Description
Chapter 1: Mathematical Foundations Chapter Goal: Setting the mathematical base for machine learning and deep learning . No of pages 100 Sub -Topics 1. Linear algebra 2. Calculus 3. Probability 4. Formulation of machine learning algorithms and optimization techniques. Chapter 2: Introduction to Deep learning Concepts and Tensorflow 2.0 Chapter Goal: Setting the foundational base for deep learning and introduction to Tensorflow 2.0 programming paradigm. No of pages: 75 Sub – Topics: 5. Deep learning and its evolution. 6. Evolution of the learning techniques: from perceptron based learning to back-propagation 7. Different deep learning objectives functions for supervised and unsupervised learning. 8. Tensorflow 2.0 9. GPU Chapter 3: Convolutional Neural networks Chapter Goal: The mathematical and technical aspects of convolutional neural network No of pages: 80 1. Convolution operation 2. Analog and digital signal 3. 2D and 3D convolution, dilation and depth-wise separable convolution 4. Common image processing filter 5. Convolutional neural network and components 6. Backpropagation through convolution and pooling layers 7. Translational invariance and equivariance 8. Batch normalization 9. Image segmentation and localization methods (Moved from advanced Neural Network to here, to make room for Graph Neural Networks ) Chapter 4: Deep learning for Natural Language Processing Chapter Goal: Deep learning methods and natural language processing No of pages: Sub – Topics: 1. Vector space model 2. Word2Vec 3. Introduction to recurrent neural network and LSTM 4. Attention 5. Transformer network architectures Chapter 5: Unsupervised Deep Learning Methods Chapter Goal: Foundations for different unsupervised deep learning techniques No of pages: 60 Sub – Topics: 1. Boltzmann distribution 2. Bayesian inference 3. Restricted Boltzmann machines 4. Auto Encoders and variation methods Chapter 6: Advanced Neural Networks Chapter Goal: Generative adversarial networks and graph neural networks No of pages: 70 Sub – Topics: 1. Introduction to generative adversarial networks 2. CycleGAN, LSGAN Wasserstein GAN 3. Introduction to graph neural network 4. Graph attention network and graph SAGE Chapter 7: Reinforcement Learning Chapter Goal: Reinforcement Learning using Deep Learning No of pages: 50 Sub – Topics: 1. Introduction to reinforcement learning and MDP formulation 2. Value based methods 3. DQN 4. Policy based methods 5. Reinforce and actor critic network in policy based formulations 6. Transition-less reinforcement learning and bandit methods
