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Introduction to Artificial Neural Networks(ANN)

Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound.

Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

It is composed of large number of highly interconnected processing elements(neurons) working in unison to solve a specific problem.” Topics to cover: Biological Neurons (also called nerve cells) or simply neurons are the fundamental units of the brain and nervous system, the cells responsible for receiving sensory input from the external world via dendrites, process it and gives the output through Axons.

Cell body (Soma): The body of the neuron cell contains the nucleus and carries out biochemical transformation necessary to the life of neurons.

The soma processes these incoming signals over time and converts that processed value into an output, which is sent out to other neurons through the axon and the synapses.

In the above figure, for one single observation, x0, x1, x2, x3...x(n) represents various inputs(independent variables) to the network.

In the simplest case, these products are summed, fed to a transfer function (activation function) to generate a result, and this result is sent as output.

xn.wn = ∑ xi.wi Now activation function is applied 𝜙(∑ xi.wi) The Activation function is important for an ANN to learn and make sense of something really complicated.

If the input value is above or below a certain threshold, the neuron is activated and sends exactly the same signal to the next layer.

The problem with this function is for creating a binary classifier ( 1 or 0), but if you want multiple such neurons to be connected to bring in more classes, Class1, Class2, Class3, etc.

Sigmoid Activation Function — (Logistic function)A Sigmoid function is a mathematical function having a characteristic “S”-shaped curve or sigmoid curve which ranges between 0 and 1, therefore it is used for models where we need to predict the probability as an output.

The drawback of the Sigmoid activation function is that it can cause the neural network to get stuck at training time if strong negative input is provided.

The main advantage of this function is that strong negative inputs will be mapped to negative output and only zero-valued inputs are mapped to near-zero outputs.,So less likely to get stuck during training.

Rectified Linear Units — (ReLu)ReLu is the most used activation function in CNN and ANN which ranges from zero to infinity.[0,∞) It gives an output ‘x’ if x is positive and 0 otherwise.

This is simple enough but there is a way to amplify the power of the Neural Network and increase its accuracy by the addition of a hidden layer that sits between the input and output layers.

This way the neurons work and interact in a very flexible way allowing it to look for specific things and therefore make a comprehensive search for whatever it is trained for.

Learning in a neural network is closely related to how we learn in our regular lives and activities — we perform an action and are either accepted or corrected by a trainer or coach to understand how to get better at a certain task.

Based on the difference between the actual value and the predicted value, an error value also called Cost Function is computed and sent back through the system.

It is a first-order iterative optimization algorithm and its responsibility is to find the minimum cost value(loss) in the process of training the model with different weights or updating weights.

In Gradient Descent, instead of going through every weight one at a time, and ticking every wrong weight off as you go, we instead look at the angle of the function line.

If slope → Negative, that means yo go down the curve.If slope → Positive, Do nothing This way a vast number of incorrect weights are eliminated.

One thing to be noted is that, as SGD is generally noisier than typical Gradient Descent, it usually took a higher number of iterations to reach the minima, because of its randomness in its descent.

Step-6 → Repeat step-1 to 5 and update the weights after each observation(Reinforcement Learning) Step-7 → When the whole training set passed through the ANN, that makes and epoch.

They may be used for a variety of different concepts and ideas, and learn through a specific mechanism of backpropagation and error correction during the testing phase.

By properly minimizing the error, these multi-layered systems may be able to one day learn and conceptualize ideas alone, without human correction.

Artificial Neural Networks

ANN was developed considering the same as of our brain, same how our brain works was taken into account.

Each input value is associated with its weight, which passes on to next level, each perceptron will have an activation function.

Forward propagation :  The factor 1 keeps moving forward and gets activated in the next level, at the nodes the activation value like sigma, return h, based on that, values like 0, 1 or 2 is passed on to next level.

Cost function : Sometimes the algorithm we create might predict the value incorrectly, so we need cost function.

It calculates how well the neural network is performing based on the actual vs predicted value.

Back Propagation : When we feel that outputs are not correct, we back propagate the values to adjust the weights to produce the right output.

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