A Python tool to perform deep learning experiments on various hyperspectral datasets.
The main features of eecn/hyperspectral-classification are: Deep Learning and Computer Vision, Deep Learning Frameworks.
Open-source alternatives to eecn/hyperspectral-classification include: karpathy/neuraltalk2 — Neuraltalk2 is a deep learning vision system designed for automatic image captioning. Built with PyTorch, it utilizes… sshuair/torchvision-enhance — TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch. mrdbourke/zero-to-mastery-ml — This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter… udacity/self-driving-car — This is an open-source autonomous driving perception pipeline that processes camera and lidar sensor data to detect,… dipanjans/practical-machine-learning-with-python — This project serves as a comprehensive educational resource and curriculum for mastering machine learning and deep… alrojo/tensorflow-tutorial — Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE.
Neuraltalk2 is a deep learning vision system designed for automatic image captioning. Built with PyTorch, it utilizes a hybrid architecture that combines a convolutional neural network encoder with a recurrent neural network decoder to generate textual descriptions from visual input. The project features a GPU-accelerated training pipeline capable of distributing workloads across multiple graphics processing units through multi-process distribution. It supports the generation of descriptions for both static image files and real-time video streams. The framework includes capabilities for enco
TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch.
This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter Notebooks. It serves as a comprehensive guide for mastering the Python data science toolkit, providing structured tutorials for numerical computing, tabular data manipulation, and statistical visualization. The curriculum includes specific implementation guides for Scikit-Learn and a practical course on TensorFlow for constructing, training, and deploying neural networks and computer vision models. It covers the end-to-end process of building predictive models, from initial pr
This project serves as a comprehensive educational resource and curriculum for mastering machine learning and deep learning within the Python data science ecosystem. It provides a structured collection of tutorials and code examples designed to guide users through the end-to-end process of building, training, and deploying predictive models. The material focuses on practical implementation, covering the construction of machine learning pipelines that integrate data processing, feature engineering, and model training. It distinguishes itself by offering hands-on guidance for complex domains, i