oplad's repositories
onnx.github.io
Code of the official webpage of onnx
d2l-chs
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
applied_ml
Applied Machine Learning with Python
introduction_to_ml_with_python
Notebooks and code for the book "Introduction to Machine Learning with Python"
oplad-pumpkin-book-machine-learning
《机器学习》（西瓜书）公式详解
models
A collection of pre-trained, state-of-the-art models in the ONNX format
BezierInfo-2
The development repo for the Primer on Bézier curves, https://pomax.github.io/bezierinfo
Book6_First-Course-in-Data-Science
Book_6_《数据有道》 | 鸢尾花书：从加减乘除到机器学习；正在大修大改，稍等
ControlNet
Let us control diffusion models!
Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
practical-statistics-for-data-scientists
Code repository for O'Reilly book
Data-Labeling-in-Machine-Learning-with-Python
Data Labeling in Machine Learning with Python, by Packt Publishing
Machine_Learning_Code_Implementation
Mathematical derivation and pure Python code implementation of machine learning algorithms.
Book4_Power-of-Matrix
Book_4_《矩阵力量》 | 鸢尾花书：从加减乘除到机器学习；上架！
Book3_Elements-of-Mathematics
Book_3_《数学要素》 | 鸢尾花书：从加减乘除到机器学习；上架；欢迎继续纠错，纠错多的同学还会有赠书！
Book5_Essentials-of-Probability-and-Statistics
Book_5_《统计至简》 | 鸢尾花书：从加减乘除到机器学习；上架！
Book2_Beauty-of-Data-Visualization
Book_2_《可视之美》 | 鸢尾花书：从加减乘除到机器学习，欢迎批评指正
Book1_Python-For-Beginners
Book_1_《编程不难》 | 鸢尾花书：从加减乘除到机器学习；请多多批评指正！
Book7_Visualizations-for-Machine-Learning
Book_7_《机器学习》 | 鸢尾花书：从加减乘除到机器学习；正在修改
mobile-aloha
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
act-plus-plus
Imitation Learning algorithms with Co-traing for Mobile ALOHA: ACT, Diffusion Policy, VINN
Dynamics-and-Control
Jupyter notebooks for Dynamics and Control
stock-analysis
Simple to use interfaces for basic technical analysis of stocks.
prob_ml_python
Python code for "Probabilistic Machine learning" book by Kevin Murphy
jenkinsci_matlab-plugin
The Jenkins plugin for MATLAB® enables you to easily run your MATLAB tests and generate test artifacts in formats such as JUnit, TAP, and Cobertura code coverage reports.
Image-Classification-in-MATLAB-Using-TensorFlow
This example shows how to call a TensorFlow model from MATLAB using co-execution with Python.
Xiaomi_Kernel_OpenSource
Xiaomi Mobile Phone Kernel OpenSource
Hands-On-Data-Analysis-with-Pandas
Materials for following along with Hands-On Data Analysis with Pandas.
Hands-On-Data-Analysis-with-Pandas-2nd-edition
Materials for following along with Hands-On Data Analysis with Pandas – Second Edition