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https://www.udemy.com/machinelearning/learn/v4/content #machine_learning A-Z™: Hands-On #Python & R In #Data_Science

https://www.kaggle.com/ The Home of #Data_Science & #Machine_Learning Kaggle helps you learn, work, and play

http://www.kdnuggets.com/2017/09/essential-data-science-machine-learning-deep-learning-cheat-sheets.html#.WdGzWthHcEo.linkedin 30 Essential #Data_Science , #machine_learning & #Deep_Learning Cheat Sheets

https://github.com/scikit-learn/scikit-learn #scikit_learn is a Python module for #machine_learning built on top of #SciPy and distributed under the 3-Clause BSD license. The project was started in 2007 by David Cournapeau as a #Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.

http://contrib.scikit-learn.org/imbalanced-learn/ In an ideal world, we would have perfectly balanced datasets and we would all train models and be happy. Unfortunately, the real world is not like that, and certain tasks favor very imbalanced data. For example, when predicting fraud in credit card transactions, you would expect that the vast majority of the transactions (+99.9%?) are actually legit. Training #ML(#machine_learning) algorithms naively will lead to dismal performance, so extra care is needed when working with these types of datasets. #Imbalanced-learn is a Python package which offers implementations of some of those techniques, to make your life much easier.

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf #Scikit_learn is an open source Python library that implements a range of #machine_learning , preprocessing, cross-#validation and #visualization algorithms using a unified interface...

https://hackernoon.com/absolute-fundamentals-of-machine-learning-dca5deee78df?gi=2c99287cb9f5 #machine_learning , what a buzzword. I’m sure you all want to understand machine learning, and that’s what I’m going to teach in this article. I found that learning the theroetical side alongside the programming side makes it easier to learn both, so this article features both easy to understand mathematics and the algorithms implemented in Python. Also, technology becomes outdated — fast. The code used in this tutorial will likely be meaningless in 5 years time. So for that reason, I’ve decided to also teach the mathematical side to Machine Learning that will not die out in a few years.

https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/ It can be difficult to install a #Python#machine_learning environment on some platforms. Python itself must be installed first and then there are many packages to install, and it can be confusing for beginners. In this tutorial, you will discover how to set up a Python machine learning development environment using #Anaconda.

http://www.kdnuggets.com/2017/09/essential-data-science-machine-learning-deep-learning-cheat-sheets.html #Cheat_Sheet, #Data_Science, #Deep_Learning, #Machine_Learning, #Neural_Networks, #Probability, #Python, R, #SQL, #Statistics This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools

在单机上可以跑得动 Meta 发布的 LLaMA 模型。 https://til.simonwillison.net/llms/llama-7b-m2 https://twitter.com/ggerganov/status/1634282694208114690 #ml

Unum 使用四块 RTX 3090 显卡就能训练一个比 OpenAI 还要好的 Vision-Language transformer 模型 -- UForm。要知道 OpenAI 用了 1024块 A100 显卡才训练出来。 UForm 不仅各方面表现优于 OpenAI 的 CLIP，而且推算速度也大大优于 CLIP。 对了，Unum 是一家在亚美尼亚的初创公司，目前只有13个人。 https://www.unum.cloud/blog/2023-02-20-efficient-multimodality #ml