Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
Top Free C++ Books 🌠 - 100% Free or Open Source!
  • Title: Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
  • Author(s): Jeremy Howard and Sylvain Gugger
  • Publisher: O'Reilly Media; 1st edition (August 11, 2020); eBook (GitHub Edition: Jupyter Notebooks)
  • Permission: The code in the notebooks and python .py files is covered by the GPL v3 license; see the LICENSE file for details. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use.
  • Paperback: 624 pages
  • eBook: Jupyter Notebooks
  • Language: English / Spanish / Korean / Chinese / Bengali / Indonesian / Italian / Portuguese / Vietnamese
  • ISBN-10: 1492045527
  • ISBN-13: 978-1492045526
  • Share This:  

Book Description

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.

Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, 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.

  • Train models in computer vision, natural language processing, tabular data, and collaborative filtering
  • Learn the latest deep learning techniques that matter most in practice
  • Improve accuracy, speed, and reliability by understanding how deep learning models work
  • Discover how to turn your models into web applications
  • Implement deep learning algorithms from scratch
  • Consider the ethical implications of your work
  • Gain insight from the foreword by PyTorch cofounder, Soumith Chintala.
About the Authors
  • Jeremy Howard is an entrepreneur, business strategist, developer, and educator.
  • Sylvain Gugger is a former teacher and a Research Scientist at fast.ai.
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • 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 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.

  • 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.

  • Deep Learning with JavaScript: Neural Networks in TensorFlow.js

    This book shows developers how they can bring Deep Learning technology to the web. Written by the main authors of the TensorFlow library, it provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.

  • Deep Learning (Ian Goodfellow, et al)

    Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

  • 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.

  • Probabilistic Machine Learning: An Introduction (Kevin Murphy)

    This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

Book Categories
:
Other Categories
Resources and Links