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:

Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Introduction to Deep Learning - Eugene Charniak
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Python Deep Learning Cookbook - Indra den Bakker
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Pattern recognition and machine learning - Christopher M.Bishop
Machine Learning with Python for everyone - Mark E.Fenner
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Medical Image Segmentation Using Artificial Neural Networks
Neural Networks and Deep Learning - Charu C.Aggarwal
Pro Deep Learning with TensorFlow - Santunu Pattanayak
An introduction to neural networks - Kevin Gurney & University of Sheffield
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning and Neural Networks - Jeff Heaton
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda