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:

Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning and Neural Networks - Jeff Heaton
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Deep Learning for Natural Language Processing - Jason Brownlee
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Learn Keras for Deep Neural Networks - Jojo Moolayil
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Deep Learning in Python - LazyProgrammer
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Neural Networks and Deep Learning - Charu C.Aggarwal
Machine Learning with Python for everyone - Mark E.Fenner
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Python Machine Learning - Sebastian Raschka
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Medical Image Segmentation Using Artificial Neural Networks
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Introduction to Deep Learning - Eugene Charniak
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach