《Pytorch实用教程》（第二版）无论是零基础入门，还是CV、NLP、LLM项目应用，或是进阶工程化部署落地，在这里都有。相信在本书的帮助下，读者将能够轻松掌握 PyTorch 的使用，成为一名优秀的深度学习工程师。

A transposition caller.

《Pytorch模型训练实用教程》中配套代码

这是我学习 PyTorch 的笔记对应的代码，点击查看 PyTorch 笔记在线电子书

Structural variation caller using third generation sequencing

🧙 ERVmancer conda package

Foldseek enables fast and sensitive comparisons of large structure sets.

Finding TEs with short-read data using random forests

本仓库将PyTorch官方书籍《Deep learning with PyTorch》（基本摘录版）翻译成中文版并给出可运行的相关代码。

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

Software for detecting transposable element insertions from next-generation sequencing data

gReLU is a python library to train, interpret, and apply deep learning models to DNA sequences.

Detecting genome structural variants with deep learning in single molecule sequencing

An accurate repeat detection from Nanopore data using deep learning and image techniques

The fastai book, published as Jupyter Notebooks

Foundation Models for Genomics & Transcriptomics

Biological foundation modeling from molecular to genome scale

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Genome modeling and design across all domains of life

TE detection pipeline for nanopore sequencing

Deep learning framework for SV calling and genotyping

Phased structural variant discovery in pangenomes