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Embeddings (and how to create them) are, perhaps, the most interesting idea behind Large Language Models. I built a simple model to help you understand embeddings from scratch. Here is a step-by-step video explanation:

Santiago

438,377 subscribers

44,107 次观看 • 1 年前 •via X (Twitter)

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Santiago 的头像

Santiago1 年前

Here is the same video, but now on YouTube: And here is the code I used in the video:

Matt Figdore 的头像

Matt Figdore2 年前

This is the biggest productivity cheat code right now. Kiss reading documents goodbye. You can get an instant summary of any document with this tool.

Joao Marcos 的头像

Joao Marcos1 年前

@PLynchado

Sumanth 的头像

Sumanth1 年前

Impressive work Santiago. Thanks for the detailed tutorial!

Mohd Shadab Khan 的头像

Mohd Shadab Khan1 年前

How do embeddings improve AI’s reasoning? Can this method work beyond text, like for images/videos?

Santiago 的头像

Santiago1 年前

Yes, this works for images/videos. In fact, in this example, I create embeddings for images.

Foreign Policy Expert 的头像

Foreign Policy Expert1 年前

Do embeddings matter as much anymore? We’ve got smolagents, better instruct models, and bigger contexts that allow for better hierarchical search and broader contextual understanding? Embedding search is cheaper, sure, but if the docs change over time don’t the embeddings “fail”

Ray | AI marketer - Social Media Assistant 的头像

Ray | AI marketer - Social Media Assistant1 年前

How do you see embeddings improving the efficiency of your model? They can really help in capturing semantic relationships, which is crucial for better understanding context in AI.

Uncle Drei 的头像

Uncle Drei1 年前

@memdotai meme this

scuzzlebot 的头像

scuzzlebot1 年前

Great educational content on embeddings! These fundamental concepts are crucial for understanding how modern AI systems work. Looking forward to the video explanation.

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The most clearest and crisp explanation, I've ever heard, of how large language models compress and capture a "world-model" in their weights simply by learning to predict the next word accurately. Furthermore, how the raw power of these base models can then be tamed by teaching them to follow instructions from humans. Source:

The most clearest and crisp explanation, I've ever heard, of how large language models compress and capture a "world-model" in their weights simply by learning to predict the next word accurately. Furthermore, how the raw power of these base models can then be tamed by teaching them to follow instructions from humans. Source:

Zain

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SceneScript treats 3D reconstruction as a language problem rather than a geometry one. The model watches a video of a room and just learns to write a script for it. It autoregressively spits out text commands like make_wall(...) or make_bbox(...) that define the scene. Stanford's new "Scene Language" paper goes a step further adding CLIP embeddings to capture visual appearance too. The fact that language models already understand spatial relationships well enough to write out scene graphs is pretty wild.

SceneScript treats 3D reconstruction as a language problem rather than a geometry one. The model watches a video of a room and just learns to write a script for it. It autoregressively spits out text commands like make_wall(...) or make_bbox(...) that define the scene. Stanford's new "Scene Language" paper goes a step further adding CLIP embeddings to capture visual appearance too. The fact that language models already understand spatial relationships well enough to write out scene graphs is pretty wild.

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[1/n] Do distinct large models admit a simple map that aligns their embedding spaces? We show that across multimodal contrastive models—trained on different data and architectures—an orthogonal map aligns image embeddings. Strikingly, the same map also aligns text embeddings.

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CJ Zafir

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building large language models from scratch by Sebastian Raschka was a great chance for me to sit down and study again all the LLM basics > token and positional embeddings > self-attention and what QKV is about > causal & multi-head attention studying llms and how they work can seem overwhelming at first. but once you taste how good it feels to learn these things intuitively there's no going back. I shared the resources and my notes on my repo and I hope it's a motivation if you want to start as well or recap.

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ℏεsam

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Why Ticks are very difficult to remove...but a simple step from the video will help you easily 🤗

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596,117 次观看 • 2 年前

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Here is a killer way to use embeddings: I built a notebook to show you how to do anomaly detection on images. Anomaly detection is one of the most common problems in the real world. Learn how to implement anomaly detection, and you'll be able to solve a ton of use cases that happen every day: • Predicting equipment failures • Identifying defective products • Identifying tumors in medical images • Spotting counterfeit products • Detecting fraud • Flagging fake reviews • Detecting bot traffic My notebook focuses on images. It uses: • Langchain • OpenClip embeddings • Oracle vector store This is the way my example works: • You start with a collection of similar images • You can then check whether a new image "belongs" to that collection I generate embeddings and use Oracle 26ai to store them and retrieve similar images on demand.

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