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The most popular blog posts about ai & machine learning on Hacker News. 109 posts.
Preface The intended audience of this article is someone who knows something about Machine Learning and Artifical Neural Networks (ANNs) in particular and who recalls that fitting an ANN required a…
There's a lot going on behind the scenes, since I am not only writing the books; at the same time I have to create the software to write about. For example, ...
A great moment has arrived. We are going to apply our neural networks implementation to a regression problem. The network is going to learn a known function,...
The stage has been set for wrapping up the simplest version of a complete neural network API, and its key part that offers the entry for the /learning/ funct...
Gwern has answered my prayers and taught GPT-2 poetry. GPT-2 is the language processing system that OpenAI announced a few weeks ago. They are keeping the full version secret, but have released a s…
Neural networks involve long sequence of multiplications, usually between a matrix and a vector, say \(a*x\). The result of this sequence of multiplications will either have a huge magnitude or be reduced to 0. We can divide \(a\) by a number ( scaling_factor ) to scale down its magnitude to the right level. Proper…
We generalize the network code and run it on the GPU. On an Nvidia GPU with CUDA, and on an AMD GPU with OpenCL. Even more - we mix both CUDA and OpenCL, jus...
Gaussian Processes have a mystique related to the dense probabilistic terminology that's already evident in their name. But Gaussian Processes are just models, and they're much more like k-nearest neighbors and linear regression than may at first be apparent.
Redirecting to https://www.mukeshmithrakumar.com/blog/deep-learning-with-tf2-probability-and-information-theory/ .
Sharing and reusing memory buffers is inescapable if we want high performance. It is a sharp but powerful tool.
