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:
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Fundamentals of Deep Learning - Nikhil Bubuma
Machine Learning with spark and python - Michael Bowles
R Deep Learning Essentials - Dr. Joshua F.Wiley
Intelligent Projects Using Python - Santanu Pattanayak
Deep Learning for Natural Language Processing - Jason Brownlee
Deep Learning with Python - Francois Chollet
Amazon Machine Learning Developer Guild Version Latest
Learn Keras for Deep Neural Networks - Jojo Moolayil
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Python Machine Learning - Sebastian Raschka
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Java Deep Learning Essentials - Yusuke Sugomori
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Data Science and Big Data Analytics - EMC Education Services
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Introduction to Scientific Programming with Python - Joakim Sundnes
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Introduction to Deep Learning - Eugene Charniak
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Introduction to the Math of Neural Networks - Jeff Heaton
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Deep Learning in Python - LazyProgrammer