July 2017
Intermediate to advanced
240 pages
6h 40m
English
book
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R
by Taweh Beysolow II
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Index
A
A/B testing
beta-binomial hierarchical model for
simple two-sample
Activation function
Additive law of probability
Akaike information criterion (AIC)
AlexNet
Amazon Web Services (AWS)
Analysis of Variance (ANOVA)
MANOVA
mixed-design
one-way
two-way (multiple-way)
Ant colony optimization (ACO)
Arithmetic mean
Asset price prediction
description of experiment
feature selection
supervised learning
Associative property
Autoencoders
linear autoencoders vs. PCA
Axioms
associative property
commutative property
distributivity of scalar multiplication
identity element of addition
identity element of scalar multiplication
inverse elements of addition
B
Back-propagation algorithm
Back-propagation through time (BPPT)
Backward selection
Batch learning
Bayesian classifier
Bayesian learning ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.