Deep Learning, David Feldspar
Deep Learning, David Feldspar
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Deep Learning
Machine Learning and Data Analytics Explained

Author: David Feldspar

Narrator: Jason R. Gray

Unabridged: 1 hr 12 min

Format: Digital Audiobook Download

Published: 03/15/2018


Synopsis

How can deep learning, even machine learning, help your organization?
The lofty expectations about machine learning and deep studies and projects have skyrocketed, and yet, there is so much left to be said about the methods that trigger the higher-functioning corners of the human neural networks. With so many data and investments on the line, how can we deepen our understanding of these subjects?
That is where this guide will take you to the next level. It touches on exactly those problems and methods that optimize your financing and comprehension of the little details that often get overlooked. Furthermore, you will hear about subtopics like:
Popular machine learning methods that are being applied today.Data mining processes that you can easily use for your own company or individual proprietorship.Insights in supervised versus unsupervised data mining.Machine learning tactics and know-how.The five best steps to implement unsupervised big data machine learning.Ten ways to apply predictive analyses to the banking sector.Financial optimization techniques for regular processes.These machine learning, data mining, and other financing strategies are an intellectual, analytical goldmine you can feast your mind on.

Reviews

Goodreads review by Simone on March 29, 2024

The PRML book was a good part of my ML education, and I was looking forward to reading this new book. It has all the qualities of PRML: good organization of the material, great figures, and everything connected to probabilistic models is some of the best material I have found around. The first part......more

Goodreads review by Martin on February 17, 2025

A very modern and readable introduction to the basics of machine learning. Compared to its predecessor, it feels less Bayesian in spirit but still incorporates more of that perspective than the ML course I took. What I appreciated most was how well the book motivates the key decisions in modern ML. T......more

Goodreads review by Curtis on February 18, 2025

Used in the ML class at Berkeley. From a theoretical perspective, it is a wonderful introduction. Most books either lack mathematical rigor or lose you in the dense math without any grounds for intuition. This book is the best of both worlds: intuition through lots of graphs and pictures described w......more

Goodreads review by N1ng on April 28, 2024

This excellent textbook serves as a sequel to the book "Pattern Recognition and Machine Learning", with several already covered chapters in the latter. It provides comprehensive coverage of the foundational principles and concepts in the field of Deep Learning, presented in a clear and cohesive mann......more

Goodreads review by Raoul on August 18, 2024

Review based on the first 11 chapters as I'm still reading, but I'm not entirely convinced so far. A lot of the mathematical background seems pretty irrelevant as presented. For example, design matrices and the Moore-Penrose pseudo inverse are introduced on page 116, then never mentioned again (I've......more