PDF Preview:
| PDF Title : | Machine Learning Refined |
|---|---|
| Total Page : | 301 Pages |
| Author: | Jeremy Watt |
| PDF Size : | 6.1 MB |
| Language : | English |
| Publisher : | cambridge.org |
| PDF Link : | Available |
Summary
Here on this page, we have provided the latest download link for Machine Learning Refined : Foundations, Algorithms, and Applications PDF. Please feel free to download it on your computer/mobile. For further reference, you can go to cambridge.org
Machine Learning Refined : Foundations, Algorithms, and Applications – Book
The objective or cost function value of two runs of the standard gradient descent scheme, the first using the conservatively optimal fixed step length and the second using the adaptive rule, along with the corresponding cost function value of the stochastic gradient procedure.
Here the results of 15 runs of each method are shown in lighter colors, where in each instance a shared random initialization is used by all three methods, and the average overall runs of each method is highlighted as a darker curve.
We can see that the stochastic gradient descent scheme is massively superior on this large dataset in terms of the rapidity of its convergence when compared to the standard gradient method.
Machine Learning Refined : Foundations, Algorithms, and Applications PDF
Know more about our initiative
[yasr_visitor_votes size=”medium”]