FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
|
|
- Title: Deep Learning
- Author(s) Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Publisher: The MIT Press (November 18, 2016); eBook (Online Version - Free)
- Permission: This is the online version of the published book. It's Free!
- Hardcover: 775 pages
- eBook: HTML
- Language: English
- ISBN-10: 0262035618
- ISBN-13: 978-0262035613
- Share This:
This is the online version of the published book. It's Free!
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX.
About the Authors- Ian Goodfellow is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning.
- Yoshua Bengio is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.
- Aaron Courville is Assistant Professor of Computer Science at the Université de Montréal.
- Deep Learning and Neural Networks
- Machine Learning
- Data Science
- Artificial Intelligence
- Data Analysis and Data Mining
-
Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al)
This book aims to provide an introduction to the topic of deep learning algorithms, coverss essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures and algorithms.
-
Neural Networks and Deep Learning (Michael Nielsen)
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.
-
Deep Learning on Graphs (Yao Ma, et al)
The book is a self-contained, comprehensive text on foundations and techniques of Graph Neural Networks with applications in NLP, data mining, vision and healthcare. Accessible to who want to use graph neural networks to advance their disciplines.
-
Deep Neural Networks and Data for Automated Driving
This open access book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving and artificial intelligence.
-
Hyperparameter Tuning for Deep Learning: A Practical Guide
This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.
-
Deep Learning with Python, 2nd Edition (Francois Chollet)
This book introduces the field of deep learning using Python and the powerful Keras library. It offers insights for both novice and experienced machine learning practitioners, and builds your understanding through intuitive explanations and practical examples.
-
Dive into Deep Learning (Aston Zhang, et al.)
This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.
-
Deep Learning for Coders with Fastai and PyTorch
This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.
-
Deep Learning with PyTorch (Eli Stevens, et al.)
This book teaches you to create deep learning and neural network systems with PyTorch. It gets you to work right away building a tumor image classifier from scratch. You'll learn best practices for the entire deep learning pipeline, tackling advanced projects.
-
Approaching (Almost) Any Machine Learning Problem
This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.
-
Machine Learning with Neural Networks (Bernhard Mehlig)
This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.
-
Learning Deep Architectures for AI (Yoshua Bengio)
This book discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed.
-
First Contact with Deep Learning: Practical Introduction with Keras
This book gradually starts the reader off in Deep Learning, in a practical way with the Python language. Using the Keras library allows the development of Deep Learning models and abstracts much of the mathematical complexity involved in its implementation.
:
|
|