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
Deep Learning with Theano - Christopher Bourez
Learn Keras for Deep Neural Networks - Jojo Moolayil
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Coding Theory - Algorithms, Architectures and Application
Deep Learning with PyTorch - Vishnu Subramanian
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Introduction to Deep Learning - Eugene Charniak
An introduction to neural networks - Kevin Gurney & University of Sheffield
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Deep Learning with Hadoop - Dipayan Dev
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Artificial Intelligence by example - Denis Rothman
Fundamentals of Deep Learning - Nikhil Bubuma
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Introduction to the Math of Neural Networks - Jeff Heaton
Python Deep Learning Cookbook - Indra den Bakker
Neural Networks and Deep Learning - Charu C.Aggarwal
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Deep Learning in Python - LazyProgrammer
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Intelligent Projects Using Python - Santanu Pattanayak
R Deep Learning Essentials - Dr. Joshua F.Wiley
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Medical Image Segmentation Using Artificial Neural Networks
Neural Networks - A visual introduction for beginners - Michael Taylor
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
