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 with PyTorch - Vishnu Subramanian
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Java Deep Learning Essentials - Yusuke Sugomori
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Deep Learning with Theano - Christopher Bourez
Coding Theory - Algorithms, Architectures and Application
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Python Data Structures and Algorithms - Benjamin Baka
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Neural Networks and Deep Learning - Charu C.Aggarwal
Machine Learning with spark and python - Michael Bowles
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
The hundred-page Machine Learning Book - Andriy Burkov
Neural Networks - A visual introduction for beginners - Michael Taylor
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Python Deep Learning Cookbook - Indra den Bakker
Introduction to the Math of Neural Networks - Jeff Heaton
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Data Science and Big Data Analytics - EMC Education Services
Medical Image Segmentation Using Artificial Neural Networks
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Deep Learning for Natural Language Processing - Jason Brownlee
Deep Learning with Hadoop - Dipayan Dev