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 version r1.5, Google's open source machine learning and neural network library is more capable, more mature, and easier to learn and use If you looked at TensorFlow as a deep learning framework ...

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 ...

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 ...

TensorFlow was created simply to develop your own machine-learning (ML) models. You might even experience it daily and not know it, like recommendation systems that suggest the next YouTube video, ...

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

Data science is often cited as one of the main reasons for Python's growing popularity. But while people are definitely using Python for data analysis and machine learning, not many of those using ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Cory Benfield discusses the evolution of ...

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 ...

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.