AD-601-Deep Learning

AD-601Deep Learning

Unit-I: Introduction to Deep Learning 

Introduction to Deep Learning: Basics: Biological Neuron, Idea of computational units, McCulloch–Pitts Neural Model, Linear Perceptron, Perceptron Learning, Feed Forward and Back Propagation Networks.

Unit-II: Feedforward Networks

Feedforward Networks: Multilayer Perceptron, Gradient Descent, Backpropagation, Empirical Risk Minimization, regularization, auto encoders.

Unit-III: Convolutional Networks

Convolutional Networks: The Convolution Operation, Variants of the Basic Convolution Function, Structured Outputs, Efficient Convolution Algorithms, Random or Unsupervised Features, LeNet, AlexNet

Unit-IV: Recurrent Neural Networks

Recurrent Neural Networks: Bidirectional RNNs, Deep Recurrent Networks Recursive Neural Networks, The Long Short-Term Memory and Other Gated RNNs

Unit-V: Deep Generative Models

Deep Generative Models: Boltzmann Machines, Restricted Boltzmann Machines, Introduction to MCMC and Gibbs Sampling, Gradient computations in RBMs, Deep Belief Networks, Deep Boltzmann Machines

APPLICATIONS

Image Processing, Speech Recognition, Natural Language Processing

REFERENCES

1. Ian Goodfellow, YoshuaBengio, Aaron Courville, “Deep Learning”, MIT Press, 2016.

2. Francois Chollet, “Deep Learning with Python”, Manning; Second Edition, 2021.

Notes

AD-601_ Deep_Learning_UNIT-1 

• Introduction to Deep learning 

AD-601_ Deep_Learning_UNIT-2

AD-601_ Deep_Learning_UNIT-3

AD-601_ Deep_Learning_UNIT-4

AD-601_ Deep_Learning_UNIT-5

Assignment

• AD-601 _Assignment 1 (Unit-1) Deadline: 17/04/2026 

• AD-601 _Assignment 2 (Unit-2) Deadline: 18/04/2026

• AD-601 _Assignment 3 (Unit-3) Deadline: 20/04/2026 

• Numerical Problem on FFNN with Backpropagation.

RGPV OLD PAPER

• ad-601-deep-learning-jun-2025.pdf

• ad-601-deep-learning-may-2024.pdf

• ad-601-deep-learning-may-2023.pdf 

LAB Experiments-

1. Write a Program to implement Linear Perceptron.

2. Write a Program to implement Multi-Layer Perceptron.

3. Write a Program to implement Autoencoders.

4. Write a Program to implement basic Convolutional Neural Network for Image Classification.

5. Write a Program to implement LeNet for image classification

6. Write a Program to implement AlexNet for image classification

7. Write a Program to implement RNN for text classification

8. Write a Program to implement LSTM for text prediction.

9. Write a Program to implementBoltzmann Machines for any real world classification problem.

10. Write a Program to implement restricted Boltzmann Machines for any real world classification problem.

I

mpotant link :     https://playground.tensorflow.org