Neural Network Tutorial in Python

Today we will learn Neural Network Tutorial in advance. After reading this article you should know about Neural Network, Artificial Neural Network, Deep Neural Network, and these types like Convolutional Neural Network, Recurrent Neural Network, Feed Forward Neural Network, Modular Neural Network and many other types of Neural Network. In the Neural Network Tutorial, you can also program the small software which can recognize the handwritten digits.

Introduction of Neural Network

Neural Network is a system or hardware that is designed to operate like a human brain. Which is inspired by the Biological Neurons system. In our Human brain, Billions of neurons are present. Neural Network is also called Artificial Neural Network. Neural Network is used in Speech Recognition, Handwriting Recognition, Text Translate, Image Classification, Solve Travelling Sales Man Problem, Image Compression, and many more.

Neural Network Tutorial

Since 1950’s, Scientists have been trying to mimic the functioning of a neurons and use it to build smarter robots. After a lot of trail and error, Humans finally designed a computer that can recognize human speech.

In the 20th Century, humans were able to give birth to deep learning that was able to see and distinguish between different images and videos.

Deep Learning

In Neural Network Tutorial we should know about Deep Learning. Deep learning is a machine learning technique based on Neural Network that teaches computers to do just like a human. Deep learning can be Supervised Learning, Un-Supervised Learning, Semi-Supervised Learning.

By using neuron methodology. Scientists managed to build an artificial form of it, that power any deep learning-based machine. So, by these techniques, the named comes an Artificial Neural Network.

So, to understand how Neural Network work, we should know about how the real Neural Network works in our body. Neurons are divided into 4 parts.

Neurons ( Neural Network Tutorial ))
  • Dendrite:- Take an Input to a neuron and send it to the cell body for processing.
  • Cell Body:- Information processing happens in the cell body and transfers to the axon.
  • Axon:- Output to the neuron.
  • Synapses − It is the connection between the axon and other neuron dendrites.

Working Process of Neural Network

In Neural Network Tutorial, You must understand about how the Neural Network Work. So far, the Neural Network is divided into 3 layers.

Neural Network layer ( Neural Network Tutorial )
Block Diagram of Neural Network Tutorial

Input Layer :- In this layer, the input data for Neural Network.

Hidden Layer :- In this layer, the all the computation and processing is done for required output.

Output Layer :- In this layer, the result is produced from the given input.

Neural Network first take the input, Process these data and produce the required result.

Neural Network Tutorial

For the above general model of artificial neural network, the net input can be calculated as follows

Neural Network Function

The output can be calculated by applying the activation function over the net input

Neural Network Function

Types of Neural Network

1) Feed Forward Neural Network

It is the simplest form of Artificial Neural Network ( ANN ), Data travels only in one direction from input to output. The main application of Feed Forward Neural Network is Computer vision and Speech Recognition.

2) Recurrent Neural Network

It is a class of Artificial Neural Network in which the hidden layer saves its output to used for further prediction. The main application of Recurrent Neural Network is Text to speech conversion model.

3) Convolutional Neural Network

It is a class of Artificial Neural Network in which the input feature are taken in batches like a filter. This allows the network to remember an image in parts. The main application of Convolutional Neural Network is Image Processing and Signal Processing.

4) Radial Basis Function Neural Network

This Data classifies the data point based on its distance from a center point. The main application of Radial Basis Function Neural Network is Power Restoration Systems.

5) KOHONEN Self Organizing Neural Network

It is a class of Artificial Neural Network in which the vector of random dimensions is input to a discrete map comprised of neurons. Mainly, it is used to recognize the pattern in data like in medical analysis.

6) Modular Neural Network

It is also a class of Artificial Neural Network. It has a collection of different Neural Network together to get the output. Modular Neural Network is under the research till now.

Application Neural Network

i) Speech Recognition

For Speech Recognition Recurrent Neural Network is used. It is used to recognize the speech of any person easily. Example are Okey Google, Alexa, Cortana, Siri, etc.

ii) Handwriting Recognition

Neural Network is used to convert handwritten characters into digital characters that the system can recognize. The text is identified using optical character recognition. An example is google lens.

iii) Text Translate

By using Neural Network the text can translate from one language to another language easily. The technology behind the translator is a sequence to sequence learning. For example Google Translate.

iv) Image Compression

The main idea behind the image compression Neural Network is to store, encrypt and re-create the actual image again.

v) Image Recognition

Image Classification is the way of classifying the photo on their color, behavior, shape. Image recognition happens through Convolutional Neural Network ( CNN ). For example classification of Cat and Dog.

vi) Travelling Salesman Problem

It refers to finding the optimal path to travel between all cities in an area. Neural Network helps to solve this problem providing higher revenue at a minimal cost.

vii) Stock Exchange Predictions

There are many factors that affect the stock market. Neural networks can examine a lot of factors and predict the prices on a daily basis helping the stock brokers.

Advantage of Neural Network

  • Neural Network’s output isn’t limited entirely by inputs and results given to the initially by an export system.
  • Neural Networks have the potential for high fault tolerance.
  • The ability of Neural Network comes handy for robotics and pattern tolerance.
  • Neural networks are capable of debugging or diagnosing a network on their own.
  • Non-linear systems have the capability of finding shortcuts to reach computationally expensive solutions.

Future works of Neural Network

  • More personalized choices for users and customers all over the world.
  • Hyper intelligent virtual assistants will make life easier.
  • New forms of algorithm or learning methods would be discovered.
  • Neural networks will be a lot faster in the future.
  • Neural Networks tools will be embedded in every design surface.
  • Neural Networks will be used in the field of medicine, agriculture, etc.

Video For Basic Neural Network Tutorial

FAQ for Neural Network Tutorial in Python

  1. what is a neural network?

    A Neural Network is a system of hardware or software patterned after the operation of neurons in the human brain. Neural Network is inspired by the neurons in the Human Brain. Neural Network is also called Artificial Neural Network.

  2. what is artificial neural network?

    An Artificial Neural Network is called as Neural Network which is inspired by neurons of human brain. Artificial Neural Network is a system of hardware or software. which is of three layer Input Layer, Hidden Layer, Output Layer.

  3. what is a convolutional neural network?

    A Convolutional Neural Network is a part of the Neural Network. Convolutional Neural Network is also called as CNN or ConvNet’s. By using Convolutional Neural Network we can do image classification, image recognition, face recognition, Object detection, etc.

  4. what are the types of artificial neural network?

    Mainly Artificial Neural Network is of 6 Types:-
    1) Feed Forward Neural Network
    2) Recurrent Neural Network
    3) Convolutional Neural Network
    4) Radial Basis Function Neural Network
    5) KOHONEN Self Organizing Neural Network
    6) Modular Neural Network

  5. what are neural network?

    A Neural Network is a system of software or hardware, which is design is like a human brain. Basically Neural Network is Inspired by neurons of the human brain.

  6. how to create a neural network?

    Neural Network can be created as the following steps:-
    1) Take an Input data.
    2) Process these data.
    3) By using Activation function we can classify the data.
    4) Produce the result.

  7. how to make a neural network in python?

    Neural Network can be created in python as the following steps:-
    1) Take an Input data.
    2) Process these data.
    3) By using Activation function we can classify the data.
    4) Produce the result.

  8. What is a hidden layer in a neural network?

    In Neural Network there are three layer Input Layer, Hidden Layer, Output Layer. The Input layer take an input for Neural Network, In Hidden Layer the input data are compute and process to produce a result as output, and in the output layer the result is produce.

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