AutoML library for deep learning

Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)

Automated deep learning algorithms implemented in PyTorch.

Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL challenge@NeurIPS.

Awesome Neural Architecture Search Papers

DataStructure(SwordOffer、LeetCode)、Deep Learning(Tensorflow、Keras、Pytorch)、Machine Learning(sklearn、spark)、AutoML、AutoDL、ModelDeploying、SQL

A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

Auto-DL helps you make Deep Learning models without writing a single line of code and giving as little input as possible.

some scripts for stable-diffusion-webui in the autodl

Package: R Interface to AutoKeras

An Agent Skill for the DL experiment lifecycle: RUN (a GPU you own or rent) → VERIFY the number is real → DELIVER reproducible, single-source figures and tables.

collecting related resources of automated machine learning here

Reproduce alpaca

An AutoDL tool for Neural Architecture Search and Hyperparameter Optimization on Tensorflow and Keras

Python library and dashboard for hyperparameter search and model training for computer vision tasks based on PyTorch, Optuna, FiftyOne, Dash, Segmentation Model Pytorch.

Auto-DL helps you make Deep Learning models without writing a single line of code and giving as little input as possible.

Weibo SentimentBot is built Qwen3.5-0.6B Fine-Tuning Based. It is trained through a complete fine-tuning pipeline on the AutoDL cloud computing infrastructure. Demonstrating a complete LLMs-Alignment training pipeline: starting from Qwen3.5-0.6B, through stages: SFT-Training, LoRA, and DPO preference optimization, the model gradually evolves fr

The MLOps platform for innovators 🚀

Meta-Padawan solution to the NeurIPS (2021) - Few-shot learning competition.