Find similar content

http://mdp.cdm.depaul.edu/DePy2016 3rd Annual #Conference on #Python applications in #Data_Analysis, #Machine_Learning, and Web May 6, 7 DePaul University - Room LL105 14 E Jackson Blvd Chicago IL 60604, USA

A team of ex-OpenAI fellows at Together have released a 20B chat-GPT model, fine-tuned for chat using EleutherAI's GPT-NeoX-20B, with over 43 million instructions under the Apache-2.0 license. https://github.com/togethercomputer/OpenChatKit https://www.together.xyz/blog/openchatkit #nlp

Haystack • Ask questions in natural language and find granular answers in your documents. • Perform semantic search and retrieve documents according to meaning, not keywords. • Use off-the-shelf models or fine-tune them to your domain. • Use user feedback to evaluate, benchmark, and continuously improve your live models. • Leverage existing knowledge bases and better handle the long tail of queries that chatbots receive. • Automate processes by automatically applying a list of questions to new documents and using the extracted answers. https://github.com/deepset-ai/haystack #nlp

为什么 ChatGPT API 是革命性的？ 这几天读了读 ChatGPT API 的文档，太惊喜了： 1. 最新版的 API 是基于 gpt-turbo-3.5 的，这一版的 API 的交互是革命性的。得益于模型的强大，用户不需要提交各种参数，只要写 prompt 就行。也就是说 API 的 UX 被大大简化。用户不需要在请求里写参数，只要在 prompt 里写人话，模型自行能够明白用户的表达。 2. 更厉害的是，gpt 这类模型可以接受 chain of thoughts (COT) 的 prompt，如果用户觉得结果不满意，可以继续提交请求让模型生成更好的答案。在李宏毅的讲座里，他给出了一个例子就是，如果让模型直接解答一个复杂的数学题，效果可能不是很好，但是加上 let’s do it step by step 的 prompt 之后，模型给出了一步步的推导过程，结果大为改善。 3. 除了直接调用 ChatGPT API 的基础模型以外，OpenAI 还提供了让用户提交自己的 embedding 和 fine-tuning 等定制模型的方式，这两种都可以通过 API 来实现，不需要额外的步骤。不过，最新的 API 暂时不支持 fine-tuning 4. 以前随便开发一个 NLP 的模型，基本上开发周期是以月计算的，有了 ChatGPT API 之后，抛去准备数据的时间，开发周期可以以小时计算。我从零开始开始读文档，到写出一个 Q&A 生成的项目，只花了半天时间。放在以前，至少要花一两个月的时间吧。 #nlp

https://www.analyticsvidhya.com/learning-paths-data-science-business-analytics-business-intelligence-big-data/learning-path-data-science-python/ Comprehensive learning path – #Data_Science in Python Journey from a Python noob to a Kaggler on Python So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for #data_analysis. If you already have some background, or don’t need all the components, feel free to adapt your own paths and let us know how you made changes in the path. You can also check the mini version of this learning path #Deep_Learning

http://www.paulbrownmagic.com/blog/vslambda Python has support for #lambda functions, Haskell is built upon lambda calculus. The two are not the same and this is the reason why lambda should have been removed in #Python3. This post examines the differences, reviews the use in Python, and offers a more pythonic, honest syntax. #learn

https://staltz.com/djangoconfi-mongoengine/#/ using #MongoDB as your primary #Django database, #python3. #learn#example

Version 3.10 of the legendary programming language is now here: https://www.python.org/downloads/release/python-3100 No rush to update, though. #Python

#Python is the main language of data science, per this analysis on 10M Jupyter Notebooks: https://blog.jetbrains.com/datalore/2020/12/17/we-downloaded-10-000-000-jupyter-notebooks-from-github-this-is-what-we-learned/

https://simpleisbetterthancomplex.com/2015/11/23/small-open-source-django-projects-to-get-started.html Small Open-Source Django Projects to Get Started Learning #Django and #Python can be very fun. I personally love programming with Python and for the most part, work with the Django framework. But in the beginning some stuff can be confusing, especially if you are coming from a Java or C♯ background, like me.