Neural Network or artificial neural network (ANN) are modeled the same as the human brain. The human brain has a mind to think and analyze any task in a particular situation.

But how can a machine think like that? For the aim, a man-made brain was designed is understood as a neural network. The neural network is formed up many perceptrons

Perceptron is a single layer neural network. it’s a binary classifier and a part of supervised learning. an easy model of the biological neuron in a man-made neural network is understood because the perceptron.

The artificial neuron has input and output.

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Representation of perceptron model mathematically.

Representation of perceptron model mathematically.

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Human brain has neurons for passing information, similarly neural network has nodes to perform the same task. Nodes are the mathematical functions.

A neural network is predicated on the structure and function of biological neural networks. A neural network itself changes or learn based on input and output. The information flows through the system affect the structure of the artificial neural network because of its learning and improving the property.

A Neural Network is also defined as:

A computing system made of several simple, highly interconnected processing elements, which process information by its dynamic state response to external inputs.

A neural network can be made with multiple perceptrons. Where there are three layers-

Input layer: Input layers are the real value from the data.
Hidden layer: Hidden layers are between input and output layers where three or more layers are deep network.
Output layer: It is the final estimate of the output.

Types of Artificial Neural Network

Neural Network works an equivalent because the human systema nervosum functions. There are several sorts of neural network. These networks implementation is supported the set of parameter and mathematical process that are required for determining the output.

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Feedforward Neural Network (Artificial Neuron)

FNN is the purest form of ANN in which input and data travel in only one direction. Data flows in an only forward direction; that’s why it is known as the Feedforward Neural Network. The data passes through input nodes and exit from the output nodes. The nodes are not connected cyclically. It doesn’t need to have a hidden layer. In FNN, there doesn’t need to be multiple layers. It may have a single layer also.

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It has a front propagate wave that’s achieved by using a classifying activation function. All other types of neural network use backpropagation, but FNN can’t. In FNN, the sum of the product’s input and weight are calculated, and then it is fed to the output. Technologies like face recognition and computer vision are used FNN.

Redial basis function Neural Network

RBFNN find the space of some extent to the centre and thought of it to figure smoothly. There are two layers within the RBF Neural Network. within the inner layer, the features are combined with the radial basis function. Features provide an output that’s utilized in consideration. Other measures also can be used instead of Euclidean.

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Redial Basis Function

We define a receptor t.
Confronted maps are drawn around the receptor.
For RBF Gaussian Functions are generally used. So we can define the radial distance r=||X-t||.

Redial Function=Φ(r) = exp (- r²/2σ²), where σ > 0

This Neural Network is used in power restoration system. In the present era power grid have increased in size and complexity. It’s both factors increase the risk of major power outages. Power needs to be restored as quickly and reliably as possible after a blackout.

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Multilayer Perceptron

A Multilayer Perceptron has three or more layer. The data that cannot be separated linearly is classified with the assistance of this network. This network is a fully connected network that means every single node is connected with all other nodes that are in the next layer. A Nonlinear Activation Function is used in Multilayer Perceptron. It’s input and output layer nodes are connected as a directed graph. It is a deep learning method so that for training the network it uses backpropagation. It is extensively applied in speech recognition and machine translation technologies.

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Convolutional Neural Network

In image classification and image recognition, a Convolutional Neural Network plays a vital role, or we can say it is the main category for those. Face recognition, object detection, etc., are some areas where CNN are widely used. It is similar to FNN, learn-able weights and biases are available in neurons.

CNN takes an image as input that is classified and process under a certain category such as dog, cat, lion, tiger, etc. As we know, the computer sees an image as pixels and depends on the resolution of the picture. Based on image resolution, it will see h * w * d, where h= height w= width and d= dimension. For example, An RGB image is 6 * 6 * 3 array of the matrix, and the grayscale image is 4 * 4 * 3 array of the pattern.

