Prompt Engineering Tutorial – Master ChatGPT and LLM Responses

Check out how large language models (LLMs) and generative AI intersect to push the boundaries of possibility. Unlock real-world use cases and learn how the power of a prompt can enhance LLM performance. You’ll also explore Google tools to help you learn to develop your own gen AI apps. https://www.youtube.com/watch?v=RBzXsQHjptQ https://www.youtube.com/watch?v=RBzXsQHjptQ

Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer. This Python course teaches you how to use RAG to combine your own custom data with the power of Large Language Models (LLMs). 💻 Code: https://github.com/langchain-ai/rag-from-scratch ⭐️ Course Contents ⭐️⌨️ (0:00:00) Overview⌨️ (0:05:53) Indexing⌨️ (0:10:40) Retrieval⌨️ (0:15:52) Generation⌨️ (0:22:14)…

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

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…