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Source channel @githubtrending · Post #15168 · Sep 25

#python#ai#context#embedded#faiss#knowledge_base#knowledge_graph#llm#machine_learning#memory#nlp#offline_first#opencv#python#rag#retrieval_augmented_generation#semantic_search#vector_database#video_processing Memvid lets you store millions of text pieces inside a single MP4 video file using QR codes, making your data 50-100 times smaller than usual databases. You can search this video instantly in under 100 milliseconds without needing servers or internet after setup. It works offline, is easy to use with simple Python code, and supports PDFs and chat with your data. The upcoming version 2 will add features like continuous memory updates, shareable capsules, fast local caching, and better video compression, making your AI memory smarter, faster, and more flexible. This means you get a powerful, portable, and efficient way to manage and search huge knowledge bases quickly and easily. https://github.com/Olow304/memvid

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10 similar posts found

djangoproject

@djangoproject · Post #584 · 03/22/2018, 11:01 AM

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 Also, technology becomes outdated — fast.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.

DPS Build

@dps_build · Post #52 · 03/12/2023, 11:07 AM

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

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DPS Build

@dps_build · Post #51 · 03/12/2023, 03:50 AM

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

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DPS Build

@dps_build · Post #49 · 03/11/2023, 11:33 PM

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

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djangoproject

@djangoproject · Post #445 · 09/17/2017, 01:01 AM

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.

djangoproject

@djangoproject · Post #230 · 01/16/2017, 01:42 PM

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.

djangoproject

@djangoproject · Post #229 · 01/16/2017, 01:41 PM

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.

djangoproject

@djangoproject · Post #525 · 12/18/2017, 02:05 PM

https://www.python-course.eu/machine_learning.php Tutorial and Online Course #machine_learning machine learning: robot jugglers This is a completely new and incomplete chapter of our tutorial! We started work in January 2017! #learn