0

Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Democratizing Artificial Intelligence with UiPath

You're reading from Democratizing Artificial Intelligence with UiPath Expand automation in your organization to achieve operational efficiency and high performance

Product type Paperback

Published in Apr 2022

Publisher Packt

ISBN-13 9781801817653

Length 376 pages

Edition 1st Edition

Tools

UiPath

Concepts

Artificial Intelligence

Authors (2):

Fanny Ip

Jeremiah Crowley

View More author details

Table of Contents (16) Chapters

Preface

1. Section 1: The Basics

2. Chapter 1: Understanding Essential Artificial Intelligence Basics for RPA Developers FREE CHAPTER

3. Chapter 2: Bridging the Gap between RPA and Cognitive Automation

4. Chapter 3: Understanding the UiPath Platform in the Cognitive Automation Life Cycle

5. Section 2: The Development Life Cycle with AI Center and Document Understanding

6. Chapter 4: Identifying Cognitive Opportunities

7. Chapter 5: Designing Automation with End User Considerations

8. Chapter 6: Understanding Your Tools

9. Chapter 7: Testing and Refining Development Efforts

10. Section 3: Building with UiPath Document Understanding, AI Center, and Druid

11. Chapter 8: Use Case 1 – Receipt Processing with Document Understanding

12. Chapter 9: Use Case 2 – Email Classification with AI Center

13. Chapter 10: Use Case 3 – Chatbots with Druid

14. Chapter 11: AI Center Advanced Topics

15. Other Books You May Enjoy

NER with AI Center

Many use cases that leverage ML models use some sort of classification. NER is the task of identifying and categorizing key information within texts. Example use cases include extracting and classifying text from emails, letters, web pages, or even transcripts. In this section, we'll discuss AI Center's NER models and discuss how to use the trainable NER model.

Introducing AI Center's NER models

NER is a form of natural language processing (NLP) that is tasked to process and analyze natural text by detecting and categorizing entities within language. Examples of common entity categories include the following:

Person: Elvis Presley, Joe Biden
Company: Google, Apple
Time: 2010, Winter, 3 a.m.
Location: New York City, Machu Picchu

Having technology that can recognize these common entities can be extremely powerful when used with automation. UiPath's NER ML model can be used in many ways, as shown in Figure 11.5...

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at $19.99/month. Cancel anytime

Authors (2)

Ip

Ip

Fanny Ip is a thought leader in Automation, Business Transformation, and Innovation. Currently, Fanny is the VP of the Automation Consulting Innovation Office at UiPath and leads a team of experienced management consultants to develop field-tested solutions for customers - from start-up, expand, to scale. Before joining UiPath, Fanny co-led McKinsey's Automation practice in North America and developed automation strategies for CxOs to deliver top-line revenue growth and achieve bottom-line impact. Before automation, Fanny has also led many business transformation projects at PwC Advisory and Deloitte Consulting. Fanny earned a BA in Economics from the University of Chicago and an MBA from the Anderson School of Management at UCLA. Originally from Hong Kong, Fanny resides in Los Angeles with her family.

See other products by Ip

Crowley

Crowley

Jeremiah Crowley is a multi-disciplined developer with a wide technical and operational lens. His initial passion for software development and implementation led him to automation and process efficiency. He enjoys working on and solving problems irrespective of size and technology. Jeremiah received his BA in Computer Science from New York University. His work history includes the likes of EY and McKinsey & Company, where he played roles within their Automation and Service Operations practices. He is currently a Director within UiPath's Automation Consulting Innovation Office, assisting organizations drive adoption and scale of automation by managing digital transformation initiatives and developing hyper-automation frameworks and methodologies.

See other products by Crowley

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m