Convolution layer

This is the main layer of feature extraction part of the convolution neural networks.

Convolution layer

They had a filter of shape (x,x), generally x=3 so shape of filter is (3,3).

Kernel

2	2	1
3	3	3
3	2	1

Image

2	3	5
3	3	3 (22)+(32)+(51)+(33)+(33)+(33)+(33)+(22)+(1*1)=56
3	2	1 Every pixel will change by 56 of image

Comments

MLP( Multi Layer Perceptron )

Multilayer Perceptron Multilayer Perceptron is collection of Perceptron ANN(MULTILAYER PERCEPTRON) Backpropagation Backpropagation is a process to find weights and baises Step 1 : Initialize all weight and biases Step 2:Choose a Loss function Step 3: Apply Gradient Descent Technique to find correct weights and biases

What is Deep Learning?

Machine Learning Machine Learning is the subset of A.I it cannot extracts too hard features but its does not requires too much data Deep Learning Deep Learning is the subset of machine learning it can extracts difficult features but its requires too much data Which one I have to use? It is totally depends on data if you have too much data you can use deep learning else you can use machine learning

Perceptron

Perceptron is a general thing used in deep learning .You know what is neuron in brain Perceptron Layers 1. Input-In Input we provide Input to our model 2. Calculation-we calculate weight and biases for out Perceptron 3.Output-(sigma of wixi)+b Perceptron Architecture

Deep Learning

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