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
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.
Implemented Deep Residual Learning for Image Recognition Paper and achieved lower error rate by customizing different parts of the architecture.
TinyYoloV2 imagenet 1K results.
Add some useful functions based on AlexeyAB darknet
self-used pytorch utilities
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