Large Language Models explained briefly
Timestamps:
0:00 – Who this was made for
0:41 – What are large language models?
7:48 – Where to learn more
Timestamps:
0:00 – Who this was made for
0:41 – What are large language models?
7:48 – Where to learn more
Demystifying attention, the key mechanism inside transformers and LLMs.
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…
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
Oct 9, 2024As LLMs are being integrated into more and more applications, security standards for these integrations have lagged behind. Most security research either focuses 1) on social harms, biases exhibited by LLMs, and other content moderation tasks, or 2) zooms in on the LLM itself and ignores the applications that are built around them….
Sign up for our monthly newsletter to be informed about AI safety