RAG vs. Fine Tuning

LangChain became immensely popular when it was launched in 2022, but how can it impact your development and application of AI models, Large Language Models (LLM) in particular. In this video Martin Keen shares an overview of the features and uses of LangChain.

In this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles. OUTLINE:00:00 Introduction01:28 Historical background02:50 Curve Fitting problem06:26 Random vs guided adjustments09:43 Derivatives14:34 Gradient Descent16:23 Higher dimensions21:36 Chain Rule Intuition27:01 Computational Graph and Autodiff36:24 Summary38:16 Shortform39:20 Outro Jürgen Schmidhuber’s blog…

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…

In a society that is confronting the new age of AI in which LLMs begin to display aspects of human intelligence, understanding the fundamental theory of deep learning and applying it to real systems is a compelling and urgent need. This panel will introduce some new simple foundational results in the theory of supervised learning….

https://www.youtube.com/watch?v=9-Jl0dxWQs8 AI Alignment forum post from the Deepmind researchers referenced at the video’s start:https://www.alignmentforum.org/posts/… Anthropic posts about superposition referenced near the end:https://transformer-circuits.pub/2022…https://transformer-circuits.pub/2023… Some added resources for those interested in learning more about mechanistic interpretability, offered by Neel Nanda Mechanistic interpretability paper reading listhttps://www.alignmentforum.org/posts/… Getting started in mechanistic interpretabilityhttps://www.neelnanda.io/mechanistic-… An interactive demo of sparse autoencoders (made…

Generative AI Agents represent the current frontier of LLM technology, enabling dynamic interactions and intelligent workflow automation. However, the complexities of architecting and deploying these agents can be daunting. In this live session, Patrick Marlow demystifies the process, guiding you through the critical decisions and trade-offs involved in building production-ready agents. Explore the full spectrum…