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βοΈ Link to the textbook
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We found the textbook Mathematics for Computer Science β covering the mathematics that underlies algorithms and computer science.
Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures β all in one place.
βοΈ Link to the textbook
https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdf
https://t.iss.one/CodeProgrammer
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Forwarded from Machine Learning
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> GPU memory and profiling
> Breaking down computations into blocks, kernel fusion, and FlashAttention
> Data parallelism, tensor parallelism, pipeline parallelism, and context parallelism
I've already read the free online version, but I still had to buy a physical copy for my library. π
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> GPU memory and profiling
> Breaking down computations into blocks, kernel fusion, and FlashAttention
> Data parallelism, tensor parallelism, pipeline parallelism, and context parallelism
I've already read the free online version, but I still had to buy a physical copy for my library. π
You can also read it for free on Hugging Face:
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Playlist: https://youtube.com/playlist?list=PLMrJAkhIeNNR20Mz-VpzgfQs5zrYi085m
The course is called Control Bootcamp.
It covers topics such as linear systems, stability and eigenvalues, controllability and observability, pole placement, the Kalman filter, LQR/LQG, robust control, and MPC β all explained sequentially with examples in Matlab.
Branton is the Boeing Professor of AI & Data-Driven Engineering at the University of Washington. He holds a bachelor's degree in mathematics from Caltech, with a specialization in control and dynamical systems, and a Ph.D. in mechanical and aerospace engineering from Princeton.
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This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."
It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.
https://github.com/Nicolepcx/transformers-the-definitive-guide
https://t.iss.one/MachineLearning9π€©
It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.
https://github.com/Nicolepcx/transformers-the-definitive-guide
https://t.iss.one/MachineLearning9
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372.3 KB
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If you fully understand this article, you will understand inference better than 90% of people.
And by the way, this is just the first material in the AI Performance Engineering repository.
It's scary to think how much knowledge is contained in the rest.
https://github.com/wafer-ai/gpu-perf-engineering-resources
And by the way, this is just the first material in the AI Performance Engineering repository.
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https://github.com/wafer-ai/gpu-perf-engineering-resources
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