Keras ships with support for three back-end deep learning frameworks: TensorFlow, CNTK, and Theano. Amazon is currently working on developing a MXNet back-end for Keras.

In the deep learning sphere, there are three major GPU-accelerated libraries: cuDNN, which I mentioned earlier as the GPU component for most open source deep learning frameworks; TensorRT, which ...

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 ...

This week yielded a new benchmark effort comparing various deep learning frameworks on a short list of CPU and GPU ... mentioned achieve very high speedups compared to their CPU only versions because ...

TensorFlow, developed by Google, is one of the most widely used deep learning frameworks. It provides robust support for training and deploying neural networks, with features such as automatic ...

As the AI landscape continues to evolve, a new version of the popular Caffe open source deep learning framework has been released. Caffe2 is backed by Facebook and features a wide array of ...

As with CUDA, ROCm is an ideal solution for AI applications, as some deep-learning frameworks already support a ROCm backend (e.g., TensorFlow, PyTorch, MXNet, ONNX, CuPy, and more).

At Nvidia’s GPU Technology Conference 2015 in March, much of the focus for CEO Jen-Hsun Huang and other company executives was on the growing field of deep learning—the idea that machines can ...

It's also worth noting that the leading deep learning frameworks all support Nvidia GPU technologies. Moreover, when using Tesla V100 GPUs, these are up to 3 times faster than using Pascal-based ...

But just how deep is this moat in reality? As you might have already guessed, the answer really depends on what you're trying to achieve. If you're doing low-level programming for GPUs, the CUDA ...