neural.md

The best neural roadmap

• https://github.com/weslynn/AlphaTree-graphic-deep-neural-network

The best GAN collection

• Actually usable https://github.com/kozistr/Awesome-GANs
• Longer listing https://github.com/nightrome/really-awesome-gan

The best explanation regarding Ensemble methods

• https://www.kdnuggets.com/2017/11/difference-bagging-boosting.html

The best activation function - Mish

• https://github.com/digantamisra98/Mish
• second prizes
  • https://github.com/kligvasser/xUnit
  • GELU https://github.com/hendrycks/GELUs
  • SELU https://github.com/bigsnarfdude/SELU_Keras_Tutorial
  • Swish https://github.com/Neoanarika/Searching-for-activation-functions
  • https://github.com/ml-research/pau
• https://en.wikipedia.org/wiki/Activation_function

The best Collaborative Filtering and Recommender algorithms

• Metalist
  • https://github.com/grahamjenson/list_of_recommender_systems
  • https://github.com/hongleizhang/RSPapers
  • https://github.com/daicoolb/RecommenderSystem-Paper
  • https://github.com/chihming/competitive-recsys
• Dataset https://github.com/caserec/Datasets-for-Recommender-Systems
• Best practices https://github.com/microsoft/recommenders
• Python https://github.com/NicolasHug/Surprise
• Python https://github.com/lyst/lightfm
• Java https://github.com/guoguibing/librec
• Python https://github.com/maciejkula/spotlight
• Python https://github.com/benfred/implicit
• Ruby https://github.com/asmuth/recommendify (mono?)
• Python https://github.com/ocelma/python-recsys (mono)
• Python https://github.com/muricoca/crab
• Golang https://github.com/zhenghaoz/gorse
• Java https://github.com/lenskit/lenskit
• Python https://github.com/jadianes/spark-movie-lens (mono)
• Javascript https://github.com/guymorita/recommendationRaccoon (mono)
• Python https://github.com/Coder-Yu/RecQ
• Python https://github.com/IBM/elasticsearch-spark-recommender (mono?)
• Scala https://github.com/actionml/universal-recommender (mono?)
• Python https://github.com/Mendeley/mrec
• C# https://github.com/zenogantner/MyMediaLite
• Python https://github.com/practical-recommender-systems/moviegeek
• Python https://github.com/kakao/buffalo (mono?)
• Python https://github.com/apachecn/RecommenderSystems
• Java https://github.com/bystc/MovieRecommender (mono?)
• Java https://github.com/pranab/sifarish
• Python https://github.com/jadianes/winerama-recommender-tutorial (mono?)
• Python https://github.com/google-research/recsim
• Python https://github.com/dawenl/vae_cf (mono)
• Java https://github.com/RankSys/RankSys

The best Adversarial Reporgramming

• https://github.com/Prinsphield/Adversarial_Reprogramming
• https://github.com/kcelia/adversarial_reprogramming_of_neural_network
• https://github.com/lizhuorong/Adversarial-Reprogramming-tensorflow
• https://github.com/rajatvd/AdversarialReprogramming
• https://github.com/savan77/Adversarial-Reprogramming
• https://github.com/mishig25/adversarial-reprogramming-keras

The best Neural Architecture Search

• orang https://github.com/microsoft/nni
• Keras https://github.com/keras-team/autokeras
• Tensorflow https://github.com/tensorflow/adanet
• PyTorch https://github.com/quark0/darts

The best Privacy Tool for Machine Learning

• reference https://github.com/mortendahl/awesome-ppml
  • general computing https://github.com/tushar-semwal/awesome-federated-computing
  • https://github.com/poga/awesome-federated-learning
  • https://github.com/timmers/awesome-federated-learning
• https://github.com/OpenMined/PySyft
• https://github.com/FederatedAI/FATE
• https://github.com/tensorflow/federated
• https://github.com/facebookresearch/CrypTen
• https://github.com/shaoxiongji/federated-learning

The best knowledge distillation

• TBD
• Pruning and sparsity
• Quantization