Extremely simple and fast word2vec implementation with Negative Sampling + Sub-sampling
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Updated
Jan 21, 2021 - Python
Extremely simple and fast word2vec implementation with Negative Sampling + Sub-sampling
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
sharpDARTS: Faster and More Accurate Differentiable Architecture Search
Implemented Deep Residual Learning for Image Recognition Paper and achieved lower error rate by customizing different parts of the architecture.
End-to-end Image Classification using Deep Learning toolkit for custom image datasets. Features include Pre-Processing, Training with Multiple CNN Architectures and Statistical Inference Tools. Special utilities for RAM optimization, Learning Rate Scheduling, and Detailed Code Comments are included.
A comprehensive, research-driven collection of learning rate schedulers for PyTorch — 17 schedulers, composable warmup, opinionated presets, and first-class paper references.
self-used pytorch utilities
Short-Term Wind Power Forecasting utilizing a Transformer model and a seasonally aware custom cosine annealing scheduler.
Add some useful functions based on AlexeyAB darknet
TinyYoloV2 imagenet 1K results.
High-performance PyTorch LR schedulers with cosine annealing, flexible waypoints, plateau steps, and LR scaling. Unified API with pre-computed segments for zero runtime overhead.
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