Krohn J., Beyleveld G., Bassens A. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

Addison-Wesley Professional, 2019. — 871 p. — (Addison-Wesley Data & Analytics Series). — ISBN: 0135116694.Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely visual, intuitive, and accessible, and yet offers a comprehensive introduction to the discipline’s techniques and applications. Packed with full-color applications and easy-to-follow code, it sweeps away much of the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with crucial material from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. He also offers a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He covers essential theory with as little mathematics as possible, preferring to illuminate concepts with hands-on Python code and practical “run-throughs” in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile, high-level deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners. Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, deep feedforward networks, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more. Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects.Figures.
Tables.
Examples.Introducing Deep Learning.
Human and Machine Language.
Machine Art.
Game-Playing Machines.
The (Code) Cart Ahead of the (Theory) Horse.
Artificial Neurons Detecting Hot Dogs.
Artificial Neural Networks.
Training Deep Networks.
Improving Deep Networks.
Machine Vision.
Natural Language Processing.
Generative Adversarial Networks.
Deep Reinforcement Learning.
Moving Forward with Your Own Deep Learning Projects.
A Formal Neural Network Notation.
B Backpropagation.
C PyTorch.PDF conv. (HQ)