As soon as the first edition version 1. Thank you for your support, and I hope you enjoy the book! By becoming a patron, you'll instantly unlock access to 3 exclusive posts. Recent posts by Dragan Djuric. How it works. Choose a membership. Add a payment method. Patreon logo. Language: English United States. Currency: EUR. What is Patreon? Can I cancel my pledge any time? Fluokitten 0. I significantly improved the performance of existing currying functionality. Here's a walkthrough. This is a direct continuation of Vol 2.
Mercenary work have been finished ahead of time; a handful of time to spare. Time for free software! More Neanderthal vs ND4J comparison, then? Today, Java has a choice of a few well maintained libraries that can use Intel's MKL for fast native operations.
The reasoning is that, since two libraries use MKL, they should squeeze the same amount of juice out of it. It's not quite like that. All communication with the GPU takes place in a context.
Context is an analogous to a CPU program. It sets the environment for the particular GPU we want to use in the computations. Simple on the surface, it is, unfortunately, incidentally complex due to early CUDA legacy. So what? We have to deal with that, so, instead of complaining, we'll see how to tame it Uncomplicate libraries have just got a nice update and are ready for the latest underlying platform releases.
Yes, that is interesting, and we want to get in! But we would also like an interactive environment that is easy to set up and easy to play with, without a loss of power or performance.
This time, we look at a few important variants of the least squares problem. Least squares with equality constraints, and generalized least squares. They show how we can solve problems where there is some norm to be minimized with additional constraints to be satisfied.
A very cool and useful thing to know. How to solve linear systems that have many solutions, or those that have no solutions at all? That's the theme for a thick math textbook, of course, but from the programmer's point of view, we are interested in the practical matters, so I'll stick to the main point.
When I have less equations than there are unknowns, or I have too many equations, what can I do in Clojure to make those unknowns known? Which functions do I call? Today's article is a short one. Not because Singular Value Decomposition SVD is not important, but because it is so ubiquitous that we'll touch it in other articles where appropriate. The goal of this article is to give an overview and point to Neanderthal's functions that work with it.
When you see SVD in the literature you read, you'll know where to look for the implementation; that's the idea. The time spent over the summer on refactoring the foundations for 0.
It has been much easier for me to add many new features and polish the old ones. And the best news is that I expect this to continue giving for the upcoming releases. In the last article we have learned to solve general linear systems, assuming that the matrix of coefficients is square, dense, and unstructured. We have also seen how computing the solution is much faster and easier when we know that the matrix is triangular. These are pretty general assumptions, so we are able to solve any well-defined system.
We now explore how additional knowledge about the system can be applied to make it faster. The properties that we are looking for are symmetry, definiteness, and bandedness. Solving systems of linear equations is a staple food of linear algebra. It can be applied as a part of many machine learning tasks, although it is not always obvious to spot the opportunity. Here, we explore how triangular systems are the foundation that we need to internalize well.
By becoming a patron, you'll instantly unlock access to 3 exclusive posts. Recent posts by Dragan Djuric. How it works. Choose a membership. Add a payment method. Patreon logo. Language: English United States. Currency: EUR. What is Patreon?
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