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:

Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Python Deep Learning Cookbook - Indra den Bakker
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Introduction to Deep Learning - Eugene Charniak
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Python Data Structures and Algorithms - Benjamin Baka
Machine Learning with spark and python - Michael Bowles
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Deep Learning with Python - Francois Cholletf
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Data Science and Big Data Analytics - EMC Education Services
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Medical Image Segmentation Using Artificial Neural Networks
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Neural Networks and Deep Learning - Charu C.Aggarwal
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Deep Learning with Hadoop - Dipayan Dev
Intelligent Projects Using Python - Santanu Pattanayak