Machine Learning

Muon is a second-order optimizer for deep learning models, designed to accelerate training and reduce memory usage. It leverages information about the curvature of the loss landscape to achieve faster convergence and more efficient memory utilization. By overcoming historical computational barriers and standardizing its usage, Muon brings the theoretical advantages of second-order optimization to the scale required for LLMs, potentially reshaping both practice and expectations in deep learning.

Active learning is a powerful technique that can help us automate the labeling process for large datasets. By selecting a subset of the data that is most relevant to the task at hand, active learning can be more efficient than manually labeling every example in a dataset. This can lead to better results and more accurate predictions. In this blog post, I’ll walk through the concept of active learning, how it works, and share a step-by-step implementation of how to automate dataset labeling for a text classification task using this method.

The journey of pioneers in Machine Learning (ML) and Artificial Intelligence (AI) is a remarkable tale of innovation, collaboration, and the relentless pursuit of knowledge.

Gran Turismo Sophy is an advanced AI racing agent developed through a collaboration between Sony AI, Polyphony Digital, and Sony Interactive Entertainment. This groundbreaking technology utilizes deep reinforcement learning to master the complexities of competitive racing in the Gran Turismo Sport simulator. Initially starting as an AI that struggled to navigate tracks, Sophy has evolved into a formidable competitor capable of challenging top human drivers by mastering racing tactics, etiquette, and vehicle control.

Temporal Difference (TD) Learning is a fundamental concept in the field of reinforcement learning, which is a subfield of artificial intelligence (AI). It is particularly powerful for problems where an agent must learn to make decisions over time based on its interactions with an environment. Unlike traditional supervised learning, where a model learns from a fixed dataset, TD Learning enables agents to learn directly from experience, making it well-suited for dynamic and uncertain environments.

A neural network is like a highly sophisticated, multi-layered calculator that learns from data. It consists of numerous “neurons” (tiny calculators) connected in layers, with each layer performing a unique function to help the network make predictions or decisions.

The beauty of VAEs lies in their ability to generate new samples by randomly sampling vectors from this known region and then passing them through the generator part of our model.

An autoencoder begins its journey by compressing input data into a lower dimension. It then endeavors to reconstruct the original input from this compressed representation.