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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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Deep learning bias parameter
What is bias?
Bias is an additional parameter in the neural network. We use bias to adjust the output with the weighted sum
of the inputs. Bias value permits us to shift the activation function to the right or left, so that model can
fit the data better. Bias acts like a constant and helps the model to fit best for the given data. We can also
understand bias from a linear function.
y=mx+c
In linear equation c works like bias.
What is Variance?
Variance means that all the predicted value which was predicted by our model, how much scatter they are in relation with each other. Low variance means, all the predicted values are in a group. It means the values are not so much scattered. High variance means, all the predicted values are not in a group. It means all the values will be so much scattered.