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 - Eugene Charniak
Java Deep Learning Essentials - Yusuke Sugomori
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Fundamentals of Deep Learning - Nikhil Bubuma
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
An introduction to neural networks - Kevin Gurney & University of Sheffield
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Deep Learning with Hadoop - Dipayan Dev
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning with Python - Francois Cholletf
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Python Machine Learning - Sebastian Raschka
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Deep Learning in Python - LazyProgrammer
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Deep Learning for Natural Language Processing - Jason Brownlee
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Medical Image Segmentation Using Artificial Neural Networks
Pattern recognition and machine learning - Christopher M.Bishop
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Python Data Structures and Algorithms - Benjamin Baka