In the dynamic world of machine learning, two heavyweight frameworks often dominate the conversation: PyTorch and TensorFlow. These frameworks are more than just a means to create sophisticated ...

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models. If you’re starting a new machine learning or deep learning ...

Not every regression or classification problem needs to be solved with deep learning. For that matter, not every regression or classification problem needs to be solved with machine learning. After ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The big artificial intelligence (AI) news at Google I/O today is the ...

Overview: Top Python frameworks streamline the entire lifecycle of artificial intelligence projects from research to ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Martha Lambert introduces the "Observability ...

A new version of the machine learning technology that Facebook Inc. uses to do some 6 billion language translations a day will soon be available more widely. The social network giant, and engineering ...

JAX is one of the fastest-growing tools in machine learning, and this video breaks it down in just 100 seconds. We explain how JAX uses XLA, JIT compilation, and auto-vectorization to turn ordinary ...

At last week's Spark AI Summit Europe, we had the chance to discuss with some of the rock stars of this community. MLFlow's new version was presented in Databricks Chief Technologist Matei Zaharia's ...

At Cloud Next 2019, Google announced the launch of AI Platform, a comprehensive machine learning service for developers and data scientists. Google has many investments in the space of machine ...