize ZIP codes and other such characters.

Layers in CNN

We know that neural networks have many layers, and similar is the case with CNN. We observe different layers in this type of network, and these are described below:

As a reminder, you must know that neural networks have many layers, and the output of one layer becomes the input for the next layer. In this way, we get refined and better results in every layer. 

Long Short-Term Memory Networks (LSTMs)

This is a type of RNN (recurrent neural network) with a good memory that is used by experts to remember long-term dependencies. By default, it has the ability to recall past information over a long period of time. Because of this ability, LSTMs are used in time series prediction. It is not a single layer but a combination of four layers that communicate with each other in a unique way. Some very typical uses of LSTM are given below:

• Speech recognition

• Development in pharmaceutical operations

• Different compositions in music 

Recurrent neural networks (RNNs)

If you are familiar with the fundamentals of programming, you will understand that if we want to repeat a process, loops, or recurrent processes, are the solution. Similarly, the recurrent neural network is the one that forms the directed cycles. The unique thing about it is that the output of the LSTM becomes the input of the RNN. It means these are connected in a sequence, and in this way, the current phase becomes the output of the LSTM.

The main reason why this connection is magical is that you can utilize the feature of memory storage in LSTM and the ability of RNNs to work in a cyclic way. Some uses of RNN are given next:

• Recognition of handwriting

• Time series analysis

• Translation by the machine

• Natural language processing

• captioning the images

Working of RNN

The output of the RNN is obtained by following the equation given next:

If 

output=t-1

Then 

input=1

So at the output t

input=1+1

And this series goes on

Moreover, RNN can be used with any length of the input, but the size of the model does not increase when the input size is increased.

Generative Adversarial Networks (GANs)

Next on the list is the GAN or the generative adversarial network. These are known as “adversarial networks" because they use two networks that compete with each other to generate real-time synthesized data. It is one of the major reasons why we found applications of the generative adversarial network in video, image, and voice generation.

GANs were first described in a paper published in 2014 by Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio. Yann LeCun, Facebook's AI research director, referred to GANs as "the most interesting idea in ML in the last 10 years." This made GANs a popular and interesting neural network. Another reason why I like this network is the fantastic feature of mimicking. You can create music, voice, video, or any related application that is difficult to recognize as being made by a machine. The impressive results are making this network more and more popular every day, but the evil of this network is equal. As with all technologies, people can use them for negative purposes, so check and balance are applied to such techniques. Moreover, GAN can generate realistic images and cartoons with high-quality results and render 3D objects.

Working of GAN

At first, the network learns to distinguish between the generated fake data and sampled data. It happens when fake data is produced and the discriminator learns to recognise if it is real or false. After that, GAN is responsible to send the results to the generator so that it can learn and memorize the results and go for further training.

If it seems a simple and easy task, then think again because the recognition part is a tough job and you have to feed the perfect data in the perfect way so you may have accurate results every time. 

Radial Basis Function Networks (RBFNs)

For the problems in the function approximation, we use an artificial intelligence technique called the radial basis function network. It is somehow a little bit different from the previous ones. These are the types of forward-feed neural networks, and the speed and performance of these networks make them better than the other neural networks. These are highly efficient and have a better learning speed than others, but they require experience and hard work. Another reason to call them better is the presence of only one hidden layer and one radial basis function that is used as an activation function. Keep in mind that the activation function is highly efficient in its approximation of the results.

Working of Radial Basis Function Network

• It takes the data from a training set and measures the similarities in the input. In this way, it classifies the data. 

• In the layer of RBF neurons, the input vector is then fed into the input layer. 

• After finding the weighted sum of the inputs, we obtain the output. Each category or class of data has one node. 

• The difference from the other network is, the neurons contain a gaussian transfer function, and the output is inversely proportional to the distance between the centre of the network and the neuron. 

• In the end, we get the output, which is a combination of both, the input of the radial basis function and the neuron parameters. 

So, it seems that these networks are enough for today. Although there are different types of neural networks as well, as we have said earlier, with the advancement in deep learning, more and more algorithms for the neural networks are being introduced that have their own specifications, yet at this level, we just wanted to give you an idea about the neural networks. At the start of this article, we have seen what deep learning algorithms are and how they are different from other types of algorithms. We have seen the types of neural networks that include CNNs, LSTMNs, RNNs, GANs, and RBFs.