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Hopefully this sheds light on this important topic in a way that is different than other approaches and provides the clarity needed to understand Transformer architecture. It starts at 41:22 in the below video.
* Historical context for LLMs and GenAI
* Training an LLM -- 100K overview
* What does an LLM learn during training?
* Inferencing an LLM -- 100K overview
* 3 steps in the LLM journey from pre-training to serving
* Word Embeddings -- representing text in numeric format
* RMS Normalization -- the sound engineer of the Transformer
* Benefits of RMS Normalization over Layer Normalization
* Rotary Position Encoding (RoPE) -- making the Transformer aware of token position
* Masked Self-Attention -- making the Transformer understand context
* How RoPE generalizes well making long-context LLMs possible
* Understanding what Causal Masking is (intuition and benefit)
* Multi-Head Attention -- improving stability of Self Attention
* Residual Connections -- improving stability of learning
* Feed Forward Network
* SwiGLU Activation Function
* Stacking
* Projection Layer -- Next Token Prediction
* Inferencing a Large Language Model
* Step by Step next token generation to form sentences
* Perplexity Score -- how well did the model does
* Next Token Selector -- Greedy Sampling
* Next Token Selector -- Top-k Sampling
* Next Token Selector -- Top-p/Nucleus Sampling
* Temperature -- making an LLM's generation more creative
* Instruction finetuning -- aligning an LLM's response
* Learning going forward
Show HN: Comprehensive and Intuitive Introduction to Deep Learning
Dear HN -- I am doing a multi-part web series on teaching AI, specifically Deep Learning. Learning AI is not easy. The topics are highly technical and cover a broad range of areas without offering any intuitive or clear entry point for someone new to the field. I find that most tutorials focus on heavy math making them inaccessible to most people; or treat topics only superficially; or cover...
We found Coverity to be prohibitively expensive; but unfortunately there isn't any real alternative!
The only paper on this data structure seems to be: http://goo.gl/CVF1l
This paper is poorly written and quite honestly not useful to implement an LSM tree. Does anyone know of a better paper than this one?
Book on Parallel Programming
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Ask YC: nginx or apache for PHP?
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