Deep Learning

Deep Learning  

👉Deep Learning Syllabus

• 👉Deep Learning: Fundamentals
• 👉CNN
• 👉RNN
• 👉Autoencoders
• 👉Transfer 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.

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

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.

MCQs👇

1. Neural Network 1
   
2. Neural Network 2
3. Neural Network 3
4. Neural network 4
   
5. Neural Network 5
   
6. TensorFlow and Keras -1
   
7. TensorFlow and Keras-2
   
8. TensorFlow & keras 3
   
9. Convolutional Neural Network 1
   
10. Convolutional Neural Network 2