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awesome-automl-papers

A curated list of automated machine learning papers, articles, tutorials, slides and projects

awesome-nas

A curated list of neural architecture search (NAS) resources.

awesome-reinforcement-learning

Learning Resources And Links Of Reinforcement Learning （updating）

bayesmark

Benchmark framework to easily compare Bayesian optimization methods on real machine learning tasks

bayeso-benchmarks

Benchmarks for Bayesian optimization.

bbo_challenge_starter_kit

Starter kit for the black box optimization challenge at Neurips 2020

books

无它术，唯勤读书而多为之，自工

caffe-model

Caffe models (includimg classification, detection and segmentation) and deploy files for famouse networks

caffe_code_analysis

Caffe_Code_Analysis

classtobook

Scrapy a class from network and transform them to a book

conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

cpp-primer-practice

cvpr2019

cvpr2019／cvpr2018/cvpr2019 papers，极市团队整理

dali_pytorch_demo

Example code showing how to use Nvidia DALI in pytorch, with fallback to torchvision. Contains a few differences to the official Nvidia example, namely a completely CPU pipeline & improved memory usage

darts

Differentiable architecture search for convolutional and recurrent networks

dataloaders_dali

PyTorch DataLoaders implemented with DALI for boosting image preprocessing

deep-residual-networks

Deep Residual Learning for Image Recognition

deep_rl

PyTorch implementations of Deep Reinforcement Learning algorithms (DQN, DDQN, A2C, VPG, TRPO, PPO, DDPG, TD3, SAC, ASAC, TAC, ATAC)

dgrl_opfe

django-project-template

Project template layout for Django 2.0+

dorn

dreml

efficient_image_to_image_nas

enas

TensorFlow Code for paper "Efficient Neural Architecture Search via Parameter Sharing"

enet

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

explanatorygraph

Interpreting CNN Knowledge via an Explanatory Graph

face-landmarks-detection-benchmark

Face landmarks(fiducial points) detection benchmark

face-resources