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 ...

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 ...

At the start of May, I decided to get TensorFlow Developer Certified. So I set myself up with a curriculum to sharpen my skills and took the certification — turns out, I passed. Let me tell you how I ...

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 ...

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

Besides putting a Raspberry Pi to work on a mini Mars rover, it's now going to be a lot easier to use Google's TensorFlow artificial-intelligence framework with the low-powered computer. Developers ...

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 ...

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

Despite some of the inherent complexities of using FPGAs for implementing deep neural networks, there is a strong efficiency case for using reprogrammable devices for both training and inference.