CPU vs GPU vs TPU

Introduction to GPU

GPUs are fast because they have high-bandwidth memories and hardware that performs floating-point arithmetic at significantly higher rates than conventional CPUs

Processing large blocks of data is basically what Machine Learning does, so GPUs come in handy for ML tasks. TensorFlow and Pytorch are examples of libraries that already make use of GPUs. Now with the RAPIDS suite of libraries, we can also manipulate data frames and runmachine learning algorithms on GPUs as well.

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Randomized Optimization in Machine Learning

Algorithm of the Week: Support Vector Machine

A different approach inspired by Neural ODEs — Extrapolation of Neural Networks.

Here is a simple sin function. The neural network is trained in T=(-4,4) for 100 points , and the green line is the model for 1000 points in (-4,10). We see that it is perfect in T, but outside T says nothing, and so it is not accurate for extrapolation out of t=4.

Glow: Graph Lowering Compiler Techniques for Neural Network

Gleaning Insight from Content with IBM Watson

The Markov Chain

Using fastai’s callbacks for debugging your model

Reading The Markets — Machine Learning Versus The Financial News

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Society of AI

Society of AI

Society of AI has an vision to educate people how Artificial Intelligence can change their life!

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