About Deep Learning

 Deep Learning

UNIT I

Deep Learning: Fundamentals, Introduction, Building Block of Neural Networks, Layers, MLPs, Forward pass, backward pass, class, trainer and optimizer, The Vanishing and Exploding Gradient Problems, Difficulties in Convergence, Local and Spurious Optima, Preprocessing, Momentum, learning rate Decay, Weight Initialization, Regularization, Dropout, SoftMax, Cross Entropy loss function, Activation Functions.

👉Deep Learning: UNIT 1 (A) Notes: Deep Learning: Fundamentals Part 1 Notes

👉Deep Learning: UNIT 1 (A) PPTs: Deep Learning Fundamentals Part 1 PPTs

👉Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part 2 Notes

👉Deep Learning: UNIT 1 (B): Deep Learning: Fundamentals Part2 PPTs

 UNIT II

CNN: Introduction, striding and padding, pooling layers, structure, operations and prediction of                  CNN with layers, CNN -Case study with MNIST, CNN VS Fully Connected.

👉Deep Learning: UNIT-2: CNN PPTs

👉Deep Learning: UNIT-2: CNN Notes

 UNIT III

RNN: Handling Branches, Layers, Nodes, Essential Elements-Vanilla RNNs, GRUs, LSTM 

UNIT IV

Autoencoders: Denoising Autoencoders, Sparse Autoencoders, Deep Autoencoders, Variational                                  Autoencoders, GANS 

UNIT V

Transfer Learning- Types, Methodologies, Diving into Transfer Learning, Challenges 

Text Books:

1.  Seth Weidman, “Deep Learning from Scratch”, O'Reilly Media, Inc., 2019

2.  Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning” , MIT Press, 2015

3.  Dipanjan Sarkar, Raghav Bali, “Transfer Learning in Action”, Manning Publications, 2021 

Reference Books:

1.  Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy "Deep Learning with TensorFlow: Explore neural networks with Python", Packt Publisher, 2017.

2.  Antonio Gulli, Sujit Pal, "Deep Learning with Keras", Packt Publishers, 2017.

3.  Francois Chollet, "Deep Learning with Python", Manning Publications, 2017.