4/ Time series analysis and forecasting
MACHINE LEARNING
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How to Build NLP Products: Step-by-Step Guide
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5/ Build NLP products https://
medium.com/@avikumart_/ho
w-to-build-nlp-products-a-step-by-step-guide-a476bf47d280
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Top 7 Best Data Science and Machine Learning Articles
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I have shared many data science and machine learning articles over the period of the past 1 and a half years Here are the TOP 7 Best articles I have published so far, (And you shouldn't miss them)
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Top 10 In-Demand Skills For 2030
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The Top 10 In-Demand Skills For 2030 The #future of #work is rapidly #changing. Here we look at the #skills that will be in most demand by employers in #2030.
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Aligning Text-to-Image Models Using Human Feedback
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9/ Aligning Text-to-Image Models using Human Feedback – proposes a fine-tuning method to align generative models using human feedback.
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MERF: Memory-Efficient Radiance Field for Browser-Based View Synthesis
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10/ MERF – a memory-efficient radiance field representation for real-time view synthesis of large-scale scenes in a browser.https://t.co/o4dpMgDVLh
— DAIR.AI (@dair_ai) 26 février 202310/ MERF – a memory-efficient radiance field representation for real-time view synthesis of large-scale scenes in a browser.
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Modular Deep Learning Survey: Building Blocks Neural Networks
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5/ Modular Deep Learning – a survey offering a unified view of the building blocks of modular neural networks; it also includes a discussion about modularity in the context of scaling LMs, causal inference, and other key topics in ML.
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Recitation-Augmented LMs Enhance Knowledge-Intensive Task Performance
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6/ Recitation-Augmented LMs – an approach that recites passages from the LLM’s own memory to produce final answers; shows high performance on knowledge-intensive tasks.
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Top ML Papers Week: LLaMA, Diffusion Models, Code Optimization
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Top ML Papers of the Week (Feb 20-26): – LLaMA (65B params foundation model)
– Composer (5B params diffusion model)
– Active-Prompt
– Modular Deep Learning
– Memory-Efficient Radiance Fields
– LLMs to Optimize Code … -

LLaMA 65B: Meta’s Open Source Model Outperforms GPT-3
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1/ LLaMA – a 65B parameter foundation model released by Meta AI; relies on publicly available data and outperforms GPT-3 on most benchmarks despite being 10x smaller.