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:
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Amazon Machine Learning Developer Guild Version Latest
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Pattern recognition and machine learning - Christopher M.Bishop
Data Science and Big Data Analytics - EMC Education Services
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Python Machine Learning Eqution Reference - Sebastian Raschka
Python Deep Learning Cookbook - Indra den Bakker
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
The hundred-page Machine Learning Book - Andriy Burkov
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning with Python - Francois Cholletf
Deep Learning with Theano - Christopher Bourez
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Neural Networks - A visual introduction for beginners - Michael Taylor
Introduction to Deep Learning - Eugene Charniak
Deep Learning and Neural Networks - Jeff Heaton
Deep Learning with Python - Francois Chollet
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Pro Deep Learning with TensorFlow - Santunu Pattanayak