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Probability Theory and Mathematical Statistics Tutorial
The open-source solutions of FourCastNet and GraphCast
tfts: Time Series Deep Learning Models in TensorFlow
Anomaly detection related books, papers, videos, and toolboxes
This code is written for the blogs
Demand Forecasting Models for Kaggle competition
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Visualizations for machine learning datasets
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06
Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.
An R package for time series models and forecasts with xgboost compatible with {forecast} S3 classes
Forecasting Functions for Time Series and Linear Models
For extensive instructor led learning
Feature exploration for supervised learning
Fast and flexible AutoML with learning guarantees.
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
An open source library for Fuzzy Time Series in Python
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Code for the Kaggle Ensembling Guide Article on MLWave
StackNet is a computational, scalable and analytical Meta modelling framework
Implementation of Online Hedge Backpropagation
fairseq: Convolutional Sequence to Sequence Learning (Gehring et al. 2017) by Chainer
Kaggle | Web Traffic Forecasting 📈
This repo aims to be a useful collection of notebooks/code for understanding and implementing seq2seq neural networks for time series forecasting. Networks are constructed with keras/tensorflow.
Time series prediction using dilated causal convolutional neural nets (temporal CNN)
An implementation of a sequence to sequence neural network using an encoder-decoder
Dynamic seq2seq in TensorFlow, step by step