pein2017 Goto Github PK

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Statistical learning methods, 统计学习方法 [李航] 值得反复读. [笔记, 代码, notebook, 参考文献, Errata, lihang]

annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

ascend-npu-parallel-training

Adapts parallel computing for ImageNet on NPU Ascend910, with CIFAR-10 dataset support.

awesome-pytorch-list

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

deep-learning-with-pytorch-

Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

enso

full and simply attempt on Nino3.4 modeling

fyp-dennis

matlab code

fyp-durham

FYP for modeling about fermentation process

fyp-papers

gfs-data-downloader

GFS data retrieval demo with configurable dataset path.

global-flood-sarnet

Semantic segmentation of Sentinel-1 SAR imagery to identify floodwater presence globally.

hands-on-ml-codes

huawei_modelarts

matrixslow

A simple deep learning framework in pure python for purpose of learning in DL

mlr

Machine Learning in R

mscda--classification

pandas_advanced_exercise

Pandas进阶修炼300题

python

pytorch

数据科学的笔记以及资料搜集

pytorch-handbook

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

solar-wind-power-prediction

statistical-learning-method_code

tutorial

Some practices for implementation of classical model structure (from scratch)

wangshusen-deeplearning

wind-power-prediction

This repository leverages the PyTorch Lightning framework to streamline and accelerate the development of a wind power prediction project. Designed to take full advantage of GPU capabilities, this project aims to efficiently predict offshore wind power by integrating various algorithms and models.