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:
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning with Theano - Christopher Bourez
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Learn Keras for Deep Neural Networks - Jojo Moolayil
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning and Neural Networks - Jeff Heaton
An introduction to neural networks - Kevin Gurney & University of Sheffield
Machine Learning with Python for everyone - Mark E.Fenner
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Amazon Machine Learning Developer Guild Version Latest
Deep Learning with PyTorch - Vishnu Subramanian
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Introduction to Scientific Programming with Python - Joakim Sundnes
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Fundamentals of Deep Learning - Nikhil Bubuma
Python Data Structures and Algorithms - Benjamin Baka
Coding Theory - Algorithms, Architectures and Application
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...