Demystifying Machine and Deep Learning for Developers
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The document discusses different machine learning techniques including regression, classification, clustering, anomaly detection, and recommendation. It then provides examples of data and labels that could be used for training models with these techniques. It also discusses topics like updating model weights, learning rates, and derivatives or gradients of cost functions. Finally, it provides examples of using Azure machine learning services to train models with cloud resources and deploy them for consumption.
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[Connect(); // Japan 2016] Microsoft の AI 開発最新アップデート ～ Cognitive Services からA...Naoki (Neo) SATO Microsoft held an event to update attendees on their latest developments in AI, including Cognitive Services, Azure Machine Learning, and the Cognitive Toolkit. The presentation covered Microsoft's broad portfolio of AI products and services, from APIs and development tools to pre-trained deep learning models. It also demonstrated new capabilities in areas like computer vision, natural language processing, and machine translation using neural networks.
TAXTRON Profile_PDF
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Work Portfolio
Work PortfolioAmit Prabhudesai This document provides a summary of Amit Prabhudesai's work portfolio. It outlines his educational background and work experience in image processing and computer vision. It then describes several projects he has worked on, including human detection using Adaboost for surveillance video, optimizing a Lane Departure Warning system for a Texas Instruments DSP, developing video analytics software for retail store customer counting and queue detection, and an Automatic Fingerprint Identification System. It also lists some relevant trainings and mentorship activities.
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Complete artificial intelligence workloads faster using Microsoft Azure virtu...Principled Technologies The Edsv5-series VMs achieved greater AI performance than Edsv4-series VMs featuring older Intel Xeon processors
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Innovation with ai at scale on the edge vt sept 2019 v0
Innovation with ai at scale on the edge vt sept 2019 v0Ganesan Narayanasamy This document provides a summary of a presentation on innovating with AI at scale. The presentation discusses:
1. Implementing AI use cases at scale across industries like retail, life sciences, and transportation.
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Microsoft AI Platform Overview
Microsoft AI Platform OverviewDavid Chou Microsoft provides an AI platform and tools for developers to build, train, and deploy intelligent applications and services. Key elements of Microsoft's AI offerings include:
- A unified AI platform spanning infrastructure, tools, and services to make AI accessible and useful for every developer.
- Powerful tools for AI development including deep learning frameworks, coding and management tools, and AI services for tasks like computer vision, natural language processing, and more.
- Capabilities for training models at scale using GPU accelerated compute on Azure and deploying trained models as web APIs, mobile apps, or other applications.
- A focus on trusted, responsible, and inclusive AI that puts users in control and augments rather than replaces human
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DCEU 18: From Legacy Mainframe to the Cloud: The Finnish Railways Evolution w...Docker, Inc. Niko Virtala - Cloud Architect, VR Group (Finnish Railways)
In 2016, Finnish Railways reservation system and many other systems were monolithic applications running on mainframe or local datacenters. They began a containerization project focused on modernizing the reservation system. The invest paid off. Today, they have containerized multiple applications, running both on-premises and on AWS today. That’s allowed Finland’s leading public transport agency to shut down a data center and become a technology innovator. In this session, Finnish Rail will explain the processes and tools they used to build a multi-cloud strategy that lets them take advantage of geo-location and cost advantages to run in AWS, Azure and soon Google Cloud. You’ll learn: - How to implement a successful multi-cloud deployment - What challenges you can expect to face along the way - The processes and tools that are critical part of a successful project.
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AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...James Serra Discover, manage, deploy, monitor – rinse and repeat. In this session we show how Azure Machine Learning can be used to create the right AI model for your challenge and then easily customize it using your development tools while relying on Azure ML to optimize them to run in hardware accelerated environments for the cloud and the edge using FPGAs and Neural Network accelerators. We then show you how to deploy the model to highly scalable web services and nimble edge applications that Azure can manage and monitor for you. Finally, we illustrate how you can leverage the model telemetry to retrain and improve your content.
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Barrie Mullins, Director of Technical Product Marketing at NVIDIA, presents the “Streamlining Development of Edge AI Applications” tutorial at the May 2021 Embedded Vision Summit.
Edge AI provides benefits for cost, latency, privacy, and connectivity. Developing and deploying optimized, accurate and effect AI on edge-based systems is a time-consuming, challenging and complex process. In this session, you will learn how NVIDIA makes it easier for developers to build, deploy, maintain and manage embedded edge products.
