Records found: 11
'Compare Focal Loss and Binary Cross-Entropy on a 99:1 imbalanced dataset to see how Focal Loss improves minority-class detection and yields more meaningful decision boundaries.'
A PyTorch tutorial demonstrating how to build a memory-augmented agent that uses differentiable memory, prioritized experience replay and meta-learning to adapt continuously without catastrophic forgetting.
Explore how to build local multi-endpoint ML APIs with LitServe, showcasing batching, streaming, caching and multi-task inference with Hugging Face pipelines.
'A hands-on tutorial showing how Ivy lets you write one neural network and run it across NumPy, PyTorch, TensorFlow, and JAX, including transpilation examples, unified API usage, advanced features, and performance benchmarks.'
A hands-on guide to advanced computer vision workflows using TorchVision v2 transforms, MixUp/CutMix augmentation, a modern attention-equipped CNN architecture, and robust training practices.
LeRobot step-by-step tutorial to train, evaluate and visualize a behavior-cloning visuomotor policy on the PushT dataset, with full Colab-ready code and checkpoints.
'A curated guide to the top computer vision blogs and research hubs in 2025, focusing on sources that publish reproducible code, rigorous benchmarks, and production-ready guidance.'
'A practical comparison of PyTorch and TensorFlow in 2025 covering developer experience, performance, deployment ecosystems, and use case guidance to help you choose the right framework.'
Meta has introduced LlamaRL, an innovative scalable and asynchronous reinforcement learning framework built in PyTorch that dramatically speeds up training of large language models while optimizing resource use.
Meta introduces KernelLLM, an 8-billion-parameter model that automates converting PyTorch modules into efficient Triton GPU kernels, outperforming larger models in kernel generation benchmarks.
Hugging Face has released nanoVLM, a compact PyTorch library that enables training a vision-language model from scratch in just 750 lines of code, combining efficiency, transparency, and strong performance.