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 ...

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 ...

TensorFlow is an open source software library developed by Google for numerical computation with data flow graphs. This TensorFlow guide covers why the library matters, how to use it and more.

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 ...

I had great fun writing neural network software in the 90s, and I have been anxious to try creating some using TensorFlow. Google’s machine intelligence framework is the new hotness right now. And ...

TensorFlow is an open source machine learning framework developed by Google, designed to build and train AI models for a wide range of applications. The tool is widely used in industries such as ...

Natural language processing libraries, including NLTK, spaCy, Stanford CoreNLP, Gensim and TensorFlow, provide pre-built tools for processing and analyzing human language. Natural language processing ...

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, ...

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.