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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics, Auxiliary Tasks in Multi-task Learning
Bayesian LSTM (Tensorflow)
Multi-variate time series classification with a bi-directional ESN with a readout implemented as a deep neural network.
A library for scientific machine learning and physics-informed learning
A Surrogate Model with Data Augmentation and Deep Transfer Learning for Temperature Field Prediction of Heat Source Layout
An Intuitive Tutorial to Gaussian Processes Regression
Testing Kalman Filter for GPS data
Using Bayesian Optimization to optimize hyper parameter in Keras-made neural network model.
K-Nearest Neighbors Time Series Prediction with Invariances
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
Regression prediction of time series data using LSTM, SVM and random forest. 使用LSTM、SVM、随机森林对时间序列数据进行回归预测,注释拉满。
Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data
Unofficial implementation of: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
PDE Solvers: FEM vs Deep Learning
We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.
Investigating PINNs
Using PINN's to solve heat transfer problems for parameter
In recent years, the use of physics-informed neural networks (PINNs) has gained popularity across several engineering disciplines due to their effectiveness in solving linear and non-linear partial differential equations (PDE) and real-world problems despite noisy data. The basic approach used to solve the PINNs is to construct the neural network a
PINN for heat transfer problems
Soving heat transfer problems using PINN with tf2.0
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
PyTorch Implementation of Physics-informed Neural Networks
TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).
Deep learning for Engineers - Physics Informed Deep Learning
A place to share problems solved with SciANN
Physics-Informed Neural Network (PINN) for Solving Coupled PDEs Governing Thermochemical Physics in Bi-Material Systems
A game theoretic approach to explain the output of any machine learning model.
Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
Stochastic variational heteroscedastic Gaussian process
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.