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:

Python Data Structures and Algorithms - Benjamin Baka
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Deep Learning with Theano - Christopher Bourez
R Deep Learning Essentials - Dr. Joshua F.Wiley
Machine Learning with spark and python - Michael Bowles
Data Science and Big Data Analytics - EMC Education Services
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Medical Image Segmentation Using Artificial Neural Networks
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Machine Learning with Python for everyone - Mark E.Fenner
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Introduction to the Math of Neural Networks - Jeff Heaton
The hundred-page Machine Learning Book - Andriy Burkov
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Introduction to Scientific Programming with Python - Joakim Sundnes
Learn Keras for Deep Neural Networks - Jojo Moolayil
Java Deep Learning Essentials - Yusuke Sugomori
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Pattern recognition and machine learning - Christopher M.Bishop
Deep Learning and Neural Networks - Jeff Heaton
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain