deep-diamond
notespace
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deep-diamond | notespace | |
---|---|---|
16 | 3 | |
414 | 145 | |
1.0% | 0.7% | |
7.6 | 3.2 | |
about 1 month ago | 3 months ago | |
Clojure | Clojure | |
Eclipse Public License 1.0 | Eclipse Public License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
deep-diamond
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LLaMA-rs: Run inference of LLaMA on CPU with Rust 🦀🦙
I had some "classical ML" knowledge and knew a bit about the math behind DL and tensors in general thanks to the book Deep Learning for Programmers showcased in this repo: https://github.com/uncomplicate/deep-diamond (it's not in Rust, and I'm not sure what the current state of it is, though!).
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I want to quit my data analyst job and learn and become a Clojure developer
Do clojure as a side gig or in free time. Let day job pay the bills. If you can, maybe incorporate clojure into work job to solve small problems (https://github.com/clj-python/libpython-clj and https://github.com/scicloj/clojisr provide bridges to/from python and r). There is a lot of effort going into the data science side as well; the scicloj effort has resulted in a lot of growth over the last 2 years. tech.ml.dataset, tech.ml (now scicloj.ml). Dragan has a bunch of excellent stuff in neanderthal and deep diamond. There are also bindings to other jvm libraries from multiple languages.
- LLVM!
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Applications of Deep Neural Networks [pdf]
If I may drop in with a bit of shameless self-promotion.
My "Deep Learning for Programmers: A Tutorial with CUDA, OpenCL, DNNL, Java, and Clojure" book explains and executes every single line of code interactively, from low level operations to high-level networks that do everything automatically. The code is built on the state of the art performance operations of oneDNN (Intel, CPU) and cuDNN (CUDA, GPU). Very concise readable and understandable by humans.
https://aiprobook.com/deep-learning-for-programmers/
Here's the open source library built throughout the book:
https://github.com/uncomplicate/deep-diamond
Some chapters from the beginning of the book are available on my blog, as a tutorial series:
notespace
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Markdown Literary Programming with live preview for Clojure
There is also another project which can be described as a notebook in your favourite editor with live view. The main benefit is evaluation during doc generation and (almost*) no difference between the namespace and the notebook. Here is the project: https://github.com/scicloj/notespace
- LLVM!
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Clojure High Performance Data Processing System
Getting off topic a bit but for a REPL/notebook hybrid notespace is really interesting.
What are some alternatives?
tech.ml.dataset - A Clojure high performance data processing system
neanderthal - Fast Clojure Matrix Library
compare_gan - Compare GAN code.
clojisr - Clojure speaks statistics - a bridge between Clojure to R
scicloj.ml - A Clojure machine learning library
mmaction2 - OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
tablecloth - Dataset manipulation library built on the top of tech.ml.dataset
Beagle - Beagle helps you identify keywords, phrases, regexes, and complex search queries of interest in streams of text documents.
waqi - REPL-driven data visualizations with Clojure and Vega/Vega-Lite in the browser
geni - A Clojure dataframe library that runs on Spark