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한컴MDS_Microsoft Azure IoT Overview
한컴MDS_Microsoft Azure IoT OverviewHANCOM MDS Microsoft CSP(Cloud Solution Provider) 한컴MDS에서 Azure IoT를 소개합니다.
1. Azure IoT Overview
2. SaaS
- Azure IoT Central
3. PaaS
- Azure IoT Solutions Accelerator
- Azure IoT Components
4. Azure IoT Edge
5. Case Study
- 화재 감지 모니터링
- Kiosk 상태 모니터링 및 디바이스 관리
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Catalogue 2019 Presagis
Catalogue 2019 PresagisPresagis Presagis delivers simulation and graphics software, and services to defense and aeronautic organizations worldwide. We provide end-users, system integrators, developers, and manufacturers with advanced tools and dedicated services to help them achieve rich, immersive virtual environments, and helping design the cockpits of tomorrow.
Leverage the power of machine learning on windows
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The Content was modified from the Microsoft Content team.
Deck Owner: Nitah Onsongo
Tech/Msg Review: Cesar De La Torre, Simon Tao, Clarke Rahrig
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Event: Insider Dev Tour Berlin
Event Description: Microsoft is going on a world tour with the announcements of Build 2019. The Insider Dev Tour focuses on innovations related to Microsoft 365 from a developer's perspective.
Date: June 7th, 2019
Event link: https://www.microsoft.com/de-de/techwiese/news/best-of-build-insider-dev-tour-am-7-juni-in-berlin.aspx
Linkedin: http://linkedin.com/in/mia-chang/
Adopting the Right Architecture for IoT Implementation
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[第35回 Machine Learning 15minutes!] Microsoft AI Updates![[第35回 Machine Learning 15minutes!] Microsoft AI Updates](https://cdn.slidesharecdn.com/ss_thumbnails/20190427ml15minmicrosoftai-190427060706-thumbnail.jpg?width=560&fit=bounds)
[第35回 Machine Learning 15minutes!] Microsoft AI UpdatesNaoki (Neo) SATO 第35回 Machine Learning 15minutes! (2019/04/27)
https://machine-learning15minutes.connpass.com/event/124780/
[第35回 Machine Learning 15minutes!] Microsoft AI Updates
https://satonaoki.wordpress.com/2019/04/27/ml15min-microsoft-ai-2/
BRK2122 IOT - From the cloud to the edge
BRK2122 IOT - From the cloud to the edgeAxel Dittmann This document discusses innovations in cloud, IoT, edge computing, and AI. It highlights features of Azure like 42 regions, security programs, and partnerships. It outlines Azure IoT services for device connectivity, data analytics, and visualization. Diagrams show the Azure IoT reference architecture and how IoT Edge extends cloud intelligence to edge devices. The document also summarizes IoT device lifecycles, the device provisioning service, device management with device twins, and solutions like IoT Central and the connected factory.
Complete artificial intelligence workloads faster using Microsoft Azure virtu...
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- 6. (regression) (classification) (clustering) (anomaly detection) (recommendation)
- 29. [142, 233, 120, 23, 57, 190, 85, 39, 135]
- 30. [ 1, 1, 1, 0, 0, 0, 0, 0, 0] [142, 233, 120, 23, 57, 190, 85, 39, 135]
- 31. [5, 138, 19, 2, 227, 144, 35, 193, 168]
- 32. [5, 138, 19, 2, 227, 144, 35, 193, 168] [ 1, 1, 1, 0, 0, 0, -1, -1, -1]
- 33. [ 1, 1, 1, 0, 0, 0, -1, -1, -1] [142, 233, 120, 23, 57, 190, 85, 39, 135]
- 49. updated W current W learning rate derivative of cost function
- 50. updated W current W learning rate gradient of cost function
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- 62. AI Services in Azure Bringing the best of AI to Azure and the best of Azure to AI Pre-Built AI Azure Cognitive Services Conversational AI Azure Bot Services Custom AI Azure Machine Learning
- 63. Integrated with Azure Machine Learning Create new deep learning projects easily Scale Out with Azure Batch AI Generate C# code from TensorFlow & ONNX models Convert models to ONNX Monitor model training progress & GPU utilization Visualize your model processing with integrated open tools like TensorBoard Get started quickly with the Samples Gallery VS Tools for AI Productive AI developer tools to train models and infuse AI into your apps