Learn about some of the best Python libraries for programming artificial Intelligence, machine learning, and deep learning. A lot of software developers are drawn to Python due to its vast collection ...

TensorFlow 2.0 improves performance on Volta and Turing GPUs, increases deployment options, boasts tighter integration with Keras, and makes the platform easier for Python frequents. TensorFlow, the ...

Google's open source framework for machine learning and neural networks is fast and flexible, rich in models, and easy to run on CPUs or GPUs What makes Google Google? Arguably it is machine ...

Around the Hackaday secret bunker, we’ve been talking quite a bit about machine learning and neural networks. There’s been a lot of renewed interest in the topic recently because of the success of ...

Over the past year I’ve reviewed half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

Recent developments in Python-based system utilities are enhancing how users monitor, diagnose, and optimize PCs. From psutil’s cross-platform process metrics to advanced hardware sensor access via ...

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 ...

Python and R each excel in different aspects of data science—Python leads in machine learning, automation, and handling large datasets, while R is strong in statistical modeling and high-quality ...