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
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Deep Learning in Python - LazyProgrammer
Python Deep Learning Cookbook - Indra den Bakker
Deep Learning with Python - Francois Chollet
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Deep Learning with Hadoop - Dipayan Dev
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
An introduction to neural networks - Kevin Gurney & University of Sheffield
Machine Learning with spark and python - Michael Bowles
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Pattern recognition and machine learning - Christopher M.Bishop
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
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Introduction to Scientific Programming with Python - Joakim Sundnes
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Neural Networks - A visual introduction for beginners - Michael Taylor
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Medical Image Segmentation Using Artificial Neural Networks
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Introduction to the Math of Neural Networks - Jeff Heaton
Neural Networks and Deep Learning - Charu C.Aggarwal
