[Vuong Nguyen] clearly knows his way around artificial intelligence accelerator hardware, creating ztachip: an open source implementation of an accelerator platform for AI and traditional image ...

Overview: The choice of deep learning frameworks increasingly reflects how AI projects are built, from experimentation to ...

Overview NumPy and Pandas form the core of data science workflows. Matplotlib and Seaborn allow users to turn raw data into ...

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 ...

Ben Khalesi writes about where artificial intelligence, consumer tech, and everyday technology intersect for Android Police. With a background in AI and Data Science, he’s great at turning geek speak ...

It's possible to create neural networks from raw code. But there are many code libraries you can use to speed up the process. These libraries include Microsoft CNTK, Google TensorFlow, Theano, PyTorch ...

While you can train simple neural networks with relatively small amounts of training data with TensorFlow, for deep neural networks with large training datasets you really need to use CUDA-capable ...

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 ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article introduces practical methods for ...

Deep learning is changing our lives in small and large ways every day. Whether it’s Siri or Alexa following our voice commands, the real-time translation apps on our phones, or the computer vision ...