Keras/TF implementation of AdamW, SGDW, NadamW, Warm Restarts, and Learning Rate multipliers
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Updated
Jan 6, 2022 - Python
Keras/TF implementation of AdamW, SGDW, NadamW, Warm Restarts, and Learning Rate multipliers
Implements https://arxiv.org/abs/1711.05101 AdamW optimizer, cosine learning rate scheduler and "Cyclical Learning Rates for Training Neural Networks" https://arxiv.org/abs/1506.01186 for PyTorch framework
Newton-Muon + Preconditioned Optimizers for MoE Training at scale, with out-of-the-box support for MuP and FSDP support for Muon, built on top of Megatron-LM and TransformerEngine.
Quasi Hyperbolic Rectified DEMON Adam/Amsgrad with AdaMod, Gradient Centralization, Lookahead, iterative averaging and decorrelated Weight Decay
Pytorch implementation of lookahead optimizer(https://arxiv.org/pdf/1907.08610.pdf) and RAdam(https://arxiv.org/pdf/1908.03265.pdf)
Nadir: Cutting-edge PyTorch optimizers for simplicity & composability! 🔥🚀💻
Literature survey of convex optimizers and optimisation methods for deep-learning; made especially for optimisation researchers with ❤️
[ICLR 2026] LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning
SCAO is a sparse, second-order PyTorch optimizer designed as a high-throughput, drop-in replacement for AdamW.
GYRO is an optimizer for deep neural networks that augments Adam with a geometric rotation step applied to the gradient before momentum buffers are updated.
Clean-room GPT-2/GPT-3 implementation: tokenizers, architecture blocks, training loop with AdamW + cosine decay, CLI scripts, inference tools, and pytest suite. Covers OpenWebText-10k & WikiText-103 workflows. Designed as an academic reference for understanding and scaling decoder-only transformers
Lightweight, zero-dependency C++ Feedforward & Recurrent Neural Network library with native Python bindings (via pybind11).
Kaggle's plant disease image classification competition. Finetuning pre-trained CNN models, loss functions, and optimizers in order to achieve better results.
Computational Graph Library for Neural Network Training
🕳️ BlackHole Optimizer - A General Relativity approach to optimization. Beats Adam/AdamW via Schwarzschild metric, Hawking radiation, Kerr frame dragging, Penrose process, and Bekenstein-Hawking entropy.
reproduce Adam, AdamW, Adafactor optimizors with pytorch, and introduce popular optimizers in the training of the LLMs.
Drop-in PyTorch optimizer that beats AdamW with lower variance
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