Pro Deep Learning with TensorFlow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python - Softcover

Pattanayak, Santanu

Softcover

ISBN 10: 1484289323 ISBN 13: 9781484289327

Publisher: Apress, 2023

Synopsis

Chapter 1: Mathematical FoundationsChapter Goal: 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 Goal: Setting the foundational base for deep learning and introduction to Tensorflow 2.0 programming paradigm. No of pages: 75Sub - Topics: 5. Deep learning and its evolution.6. Evolution of the learning techniques: 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 Goal: The mathematical and technical aspects of convolutional neural networkNo of pages: 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 Goal: Deep learning methods and natural language processing No of pages: Sub - Topics: 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 Goal: Foundations for different unsupervised deep learning techniques No of pages: 60Sub - Topics: 1. Boltzmann distribution2. Bayesian inference3. 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: 70Sub - Topics: 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 Goal: Reinforcement Learning using Deep Learning No of pages: 50Sub - Topics: 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

Edition details

• Publisher: Apress
• Publication date: 2023
• Language: English
• ISBN 10: 1484289323
• ISBN 13: 9781484289327
• Binding: Paperback
• Number of pages: 674