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Deep Learning with Python: Starter Guide

Deep Learning with Python: Starter Guide

Introduction

In 2020, many pages have been turned in the book of technological advancements but Deep Learning has been, by far, the most indispensable and hottest topic. Many Data Scientists and Analysts believe that such advancements in the programs of computers or machines may replace humans in the future. There is a reason behind making such a statement and this is what we are going to disclose in this guide.

By the end of this guide, you will develop a better understanding of Deep Learning and how Python can be utilized for such learning. You will also understand the need of acquiring Python Certification once you know all the stakes. We will cover some major topics in this guide which are mentioned stated as follows:

  • What are Data Science and its components?
  • Why is there a need for Deep Learning?
  • What is meant by Deep Learning?
  • Artificial Neural Network and Perceptron
  • What are some day-to-day applications of Deep Learning?
  • Why Should You Choose Python for Deep Learning?
  • Understand Deep Learning with Python by Crafting a Deep Neural Network
  • Understand Deep Learning with Python with the Help of Perceptron Example

What is Data Science and its Components?

Data Science has been an essential part of our lives for a long time and it is quite beneficial to know about Data Science to build firm grounds. Data Science is the extraction of important information from the hidden patterns that are buried deep into the unstructured data. This is primarily done by using various algorithms, techniques, methods, tools, formulae, procedures, and numerous other sets of rules. There are three components of Data Science currently known to the entire world which are:

  1. Artificial Intelligence

Artificial Intelligence came into power with the idea that one day, machines will be intelligent enough to make their decisions. This will be done by mimicking the behavior of human beings to store all that information for later use. In the previous years, it was thought that all the programs of the computer will never be able to match the capacity of that of humans.

But the main setback was the lack of data along with the computational power to cover this aspect of machines and computers. In today's competitive world, with so much data at hand, Artificial Intelligence has certainly become the buzzword in the entire IT industry.

  1. Machine Learning

Machine Learning is considered to be a subset of Artificial Intelligence which is used to improve efficiency along with the performance of the machines. Machine Learning uses various statistical methods as well as experience to improve the programs of computers and several other machines.

  1. Deep Learning

Like Machine Learning is the subset of Artificial Intelligence, Deep learning is considered to be the subset of Machine Learning. The prime function of Deep Learning is to enhance the feasibility of the multi-layer Neural Network. Such Neural Networks are crafted because these networks mimic the decision-making process of human beings.

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Why is there a Need for Deep Learning?

Artificial Intelligence penetrated our lives with the intelligence of the machines and when it got coupled with the algorithms of Machine Learning, it gave a new meaning to the IT industry. Both of these components were primarily based on the fact that these machines should be left with large sums of data. In this way, they will explore and learn how to accumulate essential information from Big Data.

Thus, extracting vital information from the stored data wasn't a problem for analysts and scientists. So, what stimulated the growth of Deep Learning and why is it a dire need at this time? This question can be answered by focusing on the two most important facts regarding Deep Learning.

  1. Deep Learning Handles High-Dimensional Data

A large number of data gets collected each day and due to that, most of the algorithms of the Machine Learning can't handle that much of data, particularly High-Dimensional Data. This is the kind of data that is produced as the result of numerous values of input and output. In this way, one has to handle thousands of dimensions which can prove to be resource-exhausting and complex. This is where Deep Learning jumps in and shows off its capabilities of handling such kind of data.

  1. Deep Learning Focuses on the Appropriate Features

Another problem that gave rise to Deep Learning was that this technology focused on the most appropriate features of the extracted information. Before this, the programmer had to face a lot of difficulty in the extraction of such features.

These features hold immense importance to achieve better accuracy along with predicting the future outcomes of the extracted data. The role of the programmers is that they choose the algorithms to extract such features but only Machine Learning is not capable of providing such algorithms. Deep Learning provides such algorithms and systems which focus on the right features to use for future purposes.

What is meant by Deep Learning?

We have already covered this part but now, we're going into the depth of Deep Learning. As already mentioned, it is a subset of Machine Learning and it uses all the methods, techniques, algorithms, and tools to train some networks called Neural Networks. This happens when Machine Learning is not working the way it is supposed to be and thus, Deep Learning enters into the picture. To sum everything up, Deep Learning basically simulates the functions of our brain, particularly the one where everything is learned from experience.

Almost all of us know that our brain consists of billions of neurons that react according to bodily reflexes. These neurons allow us to perform a large number of exemplary functions that are now being taken up by Deep Learning. For instance, take an example of a 12-year-old kid's brain whose cerebral capacity helps to solve a large number of difficult problems that can be termed as a super-computer. This is where we will begin to understand the meaning and the working of Artificial Neural Network and Perceptron.

Artificial Neural Network and Perceptron

Deep Learning mirrors the working of the basic unit of that of the human brain and that is a neuron or a brain cell. Before understanding the artificial neural network and perceptron, we need to grasp the meaning of the biological neurons which consists of three main components which are:

  • Axon: Various signals are transmitted to other cells by this component.
  • Dendrite: Various signals are received from other cells by this component.
  • Cell Body: All the inputs are summed up by this component of the neuron.

Now, let us get back at the top at hand and talk about the artificial neural networks. These artificial neural networks are primarily used for binary classification and they are given various inputs. Each of these inputs consists of its own specific weight and the functions are performed according to the associated weights and generate outputs. The two major functions of these artificial neural networks and perceptron are stated as follows:

  • It sums up all the inputs.
  • It transforms all those inputs into outputs.

The weights associated with the neural networks hold immense importance because these weights give off the impact that they have on the neural networks. As the weight of the input increases, the impact of such input on the network increases. Another most important parameter of the artificial neuron is Bias which can be referred to as an adjusting parameter. It is so named because it adjusts the weighted inputs and their sums along with the outputs in such a way that is best suited for the entire operation.

