AnnOl1

AnnOl1's Stars

• 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:Python732133

• giuseppec/iml

  iml: interpretable machine learning R package

  Language:R49288

• jiaaro/pydub

  Manipulate audio with a simple and easy high level interface

  Language:Python8.9k1k

• cedricconol/keras-tuner-demo

  Hyperparameter Tuning Demo with Keras Tuner

  Language:Jupyter Notebook4

• keras-team/keras-tuner

  A Hyperparameter Tuning Library for Keras

  Language:Python2.9k396

• vanpntuong/DepressionDetect

  Detecting depression using DAIC WOZ dataset

  33

• mfacar/controltag

  Text analysis for anxiety level detection

  Language:Jupyter Notebook41

• timsainb/noisereduce

  Noise reduction in python using spectral gating (speech, bioacoustics, audio, time-domain signals)

  Language:Jupyter Notebook1.5k231

• MiteshPuthran/Speech-Emotion-Analyzer

  The neural network model is capable of detecting five different male/female emotions from audio speeches. (Deep Learning, NLP, Python)

  Language:Jupyter Notebook1.3k437

• PiotrSobczak/speech-emotion-recognition

  Multi-modal Speech Emotion Recogniton on IEMOCAP dataset

  Language:Python8315

• 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 Notebook672207

• wangyongjie-ntu/Awesome-explainable-AI

  A collection of research materials on explainable AI/ML

  Language:Markdown1.4k193

• lazyprogrammer/machine_learning_examples

  A collection of machine learning examples and tutorials.

  Language:Python8.4k6.3k

• julieeF/CS231N-Project

  Language:Jupyter Notebook169

• SaraSb1380/RegEx-Practice

  practicing re.split and re.findall

  Language:Jupyter Notebook1

• max1mka1/text_analysis_tasks

  Решаем задачи анализа текста методами ООП на Python, используя регулярные выражения (regex) [методы библиотеки re]. Те же задачи решены, основываясь на методологии структурного программирования (notOOP).

  Language:Python1

• cmaroti/speech_recognition

  Convolutional Neural Network for Speech Recognition, implemented in Ms. Pacman game

  Language:Jupyter Notebook63

• joanfmendo/Efficient-Large-Scale-Fleet-Management-via-Multi-Agent-Deep-Reinforcement-Learning

  Language:Python2

• sic-rus-ai/stepik-dl-nlp

  Материалы курса на Stepik "Нейронные сети и обработка текста"

  Language:Jupyter Notebook172130

• williamscott701/Information-Retrieval

  Information Retrieval algorithms developed in python. To follow the blog posts, click on the link:

  Language:Jupyter Notebook128156

• ranriy/Query-Search-using-TF-IDF-vectors-and-cosine-similarity

  Analyzed a corpus containing 30 .txt files and retrieved the most relevant document for a given query.

  Language:Python42

• RLOpensource/IMPALA-Scalable-Distributed-Deep-RL-with-Importance-Weighted-Actor-Learner-Architectures

  Language:Python363

• DIR-LAB/deep-batch-scheduler

  RLScheduler: An AutomatedHPC Batch Job Scheduler Using Reinforcement Learning [SC'20]

  Language:Python5114

• docwza/deep-rl-tsc

  Distributed Deep Reinforcement Learning Traffic Signal Control

  Language:Python4610