deep-diamond
tech.ml.dataset
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deep-diamond | tech.ml.dataset | |
---|---|---|
16 | 15 | |
414 | 626 | |
1.0% | 1.6% | |
7.6 | 8.8 | |
about 1 month ago | 17 days ago | |
Clojure | Clojure | |
Eclipse Public License 1.0 | Eclipse Public License 1.0 |
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:
tech.ml.dataset
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A Tablecloth talk by Mey Beisaron at Func Prog Sweden this week
Tablecloth by generateme is a friendly & expressive table-processing library built on top of tech.ml.dataset & dtype-next, Chris Nuernberger's high-performance data libraries.
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Best Data Tools for my use case
For 1: This ns of tech.ml.dataset supports reading of multiple worksheets per file https://github.com/techascent/tech.ml.dataset/blob/master/src/tech/v3/libs/fastexcel.clj
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Ham-Fisted - A New High Performance Clojure Library
After building tech.ml.dataset and charred I wanted to take the lessons learned there and apply them back into the base Clojure substrate of persistent maps, persistent vectors, and algorithmic primitives like group-by and frequencies.
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A data science course for Clojurians – are you interested?
Did you try tech.ml.dataset?
- Why Clojure is not widely adopted like mainstream languages?
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Rewrite Your Scripts In LISP - with Roswell
Checkout babashka for scritping and clj-python to use numpy from clojure, or https://github.com/techascent/tech.ml.dataset for pure clojure
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Notebooks suck: change my mind
Really high quality libraries for deep learning, dataset manipulation, and more
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Clojure High Performance Data Processing Updates
tech.ml.dataset has seen some major upgrades for discoverability - specifically the tech.ml.dataset main namespace has been revamped. If you use Cursive or Calva your intellisense will now work with the main namespaces.
- LLVM!
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Announcement of first beta version of new Clojure machine learning library, scicloj.ml
It is based on a state-of-the art , high performance tabular dataset implementation, tech.ml.dataset and combines it with a innovative pipeline approach build with idiomatic and functional Clojure concepts in mind. Machine Learning models get pulled in as plugins from existing ecosystems, so are available from the start. Please find user guides and example code in GitHub at https://github.com/scicloj/scicloj.ml
What are some alternatives?
tablecloth - Dataset manipulation library built on the top of tech.ml.dataset
dtype-next - A Clojure library designed to aid in the implementation of high performance algorithms and systems.
geni - A Clojure dataframe library that runs on Spark
clerk - ⚡️ Moldable Live Programming for Clojure
hanami - Interactive arts and charts plotting with Clojure(Script) and Vega-lite / Vega. Flower viewing 花見 (hanami)
libpython-clj - Python bindings for Clojure
neanderthal - Fast Clojure Matrix Library
compare_gan - Compare GAN code.
clojisr - Clojure speaks statistics - a bridge between Clojure to R
geni-performance-benchmark
scicloj.ml - A Clojure machine learning library
mmaction2 - OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark