6924 Open source projects that are alternatives of or similar to Kaggle Web Traffic

Solution to Kaggle - Web Traffic Time Series Forecasting

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

Anything can happen in the next half hour (including spectral timing made easy)!

Multiple implementations for abstractive text summurization , using google colab

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

A Multipurpose Library for Synthetic Time Series Generation in Python

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

Stock Analysis Tutorial in Python

Natural Language Processing with TensorFlow, published by Packt

Chinese Poetry Generation

Allstate Kaggle Competition ML Capstone Project

A python library for time-series smoothing and outlier detection in a vectorized way.

中文聊天机器人，基于10万组对白训练而成，采用注意力机制，对一般问题都会生成一个有意义的答复。已上传模型，可直接运行，跑不起来直播吃键盘。

深度学习入门教程, 优秀文章, Deep Learning Tutorial

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)

All about Neural Networks!

A collection of notebooks of my Machine Learning class written in python 3

Adventure into using multi attention recurrent neural networks for time-series (city traffic) for the 2017-11-18 McKinsey IronMan (24h non-stop) prediction challenge

Rapid large-scale fractional differencing with RAPIDS to minimize memory loss while making a time series stationary. 6x-400x speed up over CPU implementation.

This is a times series anomaly detection algorithm, implemented in Python, for catching multiple anomalies. It uses a moving average with an extreme student deviate (ESD) test to detect anomalous points.

Diseases Detection from NIH Chest X-ray data

NeuralProphet - A simple forecasting model based on Neural Networks in PyTorch

Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network

Deep learning meets molecular dynamics.

My Attempt(s) In The World Of ML/DL....

TensorFlow Tutorial for Time Series Prediction

Durable time-series database that's API-compatible with Prometheus.

VGG-19 deep learning model trained using ISCX 2012 IDS Dataset

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

Sentiment Analysis with LSTMs in Tensorflow

Health Check ✔ is a Machine Learning Web Application made using Flask that can predict mainly three diseases i.e. Diabetes, Heart Disease, and Cancer.

Interview = 简历指南 + LeetCode + Kaggle

RNN(SimpleRNN, LSTM, GRU) Tensorflow2.0 & Keras Notebooks (Workshop materials)

Code repository of the paper "Wavelet Networks: Scale Equivariant Learning From Raw Waveforms" https://arxiv.org/abs/2006.05259

Sequence to sequence learning with MXNET

Top 15% ranked solution to the Carvana Image Masking Challenge on Kaggle

Talk at PyCon UA 2018 (Kharkov, Ukraine)

Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

📢 Ready to learn or review your knowledge!

Implementation of RNN for Time Series prediction from the paper https://arxiv.org/abs/1704.02971

Sample notebooks for Kaggle competitions

Java implementation of Seasonal-Trend-Loess time-series decomposition algorithm.

Timeseries database

Library which contains several time-dependent data and index structures (e.g., IntervalTree, BucketTimeSeries), as well as algorithms.

Online-Recurrent-Extreme-Learning-Machine (OR-ELM) for time-series prediction, implemented in python

[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

Detecting emotions using MFCC features of human speech using Deep Learning

A tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.

PyTorch tutorial for learners

记录我学习数据挖掘过程的笔记和见到的奇技，持续更新～

Datasets, tools, and benchmarks for representation learning of code.

Deep Markov Models

GridDB is a next-generation open source database that makes time series IoT and big data fast,and easy.

Implementation of the paper - Generative Recurrent Networks for De Novo Drug Design.

Time Series Forecast with Bitcoin value, to detect upward/down trends with Machine Learning Algorithms

A use-case focused tutorial for time series forecasting with python

Set of scripts and data for reproducing dog breed classification model training, analysis, and inference.

使用PyTorch实现Char RNN生成古诗和周杰伦的歌词

Kaggle Kernel for House Prices competition https://www.kaggle.com/massquantity/all-you-need-is-pca-lb-0-11421-top-4