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 ...

Google enhances TensorFlow with deep learning capabilities and parallelism techniques for developer choice in machine language tooling.

Open source project that merges deep learning and big data frameworks is said to operate more efficiently at scale and require little change to existing Spark apps Want Google TensorFlow’s deep ...

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 ...

Abstract We introduce Bayesian code diffusion, a new deep learning program optimization strategy devised to accelerate the auto-tuning process of deep ...

Keras provides a high-level API that simplifies deep learning model development. It runs efficiently on Linux as a frontend to TensorFlow, allowing developers to rapidly prototype AI models.

Full rewrite of the deep neural network API supports Keras workflows on top of the three leading machine learning frameworks.

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 ...