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
Intelligent Projects Using Python - Santanu Pattanayak
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Neural Networks and Deep Learning - Charu C.Aggarwal
Python Machine Learning Eqution Reference - Sebastian Raschka
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Python Data Structures and Algorithms - Benjamin Baka
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Fundamentals of Deep Learning - Nikhil Bubuma
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Machine Learning with spark and python - Michael Bowles
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Medical Image Segmentation Using Artificial Neural Networks
An introduction to neural networks - Kevin Gurney & University of Sheffield
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Python Deep Learning Cookbook - Indra den Bakker
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Machine Learning with Python for everyone - Mark E.Fenner
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Deep Learning with Hadoop - Dipayan Dev
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David