Free Webinars on Cognitive Toolkit with Batch AI, DSVM & Document Collection Analysis

Join us at a set of three exciting webinars starting on Tuesday next week where we’ll show you how to train distributed convolution neural networks using Microsoft Cognitive Toolkit (aka CNTK) and Batch AI, how to do AI development using the latest version of the Data Science Virtual Machine (DSVM), and how to use Document Collection Analysis to gain insights from large sets of documents and serve your downstream NLP tasks. All sessions are entirely free, of course. More on each session below – be sure to click on the links attached to the titles of these sessions to reserve your spot now.

Train a Distributed Convolutional Neural Network using Microsoft Cognitive Toolkit and Batch AI

Deep Learning has become the de facto standard for most computer vision tasks since its breakthrough year in 2012 at the ImageNet Challenge. In the past few years, taking advantage of more complex and deeper neural network architectures, deep learning algorithms have met and exceeded human-level performance in image recognition. Increasingly, computer vision applications are starting to apply deep learning technologies, with plenty of them seeing tremendous success. Nevertheless, training deep learning networks on a large data set remains quite challenging. The sheer amount of computation needed, such as training a convolutional neural network on the Sport 1M data set, can take months. Combine that with the complexities of hyper-parameter tuning, and the community desperately needs tools to help train deep learning networks on multiple servers with multiple GPUs.

In this session, we’ll show how to use Microsoft’s Cognitive Toolkit, also known as CNTK, to train a convolutional neural network over multiple nodes and multiple GPUs. CNTK has unique advantages in speed and scalability. In the tutorial, we’ll show that CNTK achieves almost linear scalability. CNTK achieves such scalability via advanced algorithms such as 1-bit SGD and block-momentum SGD (we will explain these algorithms during the session).

This webinar runs from 10-11 AM Pacific next Tuesday, October 24th, and is presented by Anusua Trivedi, Data Scientist, and Avi Thaker, Software Engineer, at Microsoft.

AI Development in Azure using Data Science Virtual Machines (DSVM)

Azure DSVM (http://aka.ms/DSVM) provides a comprehensive development and production environment to Data Scientists and AI-savvy developers. DSVMs are specialized virtual machine images that have been curated, configured, tested and heavily used by Microsoft engineers and data scientists. DSVM is an integral part of the Microsoft AI Platform and is available for customers to use through the Microsoft Azure cloud. In this session, we will first introduce DSVM, familiarize attendees with the product, including our newest offering, namely Deep Learning Virtual Machines (DLVMs). That will be followed by technical deep-dives into samples of end-to-end AI development and deployment scenarios that involve deep learning. We will also cover scenarios involving cloud based scale-out and parallelization.

This webinar runs from 10-11 AM Pacific on Thursday next week, October 26th, and is presented by Gopi Kumar, Principal Program Manager, Paul Shealy, Senior Software Engineer, and Barnam Bora, Program Manager, at Microsoft.

Document Collection Analysis 

With the extremely large volumes of data, especially unstructured text data, that are being collected every day, a huge challenge facing customers is the need for tools and techniques to organize, search, and understand this vast quantity of text. This webinar demonstrates an efficient and automated end-to-end workflow around analyzing large document collections and serving your downstream NLP tasks. We’ll demonstrate how to summarize and analyze a large collection of documents, including techniques such as phrase learning, topic modeling, and topic model analysis using the Azure ML Workbench.

This webinar runs from 10-11 AM Pacific on Tuesday, October 31st, and will be presented by Ke Huang, Data Scientist at Microsoft.

Be sure to mark your calendars now – we look forward to seeing you at these sessions!

ML Blog Team