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Stanford CS236: Deep Generative Models I 2023 I Lecture 1 – Introduction
To follow along with the course, visit the course website:https://deepgenerativemodels.github.io/ Stefano ErmonAssociate Professor of Computer Science, Stanford Universityhttps://cs.stanford.edu/~ermon/ https://www.youtube.com/watch?v=XZ0PMRWXBEU
Backpropagation calculus | DL4
This one is a bit more symbol-heavy, and that’s actually the point. The goal here is to represent in somewhat more formal terms the intuition for how backpropagation works in part 3 of the series, hopefully providing some connection between that video and other texts/code that you come across later. For more on backpropagation:http://neuralnetworksanddeeplearning….https://github.com/mnielsen/neural-ne…http://colah.github.io/posts/2015-08-… https://colah.github.io/posts/2015-08-Backprop
Reliable, fully local RAG agents with LLaMA3.2-3b
LLaMA3.2 has released a new set of compact models designed for on-device use cases, such as locally running assistants. Here, we show how LangGraph can enable these types of local assistant by building a multi-step RAG agent – this combines ideas from 3 advanced RAG papers (Adaptive RAG, Corrective RAG, and Self-RAG) into a single…
Attention in transformers, visually explained | DL6
Demystifying attention, the key mechanism inside transformers and LLMs.
What are Transformer Models and how do they work?
This is the last of a series of 3 videos where we demystify Transformer models and explain them with visuals and friendly examples. 00:00 Introduction01:50 What is a transformer?04:35 Generating one word at a time08:59 Sentiment Analysis13:05 Neural Networks18:18 Tokenization19:12 Embeddings25:06 Positional encoding27:54 Attention32:29 Softmax35:48 Architecture of a Transformer39:00 Fine-tuning42:20 Conclusion
