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Wrapper library for text generation / language models at character and word level with RNNs in TensorFlow
The main features of batzner/tensorlm are: Deep Learning Ecosystems, Deep Learning Frameworks, Machine Learning Frameworks.
Projects with overlapping indexed features include: cornellius-gp/gpytorch — GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process… ctallec/pyvarinf — Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch. autonomio/talos — Hyperparameter Experiments with TensorFlow and Keras. catalyst-team/catalyst — Accelerated deep learning R&D. bsautermeister/tensorlight — TensorLight - A high-level framework for TensorFlow. danielegrattarola/spektral — Graph Neural Networks with Keras and Tensorflow 2.
Hyperparameter Experiments with TensorFlow and Keras
TensorLight - A high-level framework for TensorFlow
GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process models. It provides a system for Gaussian process modeling and uncertainty estimation, designed to perform efficient matrix operations on graphics hardware. The framework features a modular kernel system for constructing custom covariance functions and modeling complex data dependencies. It specifically integrates Gaussian processes with deep neural networks to create hybrid models for regression and classification. The system employs numerical linear algebra techniques, inclu
