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Active Machine Learning with Python

You're reading from   Active Machine Learning with Python Refine and elevate data quality over quantity with active learning

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781835464946
Length 176 pages
Edition 1st Edition
Languages
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Author (1):
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Margaux Masson-Forsythe Margaux Masson-Forsythe
Author Profile Icon Margaux Masson-Forsythe
Margaux Masson-Forsythe
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Toc

Table of Contents (13) Chapters Close

Preface 1. Part 1: Fundamentals of Active Machine Learning
2. Chapter 1: Introducing Active Machine Learning FREE CHAPTER 3. Chapter 2: Designing Query Strategy Frameworks 4. Chapter 3: Managing the Human in the Loop 5. Part 2: Active Machine Learning in Practice
6. Chapter 4: Applying Active Learning to Computer Vision 7. Chapter 5: Leveraging Active Learning for Big Data 8. Part 3: Applying Active Machine Learning to Real-World Projects
9. Chapter 6: Evaluating and Enhancing Efficiency 10. Chapter 7: Utilizing Tools and Packages for Active ML 11. Index 12. Other Books You May Enjoy

Technical requirements

In this chapter, you will need to install the following packages:

pip install ultralytics lightly docker encord

You will also need the following imports:

import os
from IPython.display import display, Markdown
from ultralytics import YOLO
from pathlib import Path
import json
import contextlib
from typing import Iterator
import docker
from docker.models.containers import Container
from lightly.api import ApiWorkflowClient
from lightly.openapi_generated.swagger_client import DatasetType
from lightly.openapi_generated.swagger_client import DatasourcePurpose
from encord.orm.cloud_integration import CloudIntegration
from encord.orm.dataset import AddPrivateDataResponse
from encord.user_client import EncordUserClient
from encord.orm.dataset import CreateDatasetResponse, StorageLocation

Next, you need to create a Lightly account and set up your API token, as follows:

lightly_token = "your_lightly_token"

Then, you must set up the Lightly client...

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