How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
The main features of maxpumperla/hyperas are: Deep Learning, Deep Learning Ecosystems, Deep Learning Frameworks, Hyperparameter Tuning, Machine Learning Frameworks, Developer Tools.
Projects with overlapping indexed features include: pytorch/ignite — Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep… catalyst-team/catalyst — Accelerated deep learning R&D. maxpumperla/elephas — Distributed Deep learning with Keras & Spark. deepmind/sonnet — Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep… bsautermeister/tensorlight — TensorLight - A high-level framework for TensorFlow. autonomio/talos — Hyperparameter Experiments with TensorFlow and Keras.
Distributed Deep learning with Keras & Spark
Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep learning lifecycle manager. It provides a structured system for organizing and automating training and evaluation loops, managing data iterators and triggering event handlers at specific milestones during the model training process. The project distinguishes itself through a comprehensive suite of tools for distributed training and model evaluation. It includes utilities for synchronizing gradients and coordinating collective communication across multiple GPUs or nodes, as well as a
Hyperparameter Experiments with TensorFlow and Keras
