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:

Coding Theory - Algorithms, Architectures and Application
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Deep Learning with Hadoop - Dipayan Dev
Deep Learning with Theano - Christopher Bourez
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Machine Learning with spark and python - Michael Bowles
Neural Networks - A visual introduction for beginners - Michael Taylor
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Medical Image Segmentation Using Artificial Neural Networks
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Python Data Structures and Algorithms - Benjamin Baka
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Python Machine Learning - Sebastian Raschka
Amazon Machine Learning Developer Guild Version Latest
Deep Learning with Python - Francois Cholletf
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Machine Learning with Python for everyone - Mark E.Fenner
The hundred-page Machine Learning Book - Andriy Burkov
Deep Learning in Python - LazyProgrammer
R Deep Learning Essentials - Dr. Joshua F.Wiley
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Pattern recognition and machine learning - Christopher M.Bishop
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Deep Learning and Neural Networks - Jeff Heaton