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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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Suppose you want to classify between Dog, Cat, Parrot and Lion.
At first we have to create a main folder. We will create the dataset inside this folder.
Now we have to create four folders (for store dog, cat, parrot and lion images separately) inside this main folder.
Now download dog images and store in a folder which you have create inside main folder. Similarly download
cat, parrot and lion images and put inside remaining folders. One folder must contain only one type of images.
like on folder contain dog images, one contain lion.
By doing this the dataset is created