4 مستودعات
Awesome GitHub RepositoriesML Batch Training Optimizations
Techniques for optimizing the grouping and processing of data batches to improve neural network training efficiency.
Distinct from Batch Processing: Focuses on stochastic gradient descent batching for ML training rather than general bulk data operations.
Explore 4 awesome GitHub repositories matching data & databases · ML Batch Training Optimizations. Refine with filters or upvote what's useful.
Awesome ML Batch Training Optimizations GitHub Repositories
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- morvanzhou/pytorch-tutorial
MorvanZhou/PyTorch-Tutorial
8,458عرض على GitHubThis project is a collection of PyTorch learning resources and educational guides designed to teach the construction and training of neural networks. It serves as a comprehensive deep learning tutorial covering various model architectures and practical implementation strategies. The resources provide specific guidance on implementing computer vision tasks, such as image classification and synthetic imagery generation, as well as reinforcement learning agents using value networks and experience replay. It also covers sequential data modeling through recurrent networks and generative modeling u
Applies batch processing and acceleration techniques to improve the efficiency and stability of the learning process.
Jupyter Notebookautoencoderbatchbatch-normalization - tensorflow/nmt
tensorflow/nmt
6,461عرض على GitHubThis project is a neural machine translation system used to build models that automatically translate text from one language to another. It utilizes sequence-to-sequence modeling to transform variable-length input sequences into corresponding output sequences. The system implements bidirectional recurrent neural network encoding and attention mechanisms to capture contextual information and focus on specific parts of the source text during translation. To manage training and inference, it employs separate computational graphs and supports distributing model layers across multiple GPU devices.
Optimizes training efficiency by grouping similarly sized sentences into batches to reduce padding.
Python - kevinmusgrave/pytorch-metric-learning
KevinMusgrave/pytorch-metric-learning
6,328عرض على GitHubPyTorch Metric Learning is an open-source library for training neural networks to produce similarity-preserving embedding spaces. It provides a modular framework where interchangeable loss functions, mining strategies, and evaluation tools can be composed to learn representations that map similar items to nearby points and dissimilar items to distant points in the embedding space. The library distinguishes itself through a highly configurable architecture that separates concerns across several interchangeable components. Users can assemble custom loss functions from pluggable distance metrics
Implements cross-batch memory queues that store embeddings from previous iterations for contrastive learning.
Pythoncomputer-visioncontrastive-learningdeep-learning - morvanzhou/tensorflow-tutorial
MorvanZhou/Tensorflow-Tutorial
4,334عرض على GitHubThis project is a collection of educational resources and reference implementations for neural network development using TensorFlow. It serves as a comprehensive learning course, machine learning curriculum, and practical implementation guide for building deep learning architectures. The codebase provides instructional materials and examples covering a wide range of model types, including convolutional neural networks for image classification, recurrent networks and long short-term memory cells for sequential data, and autoencoders for generative modeling. It also includes implementations for
Implements stochastic gradient descent batching to manage computational load during neural network training.
Pythonautoencoderclassificationcnn
