MDP
MDP on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on mdp in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around mdp, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
![Derivative-Free Neural Network Optimization: MNIST Case [R]](https://preview.redd.it/te5dm6f9sy6h1.png?width=140&height=106&auto=webp&s=9a10d27cdf09a1a73927311e432b19fd25a9d8b4)
Derivative-Free Neural Network Optimization: MNIST Case [R]
Recent research demonstrates the potential of derivative-free optimization for complex neural networks. A direct optimization test, utilizing the Model-Based Derivative-Free (MDP) method, successfully trained a 784-32-10 neural network for MNIST image classification, achieving a test accuracy of 93.4%. This significantly outperformed a baseline Adam optimization, showcasing convergence across a 25,450-dimensional parameter space without relying on gradient information. For those exploring the foundations of LLMs, a related discussion on "Confused, where to start" offers a useful entry point. Code and further examples are available on GitHub.
Studying Sutton and Barto's RL book and its connections to RL for LLMs (e.g., tool use, math reasoning, agents, and so on)? [D]
Studying Sutton and Barto's seminal work on reinforcement learning (RL) offers valuable insights into the foundational principles of the field, particularly as they relate to large language models (LLMs). By exploring chapters on finite Markov decision processes, temporal difference learning, and policy gradient methods, you can build a solid understanding of RL's mechanics. This knowledge is crucial for grasping how RL can enhance LLM capabilities, including tool use and mathematical reasoning.