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http://www.aparat.com/v/0scM5 Irene Chen A Beginner's Guide to Deep Learning. What is #Deep_Learning ? It has recently exploded in popularity as a complex and incredibly powerful tool. This talk will present the basic concepts underlying deep learning in understandable pieces for complete beginners to #machine_learning.

http://www.aparat.com/v/Corus Advanced users #Deep_Learning, anyone who has followed #machine_learning over the past years has heard it. In this talk I will go past the hype and show what deep learning actually means and how one goes about solving complex machine learning task with a minimum amount of code, with the help of theano, an amazing python library for deep learning.

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

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://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 Also, technology becomes outdated — fast.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.

A Hackers' guide to language models. #video#llm#ml#ai @thedevs https://thedevs.link/nWHcWR

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

https://twitter.com/aieleuther/status/1633971388317941763?s=46 #ml

各家公司 A100 显卡的囤货情况 https://twitter.com/abacaj/status/1634069822764900353 #ml

GPT-4 预计下周发布，是个多模态模型，包括处理视频数据的能力。 https://www.heise.de/news/GPT-4-is-coming-next-week-and-it-will-be-multimodal-says-Microsoft-Germany-7540972.html #ml

抽时间读了读 openAI API 的使用文档和样例代码，发现这些 API 的威力远比在 ChatGPT 前台展示得要大得多。 感觉ChatGPT API 的开放对于 NLP 任务而言，就像十年前 AlexNet 的出现对于 CV 任务的推动差不多。 ChatGPT 当然不是全知全能，但是如何好好利用 ChatGPT （比如 fine tuning）可能是所有开发者都需要好好学习的 https://platform.openai.com/examples https://github.com/openai/openai-cookbook #ml

huggingface 发布了基于 javascript 和 typescript 的框架，可以用这两种语言直接调用 huggingface hub 的 API 。 https://github.com/huggingface/huggingface.js #ml