The Science of Learning by Joseph Pear

Cover of: The Science of Learning | Joseph Pear

Published by Psychology Press .

Written in English

Read online


  • Learning,
  • Psychology,
  • PSY,
  • PSY000000,
  • Cognitive Psychology,
  • Psychology & Psychiatry / Social Psychology,
  • Learning, Psychology of,
  • General

Book details

The Physical Object
Number of Pages516
ID Numbers
Open LibraryOL8921185M
ISBN 101841690376
ISBN 109781841690377

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Neural Networks and Deep Learning - This free online book aims to teach machine learning principles. It’s not the place to go to learn the technical intricacies of any particular library, and it’s written with the now-outdated Python rather than Python 3, but there’s still a lot of valuable wisdom here.

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Chapter 1 Preview. Communication Breakdown Between Science and Practice in Education. Chapter 2 Preview. Different Types of Evidence in Education. Chapter 3 Preview. Is Intuition the Enemy of Teaching and Learning. Chapter 4 Preview. Pervasive Misunderstandings about Learning: How They Arise, and What Can We Do.

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Powerful Teaching is an indispensable book written by a cognitive scientist and a veteran K–12 teacher. Take your instruction to the next level, implement evidence-based strategies, and.

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Aug 08,  · The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.

The online version of the book is now complete and will remain available online for free. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable.

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In many ways, machine learning is the primary means by which data science manifests itself to the broader world. Machine learning is where these computational and algorithmic skills of data science meet the statistical thinking of data science, and the result is a collection of approaches to inference and data exploration that are not about effective theory so much as effective computation.

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