oplad's repositories
azureml-examples
Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
pytorch_geometric
Graph Neural Network Library for PyTorch
cuda-samples
Samples for CUDA Developers which demonstrates features in CUDA Toolkit
flatbuffers
FlatBuffers: Memory Efficient Serialization Library
meta-freescale
Layer containing NXP hardware support metadata
psychopy
For running psychology and neuroscience experiments
yocto-dockerfiles
This repository is for -base and -builder containers for building derivative containers (such as poky-container) for containerized Yocto Project builds.
docker_practice
Learn and understand Docker&Container technologies, with real DevOps practice!
WSL2-Linux-Kernel
The source for the Linux kernel used in Windows Subsystem for Linux 2 (WSL2)
skorch
A scikit-learn compatible neural network library that wraps PyTorch
SoH_estimation_of_Lithium-ion_battery
State of health (SOH) prediction for Lithium-ion batteries using regression and LSTM
meta-imx
i.MX Yocto Project i.MX BSP Layer
googletest
GoogleTest - Google Testing and Mocking Framework
toaster-container
A container to run the 'bitbake' aware 'Toaster' Django front-end to more easily build Yocto Project recipes and images, as well as collect build analytics.
DeepLearningExamples
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
imx-docker
i.MX Docker
pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
scikit-learn
scikit-learn: machine learning in Python
fsl-community-bsp-platform
BSP platform manifest
vnpy
基于Python的开源量化交易平台开发框架
tensorflow-dataset
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
scientific-python-lectures
Tutorial material on the scientific Python ecosystem
machine-learning-pytorch
Code Repository for Machine Learning with PyTorch and Scikit-Learn
hands-on-ml3
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
onnx
Open standard for machine learning interoperability
Osprey
[CVPR2024] The code for "Osprey: Pixel Understanding with Visual Instruction Tuning"
tutorials
Tutorials for creating and using ONNX models
hands-on-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.