Google announced an update to its open-source framework TensorFlow that will now run training process for creating machine learning models over hundreds of machines aligned. According to The Verge, Google opened up its TensorFlow last year for companies that wants to build their own artificial intelligence application using the same open-source library the search engine applies to power everything from photo analytics and automated email replies. Google tried extending its platform to other computer servers by publicly releasing a version that could only run a single machine. Now, Google has updated a new version of TensorFlow with a feature that will enable to run distributed computing across multiple machines at the same time. Engineering leader of TensorFlow Rajat Monga said the reason why TensorFlow's multi-server version was delayed for release because they found it difficult to adapt the open-source software to be usable outside of the highly customized data centers of Google.
Google open source machine learning library TensorFlow 2.0 is now available for public use, the company announced today. The alpha version of TensorFlow 2.0 was first made available this spring at the TensorFlow Dev Summit alongside TensorFlow Lite 1.0 for mobile and embedded devices, and other ML tools like TensorFlow Federated. TensorFlow 2.0 comes with a number of changes made in an attempt to improve ease of use, such as the elimination of some APIs thought to be redundant and a tight integration and reliance on tf.keras as its central high-level API. Initial integration with the Keras deep learning library began with the release of TensorFlow 1.0 in February 2017. It also promises three times faster training performance when using mixed precision on Nvidia's Volta and Turing GPUs, and eager execution by default means the latest version of TensorFlow delivers runtime improvements.
The art and science of training neural networks from large data sets in order to make predictions or classifications has experienced a major transition over the past several years. Through popular and growing interest from scientists and engineers, this field of data analysis has come to be called deep learning. Put succinctly, deep learning is the ability of machine learning algorithms to acquire feature hierarchies from data and then persist those features within multiple non-linear layers which comprise the machine's learning center, or neural network. Two years ago, questions were mainly about what deep learning is, and how it might be applied to problems in science, engineering, and finance. Over the past year, however, the climate of interest has changed from a curiosity about what deep learning is, and into a focus on acquiring hardware and software in order to apply deep learning frameworks to specific problems across a wide range of disciplines.
Google's Machine Learning framework TensorFlow was open-sourced in November 2015  and has since built a growing community around it. TensorFlow is supposed to be flexible for research purposes while also allowing its models to be deployed productively. This work is aimed towards people with experience in Machine Learning considering whether they should use TensorFlow in their environment. Several aspects of the framework important for such a decision are examined, such as the heterogenity, extensibility and its computation graph. A pure Python implementation of linear classification is compared with an implementation utilizing TensorFlow. I also contrast TensorFlow to other popular frameworks with respect to modeling capability, deployment and performance and give a brief description of the current adaption of the framework.
Open source deep learning neural networks are coming of age. There are several frameworks that are providing advanced machine learning and artificial intelligence (A.I.) capabilities over proprietary solutions. How do you determine which open source framework is best for you? In "Big data – a road map for smarter data," I describe a set of machine learning architectures that will provide advanced capabilities to include image, handwriting, video, and speech recognition, natural language processing and object recognition. There is no perfect deep learning network that will solve all your business problems.