DL (Unit-2)

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Introduction to neural networks
The Biological Neuron
The Perceptron
Multilayer Feed-Forward Networks
Backpropagation and Forward propagation
Activation Functions: Linear, Sigmoid, Tannh, Hard Tanh, Softmax, Rectified Linear
Loss Functions: Loss Function Notation , Loss Functions for Regression, Loss Functions for Classification, Loss Functions for Reconstruction
Hyperparameters: Learning Rate, Regularization, Momentum, Sparsity
Hidden Units, Cost Functions, Error Backpropagation
Gradient-Based Learning, Implementing Gradient Descent, vanishing and Exploding gradient descent, Sentiment Analysis
Deep Learning with Pytorch, Jupyter, colab