TensorFlow is your ally for scalability and production. PyTorch is your friend for research flexibility and ease of use. The choice depends on your project needs, expertise, and long-term goals.

TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano and Apache MXNet are the seven most popular frameworks for developing AI applications.

TensorFlow is available on Windows, macOS, and Linux and can be installed via Python’s pip package manager. It supports cloud platforms like Google Cloud, AWS, and Azure for enterprise deployment.

Unveiled November 27, and accessible from GitHub, Keras 3.0 enables developers to run Keras workflows on top of the Jax, TensorFlow, or PyTorch machine learning frameworks, featuring large-scale ...

Bibek Bhattarai details Intel's AMX, highlighting its role in accelerating deep learning on CPUs. He explains how AMX ...

Overview  Python remains the most popular and versatile language for AI development.Julia and Rust are gaining ground for ...

Accelerating deep learning with oneDNN According to Slintel, TensorFlow has a market share of 37%. Kaggle’s 2021 State of Data Science and Machine Learning survey pegged TensorFlow’s usage at 53%.

There are tools to convert Tensorflow, PyTorch, XGBoost, and LibSVM models into formats that CoreML and ML Kit understand. But other solutions try to provide a platform-agnostic layer for training ...

Qubrid AI, a leader in hybrid GPU cloud solutions and AI infrastructure & tools, today announced a major upgrade to its GPU ...