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:
An introduction to neural networks - Kevin Gurney & University of Sheffield
Fundamentals of Deep Learning - Nikhil Bubuma
Neural Networks - A visual introduction for beginners - Michael Taylor
Coding Theory - Algorithms, Architectures and Application
Python Deep Learning Cookbook - Indra den Bakker
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Amazon Machine Learning Developer Guild Version Latest
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Python Machine Learning - Sebastian Raschka
Deep Learning with Hadoop - Dipayan Dev
Pattern recognition and machine learning - Christopher M.Bishop
Introduction to Deep Learning - Eugene Charniak
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Neural Networks and Deep Learning - Charu C.Aggarwal
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Python Machine Learning Eqution Reference - Sebastian Raschka
Deep Learning with Theano - Christopher Bourez