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

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DL (Unit-2)

Get all the topics

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