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

This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you...

Full description

Bibliographic Details
Main Author: Pattanayak, Santanu
Format: eBook
Language:English
Published: Berkeley, CA Apress 2023, 2023
Edition:2nd ed. 2023
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
LEADER 02993nmm a2200313 u 4500
001 EB002137574
003 EBX01000000000000001275701
005 00000000000000.0
007 cr|||||||||||||||||||||
008 230102 ||| eng
020 |a 9781484289310 
100 1 |a Pattanayak, Santanu 
245 0 0 |a Pro Deep Learning with TensorFlow 2.0  |h Elektronische Ressource  |b A Mathematical Approach to Advanced Artificial Intelligence in Python  |c by Santanu Pattanayak 
250 |a 2nd ed. 2023 
260 |a Berkeley, CA  |b Apress  |c 2023, 2023 
300 |a XX, 652 p. 213 illus  |b online resource 
505 0 |a Chapter 1: Mathematical Foundations -- Chapter 2: Introduction to Deep learning Concepts and Tensorflow 2.0 -- Chapter 3: Convolutional Neural networks -- Chapter 4: Natural Language Processing -- Chapter 5: Unsupervised Learning with Restricted Boltzmann Machines and Auto-encoders -- Chapter 6: Advanced Neural Networks 
653 |a Machine learning 
653 |a Machine Learning 
653 |a Artificial Intelligence 
653 |a Python 
653 |a Artificial intelligence 
653 |a Python (Computer program language) 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
028 5 0 |a 10.1007/978-1-4842-8931-0 
856 4 0 |u https://doi.org/10.1007/978-1-4842-8931-0?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 006.3 
520 |a This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as Node2Vec, GCN, GraphSAGE, and graph attention networks. Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications. You will: Understand full-stack deep learning using TensorFlow 2.0 Gain an understanding of the mathematical foundations of deep learning Deploy complex deep learning solutions in production using TensorFlow 2.0 Understand generative adversarial networks, graph attention networks, and GraphSAGE.