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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

Become familiar 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

Who 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.

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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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