/explore
|
You are here 
|
sebastianraschka.com | ||
| | | | |
magazine.sebastianraschka.com
I'm an LLM Research Engineer with over a decade of experience in artificial intelligence. My work bridges academia and industry, with roles including senior staff at an AI company and a statistics professor. My expertise lies in LLM research and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations. |
|
| | | | | ||
| | | | |
www.nicktasios.nl
In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In the second post, we will bu |
|
| | | | | ||
| | | | |
neuralnetworksanddeeplearning.com
[AI summary] The text provides an in-depth explanation of the backpropagation algorithm in neural networks. It starts by discussing the concept of how small changes in weights propagate through the network to affect the final cost, leading to the derivation of the partial derivatives required for gradient descent. The explanation includes a heuristic argument based on tracking the perturbation of weights through the network, resulting in a chain of partial derivatives. The text also touches on the historical context of how backpropagation was discovered, emphasizing the process of simplifying complex proofs and the role of using weighted inputs (z-values) as intermediate variables to streamline the derivation. Finally, it concludes with a citation and licens... |
|
| | | | | ||
| | | | |
blog.christianperone.com
Note: This is a continuation of the previous post: Thoughts on Riemannian metrics and its connection with diffusion/score matching [Part I], so if you haven't read it yet, please consider reading as I won't be re-introducing in depth the concepts (e.g., the two scores) that I described there already. This article became a bit long, |
|
| | | |||
