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:

Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Introduction to the Math of Neural Networks - Jeff Heaton
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Python Deep Learning Cookbook - Indra den Bakker
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Introduction to Deep Learning - Eugene Charniak
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Machine Learning with spark and python - Michael Bowles
The hundred-page Machine Learning Book - Andriy Burkov
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Intelligent Projects Using Python - Santanu Pattanayak
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Deep Learning and Neural Networks - Jeff Heaton
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
An introduction to neural networks - Kevin Gurney & University of Sheffield
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Python Machine Learning - Sebastian Raschka
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi