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Introduction

Setup

ANN

Working process ANN

Propagation

Bias parameter

Activation function

Loss function

Overfitting and Underfitting

Optimization function

Chain rule

Minima

Gradient problem

Weight initialization

Dropout

ANN Regression Exercise

ANN Classification Exercise

Hyper parameter tuning

CNN

CNN basics

Convolution

Padding

Pooling

Data argumentation

Flattening

Create Custom Dataset

Binary Classification Exercise

Multiclass Classification Exercise

Transfer learning

Transfer model Basic template

RNN

How RNN works

LSTM

Bidirectional RNN

Sequence to sequence

Attention model

Transformer model

Bag of words

Tokenization & Stop words

Stemming & Lemmatization

TF-IDF

N-Gram

Word embedding

Normalization

Pos tagging

Parser

semantic analysis

Regular expression

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What is cnn and how it works?

What is CNN?

Before understanding CNN we have to understand how the brain works. In the backside of the brain, we have a part named Cerebral Cortex and inside this, we have the visual cortex. This visual cortex is responsible for doing various image and video-related work. When we see an image, it passes through our sensory organ means the eye then it passes through various neurons then it reaches the cerebral cortex and then it goes to the visual cortex. In the visual cortex, we have multiple layers. All layers present in the visual cortex plays a very important role. Each layer is responsible for different work for image detection. Suppose one layer is responsible for finding the edges of our image then this information will pass to the second layer and some different work will also be done there, like is the object is moving or not etc, then it will pass to another layer. It means that each layer is performing some work for extracting some information from the image or video frames. We try to do a similar thing to in CNN.

Basic information about images

Images basically mention as pixel-like 360p, 720p , 1080p, 4x4 pixel matrix, 6x6 pixel matrix, etc. Suppose we have a 4x4 pixel matrix image. The pixels value should be ranging between 0-255. It means the pixels value can be 3, 0, 245, 155, etc but must be between 0-255. For color images we use RGB. Here R=red, G=green, B=blue. So here we have three different layers.Here pixel value range is also between 0-255.

So how do we create a color image?
We put color values (between 0 to 225) in each layer. Then we combine those three layers and after that, we get a color image.

How CNN works?

In CNN, we apply a filter on the image. After applying filter, we will get a new filtered image and this process is called convolution.
In consultation, we can use too many filters like edge detection, motion detection, how many faces are there in the image, etc. After convolution we get our outputs. After getting the output, we apply an activation function on each cell value of the filtered or output image and then we try to update it. In ANN we try to update the weight but in CNN we try to update the values of filters. For updates we use optimizers.

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