ONNX Runtime inference engine is capable of executing ML models in different HW environments, taking advantage of the neural network acceleration capabilities.

Microsoft and Xilinx worked together to integrate ONNX Runtime with the VitisAI SW libraries for executing ONNX models in the Xilinx U250 FPGAs. We are happy to introduce the preview release of this capability today.

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[06:15] Demo by PeakSpeed for satellite imaging Orthorectification

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Using data for machine learning and analytics can potentially expose private data. 

How can we leverage data while ensuring that private information remains private?

In this video, learn how differential privacy can be used to preserve privacy and get a demo on how you can use newly released open source system, WhiteNoise, to put DP into your applications.

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Build 2020 starts today and here’s a special Build edition of the AI Show that covers the Bot Framework Composer.

Bot Framework Composer is an open source, integrated bot development environment available as a cross platform application on GitHub.

Bot Framework Composer provides a one stop shop environment that seamlessly integrates several key aspects of building a conversational application including language understanding, dialog modeling, language generation, memory management, and integration with external resources.

In this session, you will learn about advanced understanding and language generation capabilities offered by Bot Framework Composer. The following language understanding topics are covered in this session – flexible slot filling, interruption handling, handling local intents, confirmation & correction experience for language understanding.

You will also learn about building bots with advanced language generation capabilities including conditional response generation, media/ card generation with data binding. Introducing Bot Framework Composer is a recommended prerequisite for this session.

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Are you curious how data scientists and researchers train agents that make decisions? 

Learn how to use reinforcement learning to optimize decision making using Azure Machine Learning.  We show you how to get started.

Time Index:

  • [00:36] – What is reinforcement learning?
  • [01:37] – How do reinforcement learning algorithms work?
  • [04:10] – Reinforcement Learning on Azure – Notebook sample
  • [05:17] – Reinforcement Learning Estimator
  • [07:21] – Sample training Python script
  • [09:06] – Training Result
  • [10:15] – What kind of problems can you solve with reinforcement learning?

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Building forecasts is an integral part of any business, whether it’s revenue, inventory, sales, or customer demand.

Building machine learning models can be a time-consuming and complex with many factors to consider, such as iterating through algorithms, tuning your hyperparameters and feature engineering.

These choices multiply with time series data, with additional considerations of trends, seasonality, holidays and effectively splitting training data.

Forecasting within automated machine learning (ML) takes these factors into consideration and includes capabilities that improve the accuracy and performance of our recommended models.

This session will highlight the forecasting features of Automated ML and how to leverage them.

Time index:

  • [00:35] – What is time-series forecasting?
  • [01:30] – Simplify ML with Automated ML
  • [02:30] – DriveTime customer scenario
  • [04:15] – Features & Functionality
  • [05:20] – Demo

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This episode of the  helps the user understand how to add custom skills to a skillset in Azure Cognitive Search.

It explains what it means to enrich content as part of the ingestion pipeline.

The video describe the interface for a custom skill and how you can create your own custom skill. It introduces you to power skills so you don’t have to start from scratch.

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Indexing and Querying are pivotal when building the search platform.

This video explains how Azure cognitive search works and how developers can add their own input to it.

The focus is on explaining how information processing in search engine is divided in to indexing and querying,  important steps for both the processes, document processing, adding indexing time, creating inverted index, retrieving documents and writing the results.

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Search based apps are crucial for any business or enterprise for a variety of scenarios and end goals.

Heres a video that highlights very specific capabilities for ingesting, enriching, indexing and visualization of the data.

Watch to better understand what is azure cognitive search, the underlying technology, what unique capabilities does it offer and how can they can get started to build great apps for web, mobile or line of business.

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