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.


๐Ÿ‘‰Deep Learning: UNIT 1 (A) Notes: Deep Learning: Fundamentals Part 1 Notes

๐Ÿ‘‰Deep Learning: UNIT 1 (A) PPTs: Deep Learning Fundamentals Part 1 PPTs

๐Ÿ‘‰Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part 2 Notes

๐Ÿ‘‰Deep Learning: UNIT 1 (B): Deep Learning: Fundamentals Part2 PPTs

๐Ÿ‘‰Deep Learning: UNIT 1: Deep Learning - Fundamentals: Long Answer Questions

๐Ÿ‘‰Deep Learning: UNIT 1: Deep Learning - Fundamentals : Short Answer Questions


 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.


๐Ÿ‘‰Deep Learning: UNIT-2: CNN PPTs


๐Ÿ‘‰Deep Learning: UNIT-2: CNN Notes


๐Ÿ‘‰Deep Learning: UNIT 2: CNN : Long Answer Questions


๐Ÿ‘‰Deep Learning: UNIT 2: CNN: Short Answer Questions



 UNIT III

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


๐Ÿ‘‰Deep Learning: UNIT 3 (A): RNN: Notes

๐Ÿ‘‰Deep Learning: UNIT 3 (A) : RNN : PPTs

๐Ÿ‘‰Deep Learning: UNIT- 3: RNN: Long Answer Questions

๐Ÿ‘‰Deep Learning: UNIT 3 : RNN : Short Answer Questions



UNIT IV

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


๐Ÿ‘‰Deep Learning: UNIT 4: Autoencoders PPTs

๐Ÿ‘‰Deep Learning: UNIT 4: Autoencoders NOTEs


UNIT V

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


๐Ÿ‘‰Deep Learning : UNIT 5: Transfer Learning Notes

๐Ÿ‘‰Deep Learning : UNIT 5: Transfer Learning PPTs

Text Books:

1.  Seth Weidman, โ€œDeep Learning from Scratchโ€, O'Reilly Media, Inc., 2019

2.  Ian Goodfellow, Yoshua Bengio and Aaron Courville, โ€œDeep Learningโ€ , MIT Press, 2015

3.  Dipanjan Sarkar, Raghav Bali, โ€œTransfer Learning in Actionโ€, 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.

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