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Introduction to Deep Learning | Artificial Neural Network for Beginners

A brief introduction to deep learning and neural networks in artificial intelligence for beginners. Neural networks form the base of deep learning a sub-field of machine learning where the algorithms are inspired by the structure of the human brain. Neural networks take in data train themselves to recognize the patterns in this data, and then predict the outputs for a new set of similar data.

What is Deep Learning?

Here the Deep Learning models can focus on the right features by themselves, requiring little guidance from the programmer. These models also partially solve the dimensionality problem. So, with the help of little guidance on what these deep learning models can do? They can generate the features on which the outcome will depend, and at the same time, it solves the dimensionality problem as well if you have a very large number of inputs and outputs. You can make use of the deep earning algorithm. Now, what exactly is deep learning again? Note that Machine Learning has evolved, and machine learning is nothing but a subset of artificial intelligence. Machine learning is nothing but a substance of artificial intelligence. The idea behind artificial intelligence is imitating human behavior.

Introduction to Deep Learning | Artificial Neural Network

Why Deep Learning?

1.    Huge Amount of Data:

The major limitation of machine learning is it can’t deal with a huge amount of data. If you have a small amount of data, then the best option is machine learning because deep learning is considered an idea for a huge amount of data. The data can be any type, structured or unstructured.

2.    Complex Problems:  

These are simple real-world problems, which are very confusing, complex, and time-consuming, and machine learning can’t handle them.

3.    Feature Extraction:

Feature extraction in terms of machine learning:

You have to train your model to predict a particular object. So, you have to feed all the relevant features of that particular object manually, and then based on those features, the model can predict that particular object.

Feature extraction in terms of deep learning:

In deep learning, you have to give an image of the object to that deep learning algorithm (Model); there is no need to feed the object’s features manually because deep learning also generates all features by itself through feature learning. It generates high-order features to identify or predict this respected object.

Where this particular profound learning concept can apply?

Medical field:

To detect tumor cells’ growth and area of coverage.


As robots can perform human tasks, their ability to detect objects from the nearby environment is possible with the help of deep learning.

Self-driving cars:

When we apply deep learning to cars so it can analyze the environment, traffic lights, roads, buildings, and other vehicles. So, in this way, it can distinguish and identify all the objects, and according to this data, it can drive a car in that respective manner.


We can translate our content from one language to any language by using a deep learning algorithm.

Deep Learning is implemented through Neural Networks. The motivation behind the neural networks is biological neurons; these biological neurons are nothing but your brain cells.

Here is the diagram of neurons and neural networks:

Introduction to Deep Learning | Artificial Neural Network


So, we have dendrites here that are used to provide to our neurons; as you can see, we have multiple dendrites here. So, these many inputs will be provided to earn your own now. As you see in the image, there is a cell body, and in the cell body, there is a nucleus that performs some functions. After that, the output will travel through Exxon and go towards Exxon on terminals. And then, this neuron will fire this output toward the next neuron. The studies tell us the following year, or you can see the two neurons are never connected to each other because there is a gap between them.

Artificial Neural Network Working

Similar to neurons, we have multiple inputs now. These inputs will be provided to a processing element like a cell body. Over here, the processing element that will happen is a summation of your inputs and waits now until it moves on then, or the input will be multiplied with our waits. In the beginning, these waits are randomly assigned.

X1 multiplied by W1 will go toward the processing elements; similarly, X2 and W2 will go toward the processing elements, and so on. And then, summation will happen, generating a function of s that is f of an; after that comes the concept of the activation function.

The activation function is nothing but an order to provide a threshold, so if your output is above the threshold, then only this neuron will fire. Otherwise, it would not fire.

So, you can use a step as an activation function or a sigmoid function as your activation function. So, a network will have multiple neurons connected to each other and form an artificial neural network. And this activation function can be a sigmoid function or a step function that totally depends on your requirements. 

When this exceeds the threshold, it will be fired, and after that, I will check the output. If the output is not equal to the desired output, we compare the actual output with the desired output, And based on that difference, we update our values of waits, and this process will repeat until we get the desired output.

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