Did you know that artificial intelligence (AI) is changing our world faster than we can imagine? Try to envision all the mind-boggling benefits of a world where AI can help us to enjoy almost every area of our lives. This world will be a reality in the very near future, but along with the benefits will come new challenges as well. AI will impact every area of our lives, in some ways that we can’t even predict, whether we like it or not. Would you like to know more about this fascinating technology? In this non-technical guidebook, you will discover the many ways in which AI is already changing our world, and how you can adjust to these changes. You will also learn practical methods you can use to apply AI in your own personal and professional life. Artificial intelligence will help us to do almost everything better, faster and cheaper, but it will also bring about some considerable challenges that we need to start preparing for now. One common misconception is that only big technology companies can work with and benefit from AI. In this book you will discover several creative ways in which you can do the same.
Artificial Intelligence – 101 things you must know today about our future – Lasse Rouhiainen
Python Machine Learning Eqution Reference - Sebastian Raschka
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Introduction to Deep Learning - Eugene Charniak
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Pattern recognition and machine learning - Christopher M.Bishop
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Neural Networks - A visual introduction for beginners - Michael Taylor
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Data Science and Big Data Analytics - EMC Education Services
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Deep Learning with PyTorch - Vishnu Subramanian
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning with Applications Using Python - Navin Kumar Manaswi
An introduction to neural networks - Kevin Gurney & University of Sheffield
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Coding Theory - Algorithms, Architectures and Application
Deep Learning and Neural Networks - Jeff Heaton