Another concept to understand is that of Activated Functions which are used to translate a set of inputs into their belonging outputs. These functions get the job done by using a large number of thresholds for crafting such outputs. A lot of functions can be used as Activated Functions, however, only some of them are worth grabbing your attention and these are:

  • ReLU
  • Logistic or Sigmoid
  • Identity or Linear
  • Softmax
  • Tanh
  • Binary Step or Unit

Perceptron may perform a large number of functions but there are also a couple of setbacks associated with it which are:

  1. Single-layered perceptron is not able to classify those data points which are non-linearly separable.
  2. Single-layered perceptron is unable to solve complex problems such as those which contain a large number of parameters.

That is why a large number of neurons are used to solve this problem of complexity and acquire the desired results. When we talk about the neural networks, we see that it is basically a composition of different perceptron which is connected in a lot of different ways to form a network. To look deeper into the neural network, we are going to discuss the different kinds of layers which are used to make up this network.

  • Input Layer: All the outside information is collected by the input nodes and when put together, this forms an inner layer.
  • Hidden Layer: As the name indicates, this layer is used to transfer vital information collected by the input nodes to that of the output nodes to generate fruitful results. This transfer is made through several hidden notes which are collectively called the hidden layer.
  • Output Layer: All the information collected by the input nodes is computed, transformed, and transferred to the outside world with the help of numerous output nodes. These nodes are collectively known as the outer layer of the perceptron.

Now, we have a better understanding of the artificial neural networks, its composition, and the different layers associated with it. Therefore, we can move on with our guide and know more about Deep Learning with Python and why should you enroll in our Python Certification.

What are some day-to-day Applications of Deep Learning?

Deep Learning has been a crucial part of our lives and it has been benefiting the universe for ages. That is why you need to know and understand a few important applications of Deep Learning which are mentioned below.

  • Recognition of Speech
  • Automatic Tagging and Facial Recognition
  • Self-driving Car
  • Machine Translation
  • Virtual Personal Assistants
  • Chatbots

Why Should You Choose Python for Deep Learning?

If there wasn't any programming language, to begin with, there won't be a whole IT industry that is running the entire world. Learning programming languages provides the basis to erect a whole building of technological information, methods, techniques, and tools. There are a large number of programming languages currently known but Python is the programming language that is most used and understood because it is a general-purpose language. Python offers a multitude of benefits when it comes to quantitative and analytical computing, however, the most leading ones are mentioned below.

  • The contents and methods of Python are highly easy to understand and use.
  • This programming language has the capability of typing dynamically.
  • It converges enormous support from the entire community.
  • It contains a large number of libraries such as Numpy, Matplotlib, Scikit-Learn, Seaborn, and Pandas. These libraries can be used for various purposes depending upon the data collected by the servers and the systems.

Due to all of the aforementioned reasons, Python comes at the top of the list to be used for Deep Learning because this language is certainly best for computing purposes.

Understand Deep Learning with Python by Crafting a Deep Neural Network 

We are done with the theory part of our guide and it's time where we spice up this guide with some exciting examples. These examples will be enough to make you understand the working of the Python for Deep Learning. In the first example, we are going to craft a Deep Neural Network in which you will have to make all the layers from the scratch.

In this example, we are going to work with an efficient dataset known as the MNIST Dataset. This data set contains various samples of digit images, all of which are handwritten, and these samples are about 10,000 testing samples along with 60,000 samples of training. The digit images present in this dataset are of 28x28 pixels whereas the output of this dataset lies between 0-9. The main task of this part is to create that model which can help to identify a specific digit present on a specific image/images.

The things to consider before starting to craft a neural network is that you must keep the Future Statements at the top of the file. These statements can make or break your game because they are capable of changing the most fundamental things about your language. Thus, you should have proper knowledge that where do these statements lie from the beginning. Furthermore, you need to import a print function from Python 3 to Python 2.6+ to proceed with your coding. If you want to know the rest of the coding, get this Python Certification, and see the working of professionals.

Understand Deep Learning with Python with the Help of Perceptron Example

To understand this kind of working, you need to acquire knowledge of OR Gate first. In this kind of gate, for every 1 input, the output generated also comes out to be 1. In this example, the perceptron or the artificial neurons can be used as a decision line or a separator which can help to divide the sets of input in a couple of classes.

  • Class 1 consists of those inputs whose outputs are 0 and they lie below the separation line.
  • Class 2 consists of those inputs whose outputs are 1 and they lie above the separation line.

Now, we are going to mention some of the most indispensable steps that are needed to make an artificial neuron.

  1. You need to import a library from Python and gear it up for usage. You can use only one library or a set of libraries depending upon the number of neurons you need to create.
  2. This step consists of properly defining the variable vectors for input as well as the output.
  3. In the third step, you need to define the variables of weight assigned to each input.
  4. To accept the external inputs which are working, you will need to define some placeholders for input along with the output.
  5. This step consists of some calculation where you will calculate the value of outputs along with the activated functions.
  6. Nothing is free of error, therefore, calculate any error or cost if occurred.
  7. Reduce the error, if any.
  8. Define the variables by initializing them.
  9. Train your perceptron repetitively to minimize loss or error.
  10. Generate output.

Conclusion

If you are thinking of understanding Deep Learning with Python, this guide will help you to achieve that. Deep Learning provides a new face to the IT industry and this is the right time to grasp its concepts. That is why you need to procure Python Certification which is crafted by professionals, Data Scientists, and Analysts. With this guide and certification, you will be able to develop a deeper and better understanding of Deep Learning with Python.   

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