AnnOl1

AnnOl1's Stars

• facebookresearch/EmpatheticDialogues

  Dialogue model that produces empathetic responses when trained on the EmpatheticDialogues dataset.

  Language:Python43463

• deshpandenu/Time-Series-Forecasting-of-Amazon-Stock-Prices-using-Neural-Networks-LSTM-and-GAN-

  Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

  Language:Jupyter Notebook390120

• Mathanraj-Sharma/sample-for-medium-article

  Contains sample codes for various python tasks

  Language:Jupyter Notebook1113

• EvgeniiMunin/ctr_project

  Language:Python33

• ancatmara/data-science-nlp

  NLP Section of the Data Science course, NRU HSE

  Language:Jupyter Notebook1619

• nlp-with-transformers/notebooks

  Jupyter notebooks for the Natural Language Processing with Transformers book

  Language:Jupyter Notebook3.8k1.2k

• microsoft/CodeBERT

  CodeBERT

  Language:Python2.1k439

• JesusFreke/smali

  smali/baksmali

  Language:Java6.3k1.1k

• liuhh02/stanford-algorithms-specialization

  Problem Set and Programming Assignment Solutions to Stanford University's Algorithms Specialization on Coursera & edX

  Language:C++13057

• rvarun7777/Graph-Search--Shortest-Paths--and-Data-Structures

  Graph Search, Shortest Paths, and Data Structures

  Language:Python1512

• prabhnoor0212/Siamese-Network-Text-Similarity

  This is a basic Siamese network for checking similarity of texts. The example used is of Kaggle Question Pair Similarity Datset.

  Language:Jupyter Notebook1311

• trainindata/deploying-machine-learning-models

  Code for the online course "Deployment of Machine Learning Models"

  Language:Jupyter Notebook7807.6k

• egonSchiele/grokking_algorithms

  Code for the book Grokking Algorithms (https://amzn.to/29rVyHf)

  Language:JavaScript11.3k3.7k

• dustinsgoodman/coursera_algorithms

  Personal files related to Coursera Algorithms course

  Language:Python1111

• ageron/handson-ml2

  A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

  Language:Jupyter Notebook27.6k12.7k

• ravising-h/Urbansound8k

  Sound Classification using Librosa, ffmpeg, CNN, Keras, XGBOOST, Random Forest.

  Language:Jupyter Notebook6514

• AnnOl1/XAI-for-speech-recognition

  Language:Jupyter Notebook1

• Yorko/mlcourse.ai

  Open Machine Learning Course

  Language:Python9.6k5.7k

• abaranovskis-redsamurai/automation-repo

  Machine learning and process automation

  Language:Jupyter Notebook138115

• a1ip/Abbakumov

  Анализ данных на Python в примерах и задачах

  Language:Jupyter Notebook2935

• pandas-ml/pandas-ml

  pandas, scikit-learn, xgboost and seaborn integration

  Language:Python31878

• jrjohansson/scientific-python-lectures

  Lectures on scientific computing with python, as IPython notebooks.

  Language:Jupyter Notebook3.5k1.8k

• esokolov/ml-course-hse

  Машинное обучение на ФКН ВШЭ

  Language:Jupyter Notebook2.9k1.1k

• esokolov/ml-course-msu

  Lecture notes and code for Machine Learning practical course on CMC MSU

  Language:Jupyter Notebook871316

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook22.4k3.2k

• suinleelab/treeexplainer-study

  Code and documentation for experiments in the TreeExplainer paper

  Language:HTML17744

• EthicalML/xai

  XAI - An eXplainability toolbox for machine learning

  Language:Python1.1k166

• interpretml/interpret

  Fit interpretable models. Explain blackbox machine learning.

  Language:C++6.2k726

• ModelOriented/DrWhy

  DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

  Language:R67985

• 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:Python724133