5 Strategies for Acing Your AI Technical Interview: Introduction At this time, it’s very complex and challenging for anyone to get the desired position… https://
analyticsvidhya.com/blog/2024/03/a
i-technical-interview/?utm_source=dlvr.it&utm_medium=twitter
… #DataAnalytics #DataScience #DataDriven #BigData #AI #ArtificialIntelligence #BusinessIntelligence #ML
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Five Strategies for Acing Your AI Technical Interview
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Nostalgic Reflection on Past Project with Future Rewrite Plans
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oh my god blast from the past maybe one day i shall do a re-write of this project.
i am imagining efficient, batched training + inference running the brain of all the little bots… -
Cursor Copilot++ Gets 40% Speed Boost and Enhanced Capabilities
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We rolled out a major performance upgrade to Cursor's Copilot++ today! The same model ~40% faster now overall, and the performance is especially improved on small edits. Next up for Copilot++: more intelligence, more capabilities, and even more context.
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Major Data and AI Event: GenAI and Engineering Conference June 10-13
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POV: you would do anything to get into the biggest data and #AI event of the year.
— Databricks (@databricks) 19 avril 2024
Register to hear from leading experts, researchers, and open source contributors about topics like #GenAI and data engineering and streaming.
Join us June 10-13: https://t.co/Rl8WajxnNn pic.twitter.com/x2fNrVOgAJPOV: you would do anything to get into the biggest data and #AI event of the year. Register to hear from leading experts, researchers, and open source contributors about topics like #GenAI and data engineering and streaming. Join us June 10-13: https://
bit.ly/3UlaDA7 -
GPT-2 Weight Initialization and Fine-tuning Strategy
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atm we're doing init from gpt-2 weights and finetuning. this was very useful for debugging and when the code was slower. there is no code yet to init from scratch, so no code to warmup the lr etc. should be a very short addition though.
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Lack of Public Documentation in AI Implementation Practices
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That sounds like it would work in theory, I'd love to see documented examples with live demos of people doing that in practice I get the impression that people who do this rarely share their results publicly, which is a shame
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Synthesizing AI Responses with RAG Pipelines and SFT Models
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It can be done: Gather any available real query examples, add in synthetic Q’s derived from docs. Synthesize responses using RAG pipeline. SFT (a smaller model) on resulting Q&A’s, without RAG context. Whether it saves any money is another question.
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Fine-tuning GPT-3.5: Use Cases and Community Experiences
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Using https://
platform.openai.com/docs/guides/fi
ne-tuning
… ? What have you used that for – I've not heard many examples of people fine-tuning 3.5 -
PyTorch Support and llm.c Project Development Status
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Part agree! I love PyTorch ofc. But also llm.c is a ~2 week old project that is worked on by ~3 people as a hobby in spare time.
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torch.compile uses Triton kernels under the hood for optimization
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So if you're using torch.compile you're already using a lot of triton under the hood, afaik PyTorch picks and chooses whether to call cuda kernels or triton for different ops / settings. Triton is really awesome, but of course you're staying in the Python / torch universe. Which