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:

Machine Learning with spark and python - Michael Bowles
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Coding Theory - Algorithms, Architectures and Application
Deep Learning with Python - Francois Cholletf
Python Machine Learning - Sebastian Raschka
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Intelligent Projects Using Python - Santanu Pattanayak
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Medical Image Segmentation Using Artificial Neural Networks
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Java Deep Learning Essentials - Yusuke Sugomori
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Amazon Machine Learning Developer Guild Version Latest
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
An introduction to neural networks - Kevin Gurney & University of Sheffield
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Deep Learning with Hadoop - Dipayan Dev
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...