Adding support for polynomials to Numba https://
bit.ly/3N0MZog
#AI #MachineLearning #DeepLearning #LLMs #DataScience
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Numba Adds Polynomial Support for Machine Learning
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5 Essential Steps to Building Language Models Applications
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5 Essential Steps to Building Language Models Apps Dive into the world of Large Language Models (LLMs) with the essential steps to build, refine, and deploy AI-powered applications. Learn about selecting the right LLM, tailoring it to your needs, evaluating its performance,
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ChatGPT vs OpenAI API Assistants: Key Differences Explained
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Curious about the different between @ChatGPTapp GPT's and Assistants via the @OpenAI API? This article goes into the differences and when you would want one vs the other.
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DensePose and HR-VITON Implementation Guide for Image Synthesis
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This has details on how to run DensePose with what looks like the same styled outputs: https://
github.com/sangyun884/HR-
VITON/issues/45
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Switching DensePose ControlNet to OpenPose for Image Generation
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It looks like they use a densepose controlnet, I wonder if you could switch it out to an openpose one.
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Methods for studying implementing training building AI applications
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How are you studying/implementing/training/building AI apps?
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GPU Programming and Transformers: Essential ML Systems Knowledge
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fewer than 100 people deeply understand both (i) transformers and (ii) the GPU programming model want to learn machine learning? gain some esoteric systems knowledge; spend some time really learning CUDA
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Building RAG Pipelines with Multiple LLMs
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Building RAG Pipelines that Use Multiple LLMs: Query Transformation Many advanced RAG pipelines make multiple calls to LLMs at various stages. Most existing templates currently use the same LLM for all stages, but there's no reason that has to be the case. Over the coming
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LangSmith Tutorials: Tracing, Evaluation, and Data Annotation
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LangSmith Tutorials We've added a lot of functionality to LangSmith! If you want to get caught up on it, we've added a 7 part YouTube series covering: Tracing
Evaluation
Hub
Data Annotation
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Python’s Role in Data Science and Machine Learning Infrastructure
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Because for most of the Data Science, ML, and AI use cases almost all of the important stuff happens behind the scenes in highly optimized C, C++, Rust, and CUDA libraries. And these use cases is where Python has by now entrenched itself.