In CNN, each input image will pass through a sequence of convolution layers along with pooling, fully connected layers, filters (Also known as kernels). And apply Soft-max function to classify an object with probabilistic values 0 and 1.

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Recurrent Neural Network

Recurrent Neural Network is based on prediction. In this neural network, the output of a particular layer is saved and fed back to the input. It will help to predict the outcome of the layer. In Recurrent Neural Network, the first layer is formed in the same way as FNN’s layer, and in the subsequent layer, the recurrent neural network process begins.

Both inputs and outputs are independent of each other, but in some cases, it required to predict the subsequent word of the sentence.

Then it will depend on the previous word of the sentence. RNN is famous for its primary and most important feature, i.e., Hidden State. Hidden State remembers the information about a sequence.

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RNN has a memory to store the result after calculation. RNN uses the same parameters on each input to perform the same task on all the hidden layers or data to produce the output. Unlike other neural networks, RNN parameter complexity is less.

Modular Neural Network

In Modular Neural Network, several different networks are functionally independent. In MNN the task is divided into sub-task and perform by several systems. During the computational process, networks don’t communicate directly with each other. All the interfaces are work independently towards achieving the output. Combined networks are more powerful than flat and unrestricted. Intermediary takes the production of each system, process them to produce the final output.

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Sequence to Sequence Network

It is consisted of two recurrent neural networks. Here, encoder processes the input and decoder processes the output. The encoder and decoder can either use for same or different parameter.

Sequence-to-sequence models are applied in chatbots, machine translation, and question answering systems.

Components of an Artificial Neural Network

Neurons

Neurons are similar to the biological neurons. Neurons are nothing but the activation function. Artificial neurons or Activation function has a “switch on” characteristic when it performs the classification task. We can say when the input is higher than a specific value; the output should change state, i.e., 0 to 1, -1 to 1, etc. The sigmoid function is commonly used activation function in Artificial Neural Network.

F (Z) = 1/1+EXP (-Z)

Nodes

The biological neuron is connected in hierarchical networks, with the output of some neurons being the input to others. These networks are represented as a connected layer of nodes. Each node takes multiple weighted inputs and applies to the neuron to the summation of these inputs and generates an output.

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Bias

In the neural network, we predict the output (y) based on the given input (x). We create a model, i.e. (mx + c), which help us to predict the output. When we train the model, it finds the appropriate value of the constants m and c itself.

The constant c is the bias. Bias helps a model in such a manner that it can fit best for the given data. We can say bias gives freedom to perform best.

Algorithm

Algorithms are required in the neural network. Biological neurons have self-understanding and working capability, but how an artificial neuron will work in the same way? For this, it is necessary to train our artificial neuron network. For this purpose, there are lots of algorithms used. Each algorithm has a different way of working.

There are five algorithms which are used in training of our ANN

Gradient Descent
Newton’s Method
Conjugate Gradient
Quasi Newton’s
Levenberg Marquardt

Gradient Descent

Gradient descent algorithm is also known as the steepest descent algorithm. It is the most straightforward algorithm which requires information from the gradient vector. GD algorithm is a first-order method.

For simplicity we denote ƒ(w⁽ⁱ⁾)=ƒ⁽ⁱ⁾ and ∇ƒ(w⁽ⁱ⁾)=g⁽ⁱ⁾. The method starts from w⁽⁰⁾ and moves from w⁽ⁱ⁾ to w⁽ⁱ⁺¹⁾ in the training direction d⁽ⁱ⁾=-g⁽ⁱ⁾ until a stopping criterion is satisfied.

So the gradient descent method iterates in the following way.

w⁽ⁱ⁺¹⁾ = w⁽ⁱ⁾ – g⁽ⁱ⁾n⁽ⁱ⁾.

Newton’s Method

Newton’s method is a second-order algorithm. It makes use of the Hessian matrix. Its main task is to find better training directions by using the second derivatives of the loss function.

