Artificial Intelligence by example – Denis Rothman

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:

Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Coding Theory - Algorithms, Architectures and Application
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Introduction to Deep Learning - Eugene Charniak
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Introduction to the Math of Neural Networks - Jeff Heaton
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Python Machine Learning Eqution Reference - Sebastian Raschka
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Deep Learning in Python - LazyProgrammer
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Machine Learning with Python for everyone - Mark E.Fenner
Deep Learning with Theano - Christopher Bourez
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
R Deep Learning Essentials - Dr. Joshua F.Wiley
Java Deep Learning Essentials - Yusuke Sugomori
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Python Data Structures and Algorithms - Benjamin Baka
Medical Image Segmentation Using Artificial Neural Networks
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Amazon Machine Learning Developer Guild Version Latest
Introduction to Scientific Programming with Python - Joakim Sundnes
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Neural Networks and Deep Learning - Charu C.Aggarwal
Neural Networks - A visual introduction for beginners - Michael Taylor
The hundred-page Machine Learning Book - Andriy Burkov
Machine Learning with spark and python - Michael Bowles