hyperlearn
python-machine-learning-book
Our great sponsors
| hyperlearn | python-machine-learning-book | |
|---|---|---|
| 4 | 2 | |
| 1,510 | 12,076 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| over 1 year ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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.
hyperlearn
-
80% faster, 50% less memory, 0% accuracy loss Llama finetuning
I agree fully - what do you suggest then? OSS the entire code base and using AGPL3? I tried that with https://github.com/danielhanchen/hyperlearn to no avail - we couldn't even monetize it at all, so I just OSSed everything.
I listed all the research articles and methods in Hyperlearn which in the end were gobbled up by other packages.
We still have to cover life expenses and stuff sadly as a startup.
Do you have any suggestions how we could go about this? We thought maybe an actual training / inference platform, and not even OSSing any code, but we decided against this, so we OSSed some code.
Ay suggestions are welcome!
-
80% faster, 50% less memory, 0% loss of accuracy Llama finetuning
Good point - the main issue is we encountered this exact issue with our old package Hyperlearn (https://github.com/danielhanchen/hyperlearn).
I OSSed all the code to the community - I'm actually an extremely open person and I love contributing to the OSS community.
The issue was the package got gobbled up by other startups and big tech companies with no credit - I didn't want any cash from it, but it stung and hurt really bad hearing other startups and companies claim it was them who made it faster, whilst it was actually my work. It hurt really bad - as an OSS person, I don't want money, but just some recognition for the work.
I also used to accept and help everyone with their writing their startup's software, but I never got paid or even any thanks - sadly I didn't expect the world to be such a hostile place.
So after a sad awakening, I decided with my brother instead of OSSing everything, we would first OSS something which is still very good - 5X faster training is already very reasonable.
I'm all open to other suggestions on how we should approach this though! There are no evil intentions - in fact I insisted we OSS EVERYTHING even the 30x faster algos, but after a level headed discussion with my brother - we still have to pay life expenses no?
If you have other ways we can go about this - I'm all ears!! We're literally making stuff up as we go along!
-
[Project] BFLOAT16 on ALL hardware (>= 2009), up to 2000x faster ML algos, 50% less RAM usage for all old/new hardware - Hyperlearn Reborn.
Hello everyone!! It's been a while!! Years back I released Hyperlearn https://github.com/danielhanchen/hyperlearn. It has 1.2K Github stars, where I made tonnes of algos faster:
python-machine-learning-book
- Can you recommend a Python textbook to replace "An Introduction to Statistical Learning with Applications in R", Witten, J. et. al. [E]
-
What is the purpose of meshgrid in Python / NumPy?
I am studying "Python Machine Learning" from Sebastian Raschka, and he is using it for plotting the decision borders. See input 11 here.
What are some alternatives?
gpt-fast - Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.
Time-series-classification-and-clustering-with-Reservoir-Computing - Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.
data-science-notes - Notes of IBM Data Science Professional Certificate Courses on Coursera
artificial-intelligence-and-machine-learning - A repository for implementation of artificial intelligence algorithm which includes machine learning and deep learning algorithm as well as classical AI search algorithm
notebooks - Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.
Neural-Network-Steganography - Hide some secret 😎 data in a Neural Network - text, malicious software or watermark your NN
ocaml-torch - OCaml bindings for PyTorch
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
DiffSharp - DiffSharp: Differentiable Functional Programming
MLB-Pitch-Identification-with-ML - Identify the name of a pitch being thrown using a machine learning model.
MegEngine - MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架
weightless_NN_decompression - Proof of concept for neural network decompression without storing any weights