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Demonstration of the mini-lab (practical) component activities conducted for the course of Neural Networks and Deep Learning (19CSE456).

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34 Commits
Lab Evaluations
Lab Evaluations
 
 
Mid-Term Examination
Mid-Term Examination
 
 
Practical 1 - Python Basics
Practical 1 - Python Basics
 
 
Practical 10 - Forward-Backward Algorithm
Practical 10 - Forward-Backward Algorithm
 
 
Practical 11 - Convolutional Neural Network (CNN)
Practical 11 - Convolutional Neural Network (CNN)
 
 
Practical 2 - Perceptron Algorithm for a Binary Classification Problem
Practical 2 - Perceptron Algorithm for a Binary Classification Problem
 
 
Practical 3 - Activation Functions (Linear vs. Non-Linear w.r.t Binary Classification)
Practical 3 - Activation Functions (Linear vs. Non-Linear w.r.t Binary Classification)
 
 
Practical 4 - Implementing a Multi-Layer Perceptron (MLP)
Practical 4 - Implementing a Multi-Layer Perceptron (MLP)
 
 
Practical 5 - Implementing the Backpropagation algorithm to update the weights of an MLP
Practical 5 - Implementing the Backpropagation algorithm to update the weights of an MLP
 
 
Practical 6 - Implementing mini-batch gradient descent
Practical 6 - Implementing mini-batch gradient descent
 
 
Practical 7 - Implementing L1 and L2 regularization techniques
Practical 7 - Implementing L1 and L2 regularization techniques
 
 
Practical 8 - Restricted Boltzmann Machine (RBM)
Practical 8 - Restricted Boltzmann Machine (RBM)
 
 
Practical 9 - Autoencoder for Unsupervised Learning
Practical 9 - Autoencoder for Unsupervised Learning
 
 
Tutorial 1
Tutorial 1
 
 
README.md
README.md
 
 

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Neural-Networks-and-Deep-Learning

Demonstration of the mini-lab (practical) component activities conducted for the course of Neural Networks and Deep Learning (19CSE456). Provides an introduction to deep neural network models, and surveys some of the applications of these models in areas where they have been particularly successful. Covers feedforward networks, convolutional networks, recurrent and recursive networks, as well as general topics such as input encoding and training techniques.

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Demonstration of the mini-lab (practical) component activities conducted for the course of Neural Networks and Deep Learning (19CSE456).

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recurrent-neural-networks restricted-boltzmann-machine convolutional-neural-networks backpropagation-algorithm

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