zhangxiang390

zhangxiang390's Stars

• princewen/tensorflow_practice

  tensorflow实战练习，包括强化学习、推荐系统、nlp等

  Language:Python6.7k3.3k

• shenweichen/DeepMatch

  A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.

  Language:Python2.2k530

• benhamner/Metrics

  Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

  Language:Python1.6k453

• houshanren/hangzhou_house_knowledge

  2017年买房经历总结出来的买房购房知识分享给大家，希望对大家有所帮助。买房不易，且买且珍惜。Sharing the knowledge of buy an own house that according to the experience at hangzhou in 2017 to all the people. It's not easy to buy a own house, so I hope that it would be useful to everyone.

  Language:CSS26.7k5.3k

• hiranumn/IntegratedGradientsTF

  Tensorflow implementation of integrated gradients presented in "Axiomatic Attribution for Deep Networks". It explains connections between two tensors.

  Language:Jupyter Notebook168

• suinleelab/path_explain

  A repository for explaining feature attributions and feature interactions in deep neural networks.

  Language:Jupyter Notebook18628

• ankurtaly/Integrated-Gradients

  Attributing predictions made by the Inception network using the Integrated Gradients method

  Language:Jupyter Notebook59599

• marcoancona/DeepExplain

  A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)

  Language:Python729133

• SeldonIO/alibi

  Algorithms for explaining machine learning models

  Language:Python2.4k249

• dswah/pyGAM

  [HELP REQUESTED] Generalized Additive Models in Python

  Language:Python871159

• jphall663/interpretable_machine_learning_with_python

  Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

  Language:Jupyter Notebook674207

• jphall663/awesome-machine-learning-interpretability

  A curated list of awesome responsible machine learning resources.

  3.6k583

• h2oai/mli-resources

  H2O.ai Machine Learning Interpretability Resources

  Language:Jupyter Notebook481131

• marcotcr/lime

  Lime: Explaining the predictions of any machine learning classifier

  Language:JavaScript11.5k1.8k

• pytorch/captum

  Model interpretability and understanding for PyTorch

  Language:Python4.8k489

• dtak/tree-regularization-public

  Code for AAAI 2018 accepted paper: "Beyond Sparsity: Tree Regularization of Deep Models for Interpretability"

  Language:Python7721

• yangxudong/deeplearning

  深度学习相关的模型训练、评估和预测相关代码

  Language:Python1k377

• tensorflow/adanet

  Fast and flexible AutoML with learning guarantees.

  Language:Jupyter Notebook3.5k527

• alibaba/x-deeplearning

  An industrial deep learning framework for high-dimension sparse data

  Language:PureBasic4.3k1k

• DongjunLee/transformer-tensorflow

  TensorFlow implementation of 'Attention Is All You Need (2017. 6)'

  Language:Python348111

• aymericdamien/TensorFlow-Examples

  TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

  Language:Jupyter Notebook43.4k14.9k

• ematvey/hierarchical-attention-networks

  Document classification with Hierarchical Attention Networks in TensorFlow. WARNING: project is currently unmaintained, issues will probably not be addressed.

  Language:Python463148

• lambdaji/tf_repos

  TensorFlow Script

  Language:Python682318

• shenweichen/DeepCTR

  Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

  Language:Python7.5k2.2k

• Naresh1318/GANs_N_Roses

  Uses a Deep Convolutional Generative Adversial Network to generate images of roses.

  Language:Python67

• nfmcclure/tensorflow_cookbook

  Code for Tensorflow Machine Learning Cookbook

  Language:Jupyter Notebook6.2k2.4k

• zhangxiang390/hyperopt

  Distributed Asynchronous Hyperparameter Optimization in Python

  Language:Python1

• zhangxiang390/spearmint

  Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012

  Language:Python1