What do you think?
Rate this book
Pro Deep Learning with TensorFlow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python
Chapter 1: Mathematical FoundationsChapter Setting the mathematical base for machine learning and deep learning .No of pages 100Sub -Topics1. Linear algebra 2. Calculus3. Probability4. Formulation of machine learning algorithms and optimization techniques.
Chapter 2: Introduction to Deep learning Concepts and Tensorflow 2.0 Chapter Setting the foundational base for deep learning and introduction to Tensorflow 2.0 programming paradigm. No of 75Sub - 5. Deep learning and its evolution.6. Evolution of the learning from perceptron based learning to back-propagation7. Different deep learning objectives functions for supervised and unsupervised learning.8. Tensorflow 2.09. GPU
Chapter 3: Convolutional Neural networksChapter The mathematical and technical aspects of convolutional neural networkNo of 801. Convolution operation2. Analog and digital signal3. 2D and 3D convolution, dilation and depth-wise separable convolution 4. Common image processing filter 5. Convolutional neural network and components6. Backpropagation through convolution and pooling layers7. Translational invariance and equivariance 8. Batch normalization9. 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 Deep learning methods and natural language processing No of Sub - 1. Vector space model2. Word2Vec 3. Introduction to recurrent neural network and LSTM4. Attention 5. Transformer network architectures
Chapter 5: Unsupervised Deep Learning Methods
Chapter Foundations for different unsupervised deep learning techniques No of 60Sub - 1. Boltzmann distribution2. Bayesian inference3. Restricted Boltzmann machines 4. Auto Encoders and variation methods
Chapter 6: Advanced Neural Networks Chapter Generative adversarial networks and graph neural networks No of 70Sub - 1. Introduction to generative adversarial networks 2. CycleGAN, LSGAN Wasserstein GAN3. Introduction to graph neural network4. Graph attention network and graph SAGE
Chapter 7: Reinforcement Learning Chapter Reinforcement Learning using Deep Learning No of 50Sub - 1. Introduction to reinforcement learning and MDP formulation2. Value based methods3. DQN4. Policy based methods5. Reinforce and actor critic network in policy based formulations6. Transition-less reinforcement learning and bandit methods
Chapter 2: Introduction to Deep learning Concepts and Tensorflow 2.0 Chapter Setting the foundational base for deep learning and introduction to Tensorflow 2.0 programming paradigm. No of 75Sub - 5. Deep learning and its evolution.6. Evolution of the learning from perceptron based learning to back-propagation7. Different deep learning objectives functions for supervised and unsupervised learning.8. Tensorflow 2.09. GPU
Chapter 3: Convolutional Neural networksChapter The mathematical and technical aspects of convolutional neural networkNo of 801. Convolution operation2. Analog and digital signal3. 2D and 3D convolution, dilation and depth-wise separable convolution 4. Common image processing filter 5. Convolutional neural network and components6. Backpropagation through convolution and pooling layers7. Translational invariance and equivariance 8. Batch normalization9. 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 Deep learning methods and natural language processing No of Sub - 1. Vector space model2. Word2Vec 3. Introduction to recurrent neural network and LSTM4. Attention 5. Transformer network architectures
Chapter 5: Unsupervised Deep Learning Methods
Chapter Foundations for different unsupervised deep learning techniques No of 60Sub - 1. Boltzmann distribution2. Bayesian inference3. Restricted Boltzmann machines 4. Auto Encoders and variation methods
Chapter 6: Advanced Neural Networks Chapter Generative adversarial networks and graph neural networks No of 70Sub - 1. Introduction to generative adversarial networks 2. CycleGAN, LSGAN Wasserstein GAN3. Introduction to graph neural network4. Graph attention network and graph SAGE
Chapter 7: Reinforcement Learning Chapter Reinforcement Learning using Deep Learning No of 50Sub - 1. Introduction to reinforcement learning and MDP formulation2. Value based methods3. DQN4. Policy based methods5. Reinforce and actor critic network in policy based formulations6. Transition-less reinforcement learning and bandit methods
674 pages, Paperback
Published January 15, 2023
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
No one has reviewed this book yet.