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:

Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Deep Learning in Python - LazyProgrammer
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Deep Learning with Hadoop - Dipayan Dev
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Python Deep Learning Cookbook - Indra den Bakker
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Deep Learning and Neural Networks - Jeff Heaton
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Deep Learning with Python - Francois Cholletf
Pattern recognition and machine learning - Christopher M.Bishop
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Amazon Machine Learning Developer Guild Version Latest
Coding Theory - Algorithms, Architectures and Application
Python Data Structures and Algorithms - Benjamin Baka
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Intelligent Projects Using Python - Santanu Pattanayak
Introduction to Scientific Programming with Python - Joakim Sundnes
Introduction to the Math of Neural Networks - Jeff Heaton