The subject of this book is automated learning, or, as we will more often call it, Machine Learning (ML). That is, we wish to program computers so that they can “learn” from input available to them. Roughly speaking, learning is the process of converting experience into expertise or knowledge. The input to a learning algorithm is training data, representing experience, and the output is some expertise, which usually takes the form of another computer program that can perform some task. Seeking a formal-mathematical understanding of this concept, we’ll have to be more explicit about what we mean by each of the involved terms: What is the training data our programs will access? How can the process of learning be automated? How can we evaluate the success of such a process (namely, the quality of the output of a learning program)?
Understanding Machine Learning from theory to algorithms – Shai Shalev-Shwartz & Shai Ben-David
Python Machine Learning Eqution Reference - Sebastian Raschka
Neural Networks - A visual introduction for beginners - Michael Taylor
Intelligent Projects Using Python - Santanu Pattanayak
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Introduction to the Math of Neural Networks - Jeff Heaton
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Coding Theory - Algorithms, Architectures and Application
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Deep Learning for Natural Language Processing - Jason Brownlee
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Machine Learning with Python for everyone - Mark E.Fenner
Java Deep Learning Essentials - Yusuke Sugomori
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Pattern recognition and machine learning - Christopher M.Bishop
Deep Learning with Theano - Christopher Bourez
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Neural Networks and Deep Learning - Charu C.Aggarwal
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Deep Learning with Python - Francois Chollet
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Data Science and Big Data Analytics - EMC Education Services
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Medical Image Segmentation Using Artificial Neural Networks
Python Machine Learning - Sebastian Raschka
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
