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Fluid solver based on Lattice Boltzmann method implemented by taichi programming language
深度学习100例、深度学习DL、图片分类、目标识别、目标检测、自然语言处理nlp、文本分类、TensorFlow、PyTorch
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Notebooks for "Python for Signal Processing" book
The bearing fault data set collected by Jiangnan University.
Attention-based CNN-BiLSTM for SOH prediction of lithium-ion batteries
A PyTorch Tutorials of Sentiment Analysis Classification (RNN, LSTM, Bi-LSTM, LSTM+Attention, CNN)
使用卷积神经网络-长短期记忆网络（bi-LSTM）-注意力机制对股票收盘价进行回归预测。The convolution neural network, short-term memory network and attention mechanism are used to predict the closing price.
CNN+BiLSTM+Attention Multivariate Time Series Prediction implemented by Keras
PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series
Sequence-to-sequence model with LSTM encoder/decoders and attention
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Temporal Pattern Attention for Multivariate Time Series Forecasting
Time-Series models for multivariate and multistep forecasting, regression, and classification
Predicting power generation of a wind farm using machine learning 💨
Exercises answers to the book "machine-learning" written by Zhou Zhihua。周志华《机器学习》课后习题，个人解答。各算法都拿numpy和pandas实现了一遍
《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新，English version ongoing
Deep Learning Zero to All - Pytorch
Probabilistic time series modeling in Python
LSTM neural network realizes the prediction of wind speed through the learning of various parameters. It can provide important support for the smooth operation of power system and the optimization …
Implementation of Convolutional LSTM in PyTorch.
Minimal, clean example of lstm neural network training in python, for learning purposes.
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…