Supervised Learning - Classification Week 9 Challenge

Q1.

The neuron is the smallest unit and the building block of a neural network. A neuron takes a set of inputs, performs some mathematical computations, and gives an output.

The inputs and outputs are numbers, either positive or negative. In this example, the neuron takes two inputs. However, there is no limit to the number of inputs a neuron can take.

The first computation that a neuron performs is the weighted sum. It multiplies each input by its corresponding weight. Then all the inputs are summed and a term called bias is added.

Input Layer The input layer takes raw input from the domain. No computation is performed at this layer. Nodes here just pass on the information (features) to the hidden layer.

Hidden Layer this layer are not exposed. They provide an abstraction to the neural network.

The hidden layer performs all kinds of computation on the features entered through the input layer and transfers the result to the output layer.

Output Layer It’s the final layer of the network that brings the information learned through the hidden layer and delivers the final value as a result.

Q2.

Deep learning is a specialized form of machine learning. The main difference between deep learning and machine learning processes is how features are extracted.

Deep learning is a subset of ML that determines target features automatically, This speeds up results as the algorithm can find and select features faster than a human can.

DL also increases accuracy because the algorithm can detect all features rather than just those recognizable to the human eye.

When the amounth of data is increased, machine learning techniques are insufficient in terms of performance and deep learning gives better performance like accuracy.

Q3

Activation functions also have a major effect on the neural network ability to converge and the convergence speed, or in some cases, activation functions might prevent neural networks from converging in the first place. Activation function also helps to normalize the output of any input in the range between 1 to -1 or 0 to 1.

Y = Activation function(∑ (weights*input + bias))

So the activation function is an important part of an artificial neural network.

They decide whether a neuron should be activated or not and it is a non-linear transformation that can be done on the input before sending it to the next layer of neurons or finalizing the output.

Q4'

relu:-

The ReLU function is the Rectified linear unit. It is the most widely used activation function. It is defined as-
f(x)=max(0,x)

First things first, the ReLU function is non linear, which means we can easily backpropagate the errors and have multiple layers of neurons being activated by the ReLU function.

softmax:-

The softmax function is also a type of sigmoid function but is handy when we are trying to handle classification problems. The sigmoid function as we saw earlier was able to handle just two classes. What shall we do when we are trying to handle multiple classes.

[1.2 , 0.8 , 0.65], When we apply the softmax function

we would get [0.42 , 0.31, 0.27]. So now we can use these as probabilities for the value to be in each class.

main is Sigmoid :-

Sigmoid is a widely used activation function. It is of the form-

f(x)=1/(1+e^-x)

This is a smooth function and is continuously differentiable. The biggest advantage that it has over step and linear function is that it is non-linear.

tanh:-

The tanh function is very similar to the sigmoid function. It is actually just a scaled version of the sigmoid function.
tanh(x)=2sigmoid(2x)-1

It can be directly written as –
tanh(x)=2/(1+e^(-2x)) -1

Tanh works similar to the sigmoid function but is symmetric over the origin. it ranges from -1 to 1.

Q5