Newton’s method iterates as follows.

w⁽ⁱ⁺¹⁾ = w⁽ⁱ⁾ – H^(i)-1.g⁽ⁱ⁾For i = 0, 1…..

Here, H^(i)-1.g⁽ⁱ⁾ is known as Newton’s step. The change for parameters may move toward a maximum rather than a minimum. Below is the diagram of the training of a neural network with Newton’s method. The improvement of the parameter is made by obtaining the training direction and a suitable training rate.

Conjugate gradient

Conjugate gradient works in between gradient descent and Newton’s method. Conjugate gradient avoids the information requirements associated with evaluation, inversion of the Hessian matrix, and storage as required by Newton’s method.

In the CG algorithm, searching is done in a conjugate direction, which gives faster convergence rather than gradient descent direction. The training is done in a conjugate direction concerning the Hessian matrix. The improvement of the parameter is made by computing the conjugate training direction and then suitable training rate in that direction.

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Quasi-Newton Method

Applications of Newton’s method are costly in terms of computation. To evaluate the Hessian matrix, it requires many operations to do. For resolving this drawback, Quasi-Newton Method developed. It is also known as variable matric method. At each iteration of an algorithm, it builds up an approximation to the inverse hessian rather than calculating the hessian directly. Information on the first derivative of the loss function is used to compute approximation.

The improvement of the parameter is made by obtaining a Quasi-Newton training direction and then finds a satisfactory training rate.

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Levenberg Marquardt

Levenberg Marquardt is also known as a damped least-squares method. This algorithm is designed to work with loss function specifically. This algorithm does not compute the Hessian matrix. It works with the Jacobian matrix and the gradient vector.

In Levenberg Marquardt, the First step is to find the loss, the gradient, and the Hessian approximation, and then the dumpling parameter is adjusted.

Advantages and Disadvantages of Artificial Neural Network

Advantages of ANN

It stored the information on the entire network rather than the database.
After the training of ANN, the data may give the result even with incomplete information.
If one or more cell is corrupt of ANN, it does not prevent ANN to generate output.
ANN has distributed memory that helps to generate the desired output.
ANN can make a machine learnable.
ANN has a parallel processing capability, which means it can perform more than one task at the same time.

Disadvantages of ANN

It requires a processor with parallel processing power according to their structure.
Unexplained behavior of the network is the main problem of ANN. ANN doesn’t give a clue when it produces a probing solution.
For the determination of the structure of ANN, no specific rules are providing.
There is no information about the duration of the network.
It’s too typical to show the problem to the network.

So, this brings us to the end of blog. This Tecklearn ‘Artificial Neural Network in Tensor Flow’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Tecklearn’s Artificial Intelligence and Deep Learning with Tensor Flow course is curated by industry professionals as per the industry requirements & demands and aligned with the latest best practices. You’ll master convolutional neural networks (CNN), TensorFlow, TensorFlow code, transfer learning, graph visualization, recurrent neural networks (RNN), Deep Learning libraries, GPU in Deep Learning, Keras and TFLearn APIs, backpropagation, and hyperparameters via hands-on projects. The trainee will learn AI by mastering natural language processing, deep neural networks, predictive analytics, reinforcement learning, and more programming languages needed to shine in this field.

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What you will Learn in this Course?

Introduction to Deep Learning and AI

What is Deep Learning?
Advantage of Deep Learning over Machine learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

Environment Set Up and Essentials

Installation
Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
Numpy for Mathematical Computing

More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
Neural Network Equation
Sigmoid Function
Multi-layered perception
Weights, Biases
Activation Functions
Gradient Decent or Error function
Epoch, Forward & backword propagation
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables

Multi-layered Neural Networks

Error Back propagation issues
Drop outs

Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
SkImage
PIL

Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
Predicting Algorithm

Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN

RNN: Recurrent Neural Networks

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Application of Deep Learning in image recognition, NLP and more

Real world projects in recommender systems and others

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