Remember how the introduction of smartphones and mobile apps had brought a paradigm shift in our lives? Now machine learning (ML) is bringing a new era in mobile app development! The machine learning enabled mobile apps do not require explicit programming anymore to perform tasks. Rather they can collect and analyze information which is needed to draw conclusions and also learn automatically and improve from experience during program performance. However, the process of learning involves sophisticated algorithms which teaches machines and enables them to accumulate previous experience in order to make decisions and adapt when exposed to new data.

Microsoft has developed open-source software for crafting artificial intelligence models. But in recent months, the company has changed course, opting to work more closely with Facebook and contribute to the development of Facebook's own flavor of free AI software. Microsoft hasn't made a big deal about the change. But it reflects a willingness to back software that comes from other major technology companies, rather than focusing only on its own platforms. Google is the company behind the most popular open-source AI software, TensorFlow, which became available in late 2015.

One big change is support for running trained machine learning models as part of Windows applications, taking advantage of local GPUs to accelerate machine learning applications. Building a machine learning application can be a complex process. Training a model can require a lot of data, and a considerable amount of processing power. That's fine if you've got access to a cloud platform and lots of bandwidth, but what if you want to take an existing model from GitHub and run it on a PC? Trained machine learning models are an ideal tool for bringing the benefits of neural networks and deep learning to your applications. All you should need to do is hook up the appropriate interfaces, and they should run as part of your code.

One big change is support for running trained machine learning models as part of Windows applications, taking advantage of local GPUs to accelerate machine learning applications. Building a machine learning application can be a complex process. Training a model can require a lot of data, and a considerable amount of processing power. That's fine if you've got access to a cloud platform and lots of bandwidth, but what if you want to take an existing model from GitHub and run it on a PC? Trained machine learning models are an ideal tool for bringing the benefits of neural networks and deep learning to your applications. All you should need to do is hook up the appropriate interfaces, and they should run as part of your code.

PINSLAND.COM Microsoft Cognitive Service Microsoft Cognitive ToolKit 2. Who is Marvin?

A toolkit used across Microsoft to achieve breakthroughs in artificial intelligence is generally available to the public via an open-source license, a team of researchers and software engineers announced today. "The 2.0 version of the toolkit is now in full release," said Chris Basoglu, a partner engineering manager at Microsoft. He has played a key role in developing Microsoft Cognitive Toolkit (previously known as CNTK). The full release of Microsoft Cognitive Toolkit 2.0 for use in production-grade and enterprise-grade deep learning workloads includes hundreds of new features incorporated since the beta to streamline the process of deep learning and to ensure the toolkit's seamless integration throughout the wider AI ecosystem. New with the full release today is support for Keras, a user-friendly open-source neural network library that is popular with developers working on deep learning applications.

As both projections and expectations for big data accelerate, enterprise data groups find themselves working in a rapidly evolving arena both encouraging in its possibilities and vexing in its limitations. In 2018, big data will continue along both lines -- offering more options with greater accessibility while frustrating enterprises looking for all-encompassing answers to complex questions. Excited: Machine-learning methods become more accessible The emergence of production-ready machine-learning tools and models continue to be a reason to be excited about big data in 2018. Machine-learning models can accurately perform recognition of specific patterns in data streams. In environments already inundated with data, this capability provides high value and distinct advantages, and the industry has responded accordingly.

This is the age of artificial intelligence. Machine Learning and predictive analytics are now established and integral to just about every modern businesses, but artificial intelligence expands the scale of what's possible within those fields. It's what makes deep learning possible. Systems with greater ostensible autonomy and complexity can solve similarly complex problems. If Deep Learning is able to solve more complex problems and perform tasks of greater sophistication, building them is naturally a bigger challenge for data scientists and engineers.

Deep learning systems have long been tough to work with, due to all the fine-tuning and knob-twiddling needed to get good results from them. Gluon is a joint effort by Microsoft and Amazon Web Services do reduce all that fiddling effort. Gluon works with the Apache MXNet and Microsoft's Cognitive Toolkit frameworks to optimize deep-learning network training on those systems. The problem with steps 1 and 2 is that they're tedious and inflexible. Hard-coding a network is slow, and altering that coding to improve the network's behavior is also slow.

Today, AWS and Microsoft announced Gluon, a new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components. Developers who are new to machine learning will find this interface more familiar to traditional code, since machine learning models can be defined and manipulated just like any other data structure. More seasoned data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures, all without sacrificing training speed. Gluon is available in Apache MXNet today, a forthcoming Microsoft Cognitive Toolkit release, and in more frameworks over time.