Pinned Repositories
aaltd18
Data augmentation using synthetic data for time series classification with deep residual networks
Advanced-Deep-Learning-with-Keras
Advanced Deep Learning with Keras, published by Packt
anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
BMC
Notes on Scientific Computing for Biomechanics and Motor Control
Book4_Power-of-Matrix
Book_4_《矩阵力量》 | 鸢尾花书：从加减乘除到机器学习；本册有，584幅图，81个代码文件，其中18个Streamlit App；状态：清华社五审五校中；Github稿件基本稳定，欢迎提意见，会及时修改
brew
⛔️ DEPRECATED brew: Python Ensemble Learning API
deep-learning-papers-translation
深度学习论文翻译，包括分类论文，检测论文等
deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
deeplearning-models
A collection of various deep learning architectures, models, and tips
DeepSurv
DeepSurv is a deep learning approach to survival analysis.
application-user2's Repositories
application-user2/Advanced-Deep-Learning-with-Keras
Advanced Deep Learning with Keras, published by Packt
application-user2/anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
application-user2/BMC
Notes on Scientific Computing for Biomechanics and Motor Control
application-user2/Book4_Power-of-Matrix
Book_4_《矩阵力量》 | 鸢尾花书：从加减乘除到机器学习；本册有，584幅图，81个代码文件，其中18个Streamlit App；状态：清华社五审五校中；Github稿件基本稳定，欢迎提意见，会及时修改
application-user2/deep-learning-papers-translation
深度学习论文翻译，包括分类论文，检测论文等
application-user2/deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
application-user2/deeplearning-models
A collection of various deep learning architectures, models, and tips
application-user2/DeepSurv
DeepSurv is a deep learning approach to survival analysis.
application-user2/easy-rl
强化学习中文教程（蘑菇书），在线阅读地址：https://datawhalechina.github.io/easy-rl/
application-user2/hub
Submission to https://pytorch.org/hub/
application-user2/leeml-notes
李宏毅《机器学习》笔记，在线阅读地址：https://datawhalechina.github.io/leeml-notes
application-user2/lihang-code
《统计学习方法》的代码实现
application-user2/ML-assignments
about Regression, Classification, CNN, RNN, Explainable AI, Adversarial Attack, Network Compression, Seq2Seq, GAN, Transfer Learning, Meta Learning, Life-long Learning, Reforcement Learning.
application-user2/ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
application-user2/ML-notes
notes about machine learning
application-user2/mlcourse.ai
Open Machine Learning Course
application-user2/nndl.github.io
《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning
application-user2/OmniAnomaly
KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
application-user2/one-python-craftsman
来自一位 Pythonista 的编程经验分享，内容涵盖编码技巧、最佳实践与思维模式等方面。
application-user2/optunity
optimization routines for hyperparameter tuning
application-user2/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
application-user2/Python-for-Signal-Processing
Notebooks for "Python for Signal Processing" book
application-user2/python_for_data_analysis_2nd_chinese_version
《利用Python进行数据分析·第2版》
application-user2/pytorch-beginner
pytorch tutorial for beginners
application-user2/scikit-opt
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)
application-user2/SGANinv
Multiple-point geostatistics inversion of reservoir models using spatial generative adversarial networks
application-user2/siml
Machine Learning algorithms implemented from scratch
application-user2/sklearn-bayes
Python package for Bayesian Machine Learning with scikit-learn API
application-user2/sklearn-deap
Use evolutionary algorithms instead of gridsearch in scikit-learn
application-user2/thuthesis
LaTeX Thesis Template for Tsinghua University