If you actually need a deep learning model, PyTorch and TensorFlow are both good choices ...

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models.

When deploying large-scale deep learning applications, C++ may be a better choice than Python to meet application demands or to optimize model performance. Therefore, I specifically document my recent ...

Other optimizations to TensorFlow components resulted in significant CPU performance gains for various deep learning models. Using the Intel MKL imalloc routine, both TensorFlow and the Intel MKL-DNN ...

Google last week open sourced TensorFlow, a new machine learning library used in deep learning projects. Even though the Web giant has just started using the software in its products, it apparently ...

TensorFlow not only makes it possible for developers to include the spoils of deep learning into their products, but it makes devices and software significantly more intelligent and easier to use.

At Google’s inaugural TensorFlow Dev Summit in Mountain View, California, today, Google announced the release of version 1.0 of its TensorFlow open source framework for deep learning, a trendy ...

Caffe, CNTK, DeepLearning4j, Keras, MXNet, and TensorFlow are deep learning frameworks. Scikit-learn and Spark MLlib are machine learning frameworks. Theano straddles both categories.

Xiaoyi Lu from Ohio State University gave this talk at the 2019 OpenFabrics Workshop in Austin. "Google's TensorFlow is one of the most popular Deep Learning (DL) frameworks. We propose a unified way ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...