This book will take you through all of the main aspects of artificial intelligence:
- The theory of machine learning and deep learning
- Mathematical representations of the main AI algorithms
- Real life case studies
- Tens of opensource Python programs using TensorFlow, TensorBoard, Keras and more
- Cloud AI Platforms: Google, Amazon Web Services, IBM Watson and IBM Q to introduce you to quantum computing
- An Ubuntu VM containing all the opensource programs that you can run in one-click
- Online videos
This book will take you to the cutting edge and beyond with innovations that show how to improve existing solutions to make you a key asset as a consultant, developer, professor or any person involved in artificial intelligence.
Who this book is for
- This book contains the main artificial intelligence algorithms on the market today. Each machine learning and deep learning solution is illustrated by a case study and an open source program available on GitHub.
- Project managers and consultants: To understand how to manage AI input datasets, make a solution choice (cloud platform or development), and use the outputs of an AI system.
- Teachers, students, and developers: This book provides an overview of many key AI components, with tens of Python sample programs that run on Windows and Linux. A VM is available as well.
- Anybody who wants to understand how AI systems are built and what they are used for.
Related posts:
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
An introduction to neural networks - Kevin Gurney & University of Sheffield
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Deep Learning in Python - LazyProgrammer
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Introduction to Deep Learning - Eugene Charniak
Python Data Structures and Algorithms - Benjamin Baka
Pattern recognition and machine learning - Christopher M.Bishop
The hundred-page Machine Learning Book - Andriy Burkov
Introduction to the Math of Neural Networks - Jeff Heaton
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Python Machine Learning - Sebastian Raschka
Deep Learning with Python - Francois Chollet
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Python Machine Learning Eqution Reference - Sebastian Raschka
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Java Deep Learning Essentials - Yusuke Sugomori
R Deep Learning Essentials - Dr. Joshua F.Wiley
Intelligent Projects Using Python - Santanu Pattanayak
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning with Theano - Christopher Bourez