Machine Learning
275 articles in this category (Page 5 of 12)
Building Autonomous ML Research Loops with Karpathy’s AutoResearch Framework
Implement an automated ML research pipeline in Google Colab using Andrej Karpathy’s AutoResearch framework to iteratively optimize hyperparameters and track validation bits-per-byte metrics.
Building Scalable ML Data Pipelines for Image and Structured Data with Daft
Learn how to build an end-to-end ML pipeline using Daft, a Python-native data engine that handles MNIST image reshaping, feature engineering via batch UDFs, and Parquet persistence for high-performance processing.
Yuan 3.0 Ultra: Optimizing Trillion-Parameter MoE Efficiency via LAEP
YuanLab AI releases Yuan 3.0 Ultra, a 1T-parameter MoE model that achieves a 49% boost in pre-training efficiency. By utilizing Layer-Adaptive Expert Pruning and a Reflection Inhibition Reward Mechanism, it reduces total parameters by 33.3% while maintaining state-of-the-art performance in multimodal retrieval and enterprise benchmarks.
Meet SymTorch: A PyTorch Library for Translating Deep Learning Models into Mathematical Equations
Cambridge Researchers introduce SymTorch, a library using symbolic regression to translate PyTorch models into closed-form equations, achieving an 8.3% throughput increase in LLM inference benchmarks.
Building Scalable ML Pipelines on Millions of Rows with Vaex
Learn how to build a production-style analytics and ML pipeline on 2 million rows using Vaex, featuring lazy expressions and approximate statistics without materializing data in memory.