AI News, Introducing Gluon: a new library for machine learning from AWS and Microsoft

Introducing Gluon: a new library for machine learning from AWS and Microsoft

Share Post by Dr. Matt Wood 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.

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.

Just like query optimizers in databases, the more a training engine knows about the network and the algorithm, the more optimizations it can make to the training process (for example, it can infer what needs to be re-computed on the graph based on what else has changed, and skip the unaffected weights to speed things up).

However, in order to achieve these optimizations, most frameworks require the developer to do some extra work: specifically, by providing a formal definition of the network graph, up-front, and then ‘freezing’ the graph, and just adjusting the weights.

Friendly API: Gluon networks can be defined using a simple, clear, concise code – this is easier for developers to learn, and much easier to understand than some of the more arcane and formal ways of defining networks and their associated weighted scoring functions.

This is in contrast to the more common, formal, symbolic definition of a network which the deep learning framework has to effectively carve into stone in order to be able to effectively optimize computation during training.

Dynamic networks are easier to manage, and with Gluon, developers can easily ‘hybridize’ between these fast symbolic representations and the more friendly, dynamic ‘imperative’ definitions of the network and algorithms.

Gluon can efficiently blend together a concise API with the formal definition under the hood, without the developer having to know about the specific details or to accommodate the compiler optimizations manually.

AWS and Microsoft announce Gluon, making deep learning accessible to all developers

New open source deep learning interface allows developers to more easily and quickly build machine learning models without compromising training performance.

The Gluon interface gives developers the best of both worlds — a concise, easy-to-understand programming interface that enables developers to quickly prototype and experiment with neural network models, and a training method that has minimal impact on the speed of the underlying engine.

We look forward to our collaboration with Microsoft on continuing to evolve the Gluon interface for developers interested in making machine learning easier to use.” “We believe it is important for the industry to work together and pool resources to build technology that benefits the broader community,” said Eric Boyd, corporate vice president of Microsoft AI and Research.

To make this happen we need to put the right tools in the right hands, and the Gluon interface is a step in this direction.” “FINRA is using deep learning tools to process the vast amount of data we collect in our data lake,” said Saman Michael Far, senior vice president and CTO, FINRA.

“We are excited about the new Gluon interface, which makes it easier to leverage the capabilities of Apache MXNet, an open source framework that aligns with FINRA’s strategy of embracing open source and cloud for machine learning on big data.” “I rarely see software engineering abstraction principles and numerical machine learning playing well together — and something that may look good in a tutorial could be hundreds of lines of code,” said Andrew Moore, dean of the School of Computer Science at Carnegie Mellon University.

The documentation is great, and I’m looking forward to teaching it as part of my computer science course and in seminars that focus on teaching cutting-edge machine learning concepts across different cities in the U.S.” “We think the Gluon interface will be an important addition to our machine learning toolkit because it makes it easy to prototype machine learning models,” said Takero Ibuki, senior research engineer at DOCOMO Innovations.

AWS offers over 90 fully featured services for compute, storage, networking, database, analytics, application services, deployment, management, developer, mobile, Internet of Things (IoT), Artificial Intelligence (AI), security, hybrid and enterprise applications, from 44 Availability Zones (AZs) across 16 geographic regions in the U.S., Australia, Brazil, Canada, China, Germany, India, Ireland, Japan, Korea, Singapore, and the UK.

AWS and Microsoft Announce Gluon to Simplify Deep Learning for Developers

With Gluon, developers can build machine learning models using a simple Python API and a range of pre-built, optimized neural network components.

This makes it easier for developers to build neural networks using simple, concise code, without sacrificing training performance.

Most deep learning frameworks require developers to define models and algorithms up-front using lengthy, complex code that is difficult to change.

Flexible Structure – Gluon brings together the neural network model and the training algorithm providing greater flexibility in the development process.

Amazon and Microsoft unveil ‘Gluon’ neural network technology, teaming up on machine learning

Amazon Web Services and Microsoft’s AI and Research Group this morning announced a new open-source deep learning interface called Gluon, jointly developed by the companies to let developers “prototype, build, train and deploy sophisticated machine learning models for the cloud, devices at the edge and mobile apps,” according to an announcement just released by the companies.

Deep learning involves training a computer to recognize patterns or unlock insights based on a set of rules for parsing a massive pool of data.

In fact, the good news is that between Microsoft and Amazon, we have a lot of cross-pollination of talent, and I think it’s helpful for this region, by the way, which is something that Silicon Valley always had.” This story was updated Thursday morning with additional details.

Introducing Gluon: a new library for machine learning from AWS and Microsoft

Share Post by Dr. Matt Wood 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.

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.

Just like query optimizers in databases, the more a training engine knows about the network and the algorithm, the more optimizations it can make to the training process (for example, it can infer what needs to be re-computed on the graph based on what else has changed, and skip the unaffected weights to speed things up).

However, in order to achieve these optimizations, most frameworks require the developer to do some extra work: specifically, by providing a formal definition of the network graph, up-front, and then ‘freezing’ the graph, and just adjusting the weights.

Friendly API: Gluon networks can be defined using a simple, clear, concise code – this is easier for developers to learn, and much easier to understand than some of the more arcane and formal ways of defining networks and their associated weighted scoring functions.

This is in contrast to the more common, formal, symbolic definition of a network which the deep learning framework has to effectively carve into stone in order to be able to effectively optimize computation during training.

Dynamic networks are easier to manage, and with Gluon, developers can easily ‘hybridize’ between these fast symbolic representations and the more friendly, dynamic ‘imperative’ definitions of the network and algorithms.

Gluon can efficiently blend together a concise API with the formal definition under the hood, without the developer having to know about the specific details or to accommodate the compiler optimizations manually.

Hello World - Machine Learning Recipes #1

Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow...

Microsoft and AWS: will collaborate in the development of a interface Artificial Intelligence

The two companies will collaborate in the development of a new programming interface for the field of Deep Learning. Microsoft and Amazon Web Services (AWS) have just announced the creation...

Deep Learning Frameworks Compared

In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples,...

112 Java Demo Synchronization 2 Using Jdk 8

Best Lecture on Java Java Creating a Run able for beginners . this video will told you about Java introduction Java Creating a Run able on the JDK 8 on latest update the latest version...

Jefferson Lab Live Virtual Field Trip

We're hosting a Google+ Virtual Field Trip into our atom smasher! We invite you to join Joanna and Steve, the hosts of Jefferson Lab's Frostbite Theater series on YouTube, for this virtual...