Google expands TensorFlow open-source tooling for accelerated machine learning development
Google enhances TensorFlow with deep learning capabilities and parallelism techniques for developer choice in machine language tooling.
Google enhances TensorFlow with deep learning capabilities and parallelism techniques for developer choice in machine language tooling.
TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano and Apache MXNet are the seven most popular frameworks for developing AI applications.
Databricks and Hugging Face integrate Apache Spark to more seamlessly load and transform data for AI model training and fine-tuning.
The open-source PyTorch project is widely used for machine learning (ML) training. Its anticipated 2.0 version is now generally available.
IBM has built out a cloud-native AI supercomputer from affordable, commodity hardware for foundation model–training R&D initiatives.
Nvidia AI Enterprise 3.0 released today, with new software capabilities to help effectively build and deploy AI-driven applications.
The new PyTorch 2.0 promises to accelerate ML training and development, while being backward-compatible with existing PyTorch code.
IBM Research has contributed code to the open-source PyTorch machine learning project that could help to significantly accelerate training.
Revelations, innovations and questions about AI unfolded in VentureBeat’s news coverage this week.
Meta announced today that its AI research framework PyTorch has a new home: It is moving to an independent foundation.