B.E Artificial Intelligence and Machine Learning Deep Learning Lab Syllabus - Mumbai University

1. Implement Multilayer Perceptron algorithm to simulate XOR gate. 2. To explore python libraries for deep learning e.g. Theano, TensorFlow etc.

Module 2: Module 2 (Any Two)

• 3. Apply any of the following learning algorithms to learn the parameters of the supervised single layer feed forward neural network.
• A Stochastic Gradient Descent
• B Mini Batch Gradient Descent
• C Momentum GD
• D Nestorev GD
• E Adagrad GD
• F Adam Learning GD 4. Implement a backpropagation algorithm to train a DNN with at least 2 hidden layers. 5.Design and implement a fully connected deep neural network with at least 2 hidden layers for a classification application. Use appropriate Learning Algorithm, output function and loss function.

Module 3: Module 3 (Any One)

• 6. Design the architecture and implement the autoencoder model for Image Compression. 7. Design the architecture and implement the autoencoder model for Image denoising.

Module 4: Module 4 (Any One)

• 8. Design and implement a CNN model for digit recognition application. 9. Design and implement a CNN model for image classification. Module 5 (Any Two) 10. Design and implement LSTM model for handwriting recognition, speech recognition, machine translation, speech activity detection, robot control, video games, time series forecasting etc. 11. Design and implement GRU for any real life applications, chat bots etc. 12. Design and implement RNN for classification of temporal data , sequence to sequence data modelling etc.

Textbooks

• 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 Systemsǁ, Jaico Publishing House
• 5 M. J. Kochenderfer, Tim A. Wheeler. ―Algorithms for Optimization, MIT Press.
• 1 Deep Learning from Scratch: Building with Python from First Principles- Seth Weidman by O`Reilley
• 2 François Chollet. ―Deep learning with Python ―(Vol. 361). 2018 New York: Manning.
• 3 Douwe Osinga. ―Deep Learning Cookbookǁ, 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://keras.io/
• 2 https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
• 3 https://keras.io/examples/vision/autoencoder/
• 4 https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks