Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
First Contact with Deep Learning: Practical Introduction with Keras
🌠 Top Free Mathematics Books - 100% Free or Open Source!
  • Title: First Contact with Deep Learning: Practical Introduction with Keras
  • Author(s) Jordi Torres
  • Publisher: Independently published (July 13, 2018); eBook (Creative Commons Licensed)
  • License(s): Creative Commons License
  • Hardcover: 204 pages
  • eBook: HTML and PDF (206 pages)
  • Language: English
  • ISBN-10: 1983211559
  • ISBN-13: 978-1983211553
  • Share This:  

Book Description

Artificial Intelligence is changing our lives, and solutions based on Deep Learning are leading this transformation. Deep Learning is now of major interest to private companies, since it can be applied to many areas of activity. But getting started in this technology is not an easy task. Many enthusiastic professionals in the field of Deep Learning have difficulties establishing a starting point and breaking into this area of innovation, given the enormous amount of resources available today and the complexity of the field.

The purpose of this book is to gradually start the reader off in this exciting world, 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.

About the Authors
  • Jordi Torres is a professor at the Universitat Politecnica de Catalunya UPC Barcelona Tech with 30 years of experience in teaching and research in high-performance computing, with relevant scientific publications and R&D projects in companies and institutions.
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Understanding Deep Learning (Simon J.D. Prince)

    An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Only the most important ideas to provide a high density of critical information in an intuitive and digestible form.

  • 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.

  • The Shallow and the Deep: Introduction to Neural Networks

    This book is a collection of lecture notes that offers an accessible introduction to Neural Networks and machine learning in general. The focus lies on classical machine learning techniques, with a bias towards classification and regression.

  • Gradient Expectations: Structure of Predictive Neural Networks

    An insightful investigation into the mechanisms underlying the predictive functions of neural networks - and their ability to chart a new path for AI. Delve into the known neural architecture of the mammalian brain to illuminate the structure of predictive networks.

  • 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.

  • 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 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.

  • 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.

  • Physics-Based Deep Learning (Nils Thuerey, et al.)

    This book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. All topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started.

  • 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 Python Tutorial (Bernd Klein)

    This practical guide provides helps to solve machine learning challenges you may encounter in your work. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.

  • 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.

Book Categories
:
Other Categories
Resources and Links