#deeplearnjs resultados da pesquisa

🚀 DeepSeek-OCR — the new frontier of OCR from @deepseek_ai , exploring optical context compression for LLMs, is running blazingly fast on vLLM ⚡ (~2500 tokens/s on A100-40G) — powered by vllm==0.8.5 for day-0 model support. 🧠 Compresses visual contexts up to 20× while keeping…

bro created an entire 16-hour free youtube playlist on how to build a DeepSeek model from scratch. it goes over the papers, explains the theory, and implements the code. Syllabus: → attention mechanism fully explained → multi-head latent attention → grouped query attention…

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🚨 DeepSeek just did something wild. They built an OCR system that compresses long text into vision tokens literally turning paragraphs into pixels. Their model, DeepSeek-OCR, achieves 97% decoding precision at 10× compression and still manages 60% accuracy even at 20×. That…

DeepSeek-OCR looks impressive, but its core idea is not new. Input “Text” as “Image” — already explored by: LANGUAGE MODELING WITH PIXELS (Phillip et al., ICLR 2023) CLIPPO: Image-and-Language Understanding from Pixels Only (Michael et al. CVPR 2023) Pix2Struct: Screenshot…

BOOOOOOOM! CHINA DEEPSEEK DOES IT AGAIN! An entire encyclopedia compressed into a single, high-resolution image! — A mind-blowing breakthrough. DeepSeek-OCR, unleashed an electrifying 3-billion-parameter vision-language model that obliterates the boundaries between text and…

DeepSeek finally released a new model and paper. And because this DeepSeek-OCR release is a bit different from what everyone expected, and DeepSeek releases are generally a big deal, I wanted to do a brief explainer of what it is all about. In short, they explore how vision…

This is the JPEG moment for AI. Optical compression doesn't just make context cheaper. It makes AI memory architectures viable. Training data bottlenecks? Solved. - 200k pages/day on ONE GPU - 33M pages/day on 20 nodes - Every multimodal model is data-constrained. Not anymore.…

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DeepSeek released an OCR model today. Their motivation is really interesting: they want to use visual modality as an efficient compression medium for textual information, and use this to solve long-context challenges in LLMs. Of course, they are using it to get more training…

A more serious thread on the DeepSeek-OCR hype / serious misinterpretation going on. 1. On token reduction via representing text in images, researchers from Cambridge have previously shown that 500x prompt token compression is possible (ACL'25, Li, Su, and Collier). Without…

Unlike closed AI labs, DeepSeek proves they are truly open research Their OCR paper treats paragraphs as pixels and is 60x leap more efficient than traditional LLMs Small super efficient models are the future

DeepSeek-OCR is out! 🔥 my take ⤵️ > pretty insane it can parse and re-render charts in HTML > it uses CLIP and SAM features concatenated, so better grounding > very efficient per vision tokens/performance ratio > covers 100 languages

Most people still confuse about Machine Learning and Deep Learning. Here’s the real difference 👇 ML → Learns patterns from data but needs humans to extract features manually. DL → Learns features + patterns automatically using multi-layered neural networks.

Daily Deep Learning! Time ~ 8:44 > Convolutions with Multiple Input / Output Channels > 1x1 Convolutions as Dimensionality Reduction > MaxPool, AvgPool, StochasticPool, FractionalMaxPool > More Eigenvalues/Eigenvectors/Eigendecomposition > SVD, A = UΣVᵀ

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New machine learning system ( NL= nested learning ) optimize memory n tokens, new skills acquirement without lost of former skills research.google/blog/introduci…

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DeepSeek-OCR Contexts Optical Compression