Deep Attention Recurrent Q-Network
The main features of 5vision/darqn are: Deep Learning Frameworks.
Open-source alternatives to 5vision/darqn include: albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… alrojo/tensorflow-tutorial — Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE. amznlabs/amazon-dsstne — Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with… andersbll/deeppy — Deep learning in Python. apache/incubator-mxnet — Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying… alankbi/detecto — Build fully-functioning computer vision models with PyTorch.
Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for deep learning models. It provides a collection of transformations that modify pixel values and spatial geometry to increase the diversity of training samples and improve model generalization. The library supports both 2D image augmentation and 3D volumetric data augmentation. It handles a variety of labels alongside images, ensuring that bounding boxes, keypoints, and segmentation masks remain accurately aligned when spatial transformations are applied. The tool incorporates
Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE
Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with sparse inputs and outputs. It provides a model-parallel training framework and a GPU-accelerated sparse engine to support memory-intensive networks. The framework is specifically designed for recommendation system training and large-scale sparse learning. It enables the distribution of large weight matrices and embedding tables across multiple GPU devices to handle models that exceed the memory capacity of a single processor. The project covers a broad range of capabilities in
