Let’s start by telling the truth: machines don’t learn. What a typical “learning machine” does, is finding a mathematical formula, which, when applied to a collection of inputs (called “training data”), produces the desired outputs. This mathematical formula also generates the correct outputs for most other inputs (distinct from the training data) on the condition that those inputs come from the same or a similar statistical distribution as the one the training data was drawn from. Why isn’t that learning? Because if you slightly distort the inputs, the output is very likely to become completely wrong. It’s not how learning in animals works. If you learned to play a video game by looking straight at the screen, you would still be a good player if someone rotates the screen slightly. A machine learning algorithm, if it was trained by “looking” straight at the screen, unless it was also trained to recognize rotation, will fail to play the game on a rotated screen. So why the name “machine learning” then? The reason, as is often the case, is marketing: Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term in 1959 while at IBM. Similarly to how in the 2010s IBM tried to market the term “cognitive computing” to stand out from competition, in the 1960s, IBM used the new cool term “machine learning” to attract both clients and talented employees. As you can see, just like artificial intelligence is not intelligence, machine learning is not learning. However, machine learning is a universally recognized term that usually refers to the science and engineering of building machines capable of doing various useful things without being explicitly programmed to do so. So, the word “learning” in the term is used by analogy with the learning in animals rather than literally.
The hundred-page Machine Learning Book – Andriy Burkov
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Coding Theory - Algorithms, Architectures and Application
Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Neural Networks - A visual introduction for beginners - Michael Taylor
R Deep Learning Essentials - Dr. Joshua F.Wiley
Data Science and Big Data Analytics - EMC Education Services
An introduction to neural networks - Kevin Gurney & University of Sheffield
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Introduction to Deep Learning - Eugene Charniak
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Artificial Intelligence by example - Denis Rothman
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Neural Networks and Deep Learning - Charu C.Aggarwal
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Learn Keras for Deep Neural Networks - Jojo Moolayil
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Python Machine Learning Eqution Reference - Sebastian Raschka
Deep Learning and Neural Networks - Jeff Heaton
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad