Mathew Salvaris, Danielle Dean, Wee Hyong Tok

Deep Learning with Azure (eBook, PDF)

Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform

• Format: PDF

Produktbeschreibung

Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer.

Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI?

Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI.

What You'll Learn:

• Becomefamiliar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI
• Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more)
• Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving
• Discover the options for training and operationalizing deep learning models on Azure

This book is for professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.

Mathew Salvaris, PhD is a senior data scientist at Microsoft in the Cloud and AI division, where he works with a team of data scientists and engineers building machine learning and AI solutions for external companies utilizing Microsoft's Cloud AI platform.

Danielle Dean, PhD is a principal data science lead at Microsoft in the Cloud and AI division, where she leads a team of data scientists and engineers building artificial intelligence solutions with external companies utilizing Microsoft's Cloud AI platform.

Wee Hyong Tok, PhD is a principal data science manager at Microsoft in the Cloud and AI division. He leads the AI for Earth Engineering and Data Science team, where his team of data scientists and engineers are working to advance the boundaries of state-of-the-art deep learning algorithms and systems.

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Produktdetails

• Verlag: Apress
• Seitenzahl: 284
• Erscheinungstermin: 24. August 2018
• Englisch
• ISBN-13: 9781484236796
• Artikelnr.: 54269205

Autorenporträt

Mathew Salvaris, PhD is a senior data scientist at Microsoft in the Cloud and AI division, where he works with a team of data scientists and engineers building machine learning and AI solutions for external companies utilizing Microsoft's Cloud AI platform. He enlists the latest innovations in machine learning and deep learning to deliver novel solutions for real-world business problems, and to leverage learning from these engagements to help improve Microsoft's Cloud AI products. Prior to joining Microsoft, he worked as a data scientist for a fintech startup where he specialized in providing machine learning solutions. Previously, he held a postdoctoral research position at University College London in the Institute of Cognitive Neuroscience, where he used machine learning methods and electroencephalography to investigate volition. Prior to that position, he worked as a postdoctoral researcher in brain computer interfaces at the University of Essex. Mathew holdsa PhD and MSc in computer science.

Danielle Dean, PhD is a principal data science lead at Microsoft in the Cloud and AI division, where she leads a team of data scientists and engineers building artificial intelligence solutions with external companies utilizing Microsoft's Cloud AI platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. She has a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multi-level event history models to understand the timing and processes leading to events between dyads within social networks.

Wee Hyong Tok, PhD is a principal data science manager at Microsoft in the Cloud and AI division. He leads the AI for Earth Engineering and Data Science team, where his team of data scientists and engineers are working to advance the boundaries of state-of-art deep learning algorithms and systems. His team works extensively with deep learning frameworks, ranging from TensorFlow to CNTK, Keras, and PyTorch. He has worn many hats in his career as developer, program/product manager, data scientist, researcher, and strategist. Throughout his career, he has been a trusted advisor to the C-suite, from Fortune 500 companies to startups. He co-authored one of the first books on Azure machine learning, Predictive Analytics Using Azure Machine Learning, and authored another demonstrating how database professionals can do AI with databases, Doing Data Science with SQL Server. He has a PhD in computer science from the National University of Singapore, where he studied progressive join algorithms for data streaming systems.

Inhaltsangabe

Part 1 - Getting Started with AI.- Chapter 1: Introduction to Artificial Intelligence.- Chapter 2: Overview of Deep Learning.- Chapter 3: Trends in Deep Learning.- Part 2: Azure AI Platform and Experimentation Tools.- Chapter 4: Microsoft AI Platform.- Chapter 5: Cognitive Services and Custom Vision.- Part 3: AI Networks in Practice.- Chapter 6: Convolutional Neural Networks.- Chapter 7: Recurrent Neural Networks.- Chapter 8: Generative Adversarial Networks (GANs).- Part 4: AI Architectures and Best Practices.- Chapter 9: Training AI Models.- Chapter 10: Operationalizing AI Models.- Appendix: Notes.

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