Download Machine Learning Refined: Foundations, Algorithms, and Applications PDF Ebook Book Synopsis Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization. Additional resources including supplemental discussion topics, code demonstrations, and exercises can be found on the official textbook website at mlrefined.com

Book details ●











Author : Jeremy Watt Pages : 298 pages Publisher : Cambridge University Press 201611-04 Language : English ISBN-10 : 1107123526 ISBN-13 : 9781107123526

Download Machine Learning Refined: Foundations ...

Book Synopsis. Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts.

118KB Sizes 1 Downloads 135 Views

Recommend Documents

Refined Error Bounds for Several Learning Algorithms - Steve Hanneke
known that there exist spaces C for which this is unavoidable (Auer and Ortner, 2007). This same logarithmic factor gap ... generally denote Lm = {(X1,f⋆(X1)),...,(Xm,f⋆(Xm))}, and Vm = C[Lm] (called the version space). ...... was introduced in t

Applied Machine Learning - GitHub
In Azure ML Studio, on the Notebooks tab, open the TimeSeries notebook you uploaded ... 9. Save and run the experiment, and visualize the output of the Select ...

Machine learning - Royal Society
a vast number of examples, which machine learning .... for businesses about, for example, the value of machine ...... phone apps, but also used to automatically.

Applied Machine Learning - GitHub
Then in the Upload a new notebook dialog box, browse to select the notebook .... 9. On the browser tab containing the dashboard page for your Azure ML web ...

Machine learning - Royal Society
used on social media; voice recognition systems .... 10. MACHINE LEARNING: THE POWER AND PROMISE OF COMPUTERS THAT LEARN BY EXAMPLE ..... which show you websites or advertisements based on your web browsing habits'.

Applied Machine Learning - GitHub
course. Exploring Spatial Data. In this exercise, you will explore the Meuse ... folder where you extracted the lab files on your local computer. ... When you have completed all of the coding tasks in the notebook, save your changes and then.