B.E. (Computer Engineering) Deep Learning Syllabus - Mumbai University 2026

The University has moved this degree onto NEP 2020 one year at a time. The first and second years are NEP 2020 syllabi; the third and fourth years are still examined on the REV-2019 'C' Scheme, which is what the University sets for them this year.

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Syllabus for Deep Learning

Semester 8 · Fourth Year CE

Module 1: Fundamentals of Neural Network

1.1 Biological neuron, Mc-Culloch Pitts Neuron, Perceptron, Perceptron Learning, Delta learning, Multilayer Perceptron: Linearly separable, linearly non-separable classes
1.2 Deep Networks: Fundamentals, Brief History, Three Classes of Deep Learning Basic Terminologies of Deep Learning

Module 2: Training, Optimization and Regularization of Deep Neural Network

2.1 Training Feedforward DNN Multi Layered Feed Forward Neural Network, Learning Factors, Activation functions: Tanh, Logistic, Linear, Softmax, ReLU, Leaky ReLU, Loss functions: Squared Error loss, Cross Entropy, Choosing output function and loss function
2.2 Optimization Learning with backpropagation, Learning Parameters: Gradient Descent (GD), Stochastic and Mini Batch GD, Momentum Based GD, Nesterov Accelerated GD, AdaGrad, Adam, RMSProp
2.3 Regularization Overview of Overfitting, Types of biases, Bias Variance Tradeoff Regularization Methods: L1, L2 regularization, Parameter sharing, Dropout, Weight Decay, Batch normalization, Early stopping, Data Augmentation, Adding noise to input and output

Module 3: Autoencoders: Unsupervised Learning

3.1 Introduction, Linear Autoencoder, Undercomplete Autoencoder, Overcomplete Autoencoders, Regularization in Autoencoders
3.2 Denoising Autoencoders, Sparse Autoencoders, Contractive Autoencoders
3.3 Application of Autoencoders: Image Compression

Module 4: Convolutional Neural Networks (CNN): Supervised Learning

4.1 Convolution operation, Padding, Stride, Relation between input, output and filter size, CNN architecture: Convolution layer, Pooling Layer, Weight Sharing in CNN, Fully Connected NN vs CNN, Variants of basic Convolution function
4.2 Modern Deep Learning Architectures: LeNET: Architecture, AlexNET: Architecture

Module 5: Recurrent Neural Networks (RNN)

5.1 Sequence Learning Problem, Unfolding Computational graphs, Recurrent Neural Network, Bidirectional RNN, Backpropagation Through Time (BTT), Vanishing and Exploding Gradients, Truncated BTT
5.2 Long Short Term Memory: Selective Read, Selective write, Selective Forget, Gated Recurrent Unit

Module 6: Recent Trends and Applications

6.1 Generative Adversarial Network (GAN): Architecture
6.2 Applications: Image Generation, DeepFake

Useful Links

1 Ian Goodfellow, Yoshua Bengio, Aaron Courville. “Deep Learning”, MIT Press Ltd, 2016
2 Li Deng and Dong Yu, “Deep Learning Methods and Applications”, Publishers Inc.
3 Satish Kumar "Neural Networks A Classroom Approach" Tata McGraw-Hill.
4 JM Zurada “Introduction to Artiﬁcial Neural Systems”, Jaico Publishing House
5 M. J. Kochenderfer, Tim A. Wheeler. “Algorithms for Optimization”, MIt Press.
1 Buduma, N. and Locascio, N., “Fundamentals of deep learning: Designing next-generation machine intelligence algorithms" 2017. O'Reilly Media, Inc.".
2 François Chollet. “Deep learning with Python “(Vol. 361). 2018 New York: Manning.
3 Douwe Osinga. “Deep Learning Cookbook”, O’REILLY, SPD Publishers, Delhi.
4 Simon Haykin, Neural Network- A Comprehensive Foundation- Prentice Hall International, Inc
5 S.N.Sivanandam and S.N.Deepa, Principles of soft computing-Wiley India
1 https://nptel.ac. https://deeplearning.cs.cmu.edu/S21/index.html
2 http://www.cse.iitm.ac.in/~miteshk/CS6910.html
3 https://nptel.ac.in/courses/106/106/106106184/
4 https://www.deeplearningbook.org/

Reproduced from the University of Mumbai syllabus for B.E. (Computer Engineering) under REV-2019 'C' Scheme, in force from the academic year 2022-23. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 7.9 (R-A) B.E. (Computer Engineering) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read

PDF 6.24 (N) B.E. (Computer Engineering) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read

PDF 6.15 B.E. (Computer Engineering) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read

PDF 6.41 (R) B.E. (Computer Engineering) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read

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