3/ Searching for Best Practices in RAG – shows the best practices for building effective RAG workflows; proposes strategies that focus on performance and efficiency, including emerging multimodal retrieval techniques.
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HuggingFace Cache Storage Management Challenge
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Haha yeah I regularly need to flush my HF cache before it eats up all my storage space
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Delta Live Tables: Simplified Pipeline Development with DLT-Aware Features
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We introduced features to Delta Live Tables that can simplify pipeline development in Notebooks: Develop DLT pipelines in one single contextual UI Find syntax errors with "Validate" action Develop code faster with DLT-aware autocomplete https://
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Structured Generation Techniques for AI Model Control
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It looks like structured generation could do a lot of heavy lifting for you, have you tried it?
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Low-Level Numerics Debugging Friday Afternoon Challenge
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nothing like some low-level numerics debugging to enjoy a lazy friday afternoon
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Fine-tune Llama 3 for Customer Support Automation
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Looking for something to do this weekend? Learn how to fine-tune #llama3 to automate customer support! In this hands on #code tutorial, you'll learn how to take a small best-of-breed #opensource LLM and train it for your use case. https://
pbase.ai/3VT4bzN -
Scalar-based Backpropagation: Foundation for Tensor Derivatives
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“turned out that by only defining the derivatives for scalar values, it was sufficient to generalise to any higher dimensional Tensors. Therefore, I think building backpropagation intuition from the scalar valued perspective is extremely educational” Yep exactly. I think matrix
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Top AI Tools Handpicked by Editorial Team for Every Category
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Our editorial team has meticulously handpicked the top AI tools in each category to help you find the best solutions tailored to your needs. These elite tools are now showcased in the first row of our category pages. Discover them here today: https://
futurepedia.io/ai-tools -
Implementing _predict_single Method for k-NN Classification
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Finally a '_predict_single' method as mentioned it predicts the class label for a single data point This is done by: 1. Calculating the distance between one point to all training examples 2. Selecting the k-nearest Neighbours, & returning the most common class label among them
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Daily Content on Python Data Science Machine Learning and MLOps
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That's a wrap! If you are interested in any of these topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.