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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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Flattening converts data into a 1-dimensional array so that we can input it to the next layer. We use
flattening on the output of convolution so that we can create a single long feature vector. This single long
feature is connected to the final classification model, which is called a fully connected layer. The facility
of doing this is the pixel which was in the form of a feature map, will be reduced into vector form and it
will become the input of our CNN.
Suppose we have this matrix of image:
| 1 | 2 | 3 |
|---|---|---|
| 4 | 3 | 6 |
| 2 | 8 | 4 |
After the applying the flattening it will looks like:
1
2
3
4
3
6
2
8
4
Here what we did that we have converted each row into one column.