Attention in transformers, visually explained | DL6
Demystifying attention, the key mechanism inside transformers and LLMs.
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…
To learn more, I highly recommend the book by Michael Nielsenhttp://neuralnetworksanddeeplearning….The book walks through the code behind the example in these videos, which you can find here:https://github.com/mnielsen/neural-ne… MNIST database:http://yann.lecun.com/exdb/mnist/ Also check out Chris Olah’s blog:http://colah.github.io/His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great. And if…
This is the 5th video in a series on using large language models (LLMs) in practice. Here, I discuss how to fine-tune an existing LLM for a particular use case and walk through a concrete example with Python code.
Learn prompt engineering techniques to get better results from ChatGPT and other LLMs.
Chapters0:00 Introduction1:54 Neural N-Gram Models6:03 Recurrent Neural Networks11:47 LSTM Cells12:22 Outro