Deep Learning dummies second edition – John Paul Mueller & Luca Massaronf

As one of the most comprehensive machine learning texts around, this book does justice to the field’s incredible richness, but without losing sight of the unifying principles. Peter Flach’s clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. He covers a wide range of logical, geometric
and statistical models, and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features.


Machine Learning will set a new standard as an introductory textbook:

  • The Prologue and Chapter 1 are freely available on-line, providing an accessible first step into machine learning.
  • The use of established terminology is balanced with the introduction of new and useful concepts.
  • Well-chosen examples and illustrations form an integral part of the text.
  • Boxes summarise relevant background material and provide pointers for revision.
  • Each chapter concludes with a summary and suggestions for further reading.
  • A list of ‘Important points to remember’ is included at the back of the book together with an extensive index to help readers navigate through the material.

Related posts:

Pro Deep Learning with TensorFlow - Santunu Pattanayak
Introduction to Deep Learning - Eugene Charniak
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
An introduction to neural networks - Kevin Gurney & University of Sheffield
Coding Theory - Algorithms, Architectures and Application
Deep Learning with Python - Francois Cholletf
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Machine Learning with spark and python - Michael Bowles
Neural Networks - A visual introduction for beginners - Michael Taylor
Deep Learning with Python - Francois Chollet
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning in Python - LazyProgrammer
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Python Machine Learning - Sebastian Raschka
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Pattern recognition and machine learning - Christopher M.Bishop
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Learn Keras for Deep Neural Networks - Jojo Moolayil