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"item_title" : "AI Image Classification",
"item_author" : [" Quiet Line Press", "Blue J. Lion "],
"item_description" : "Part of the Practical Tech Guide Series, this book focuses on what actually matters: how to take a folder of images and turn it into a working model you understand and control. You don't need a machine learning background. If you can run a command and open a browser, you can follow this guide from start to finish. Inside, you'll learn: - What image classification is and how it works (in simple terms)- How to prepare and organise your own image dataset- How to train models locally on your own machine- How to choose the right architecture (PyTorch and TensorFlow)- How to read accuracy, loss, and training results with confidence- How to improve your model with better data and tuning- When to use local training vs cloud or browser-based tools Everything is built around a simple, open-source training tool with a browser-based interface - so you can focus on results, not boilerplate code. No deep math. No unnecessary theory. No black boxes. Whether you're experimenting with AI, building a small project, or working with sensitive data you don't want to upload to the cloud, this guide gives you a clear, practical path. If you want to train AI image models locally - and actually understand what you're doing - this book is for you.",
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AI Image Classification : Train Models Locally - A Hands-On Guide
by Quiet Line Press and Blue J. Lion
Overview
Part of the Practical Tech Guide Series, this book focuses on what actually matters: how to take a folder of images and turn it into a working model you understand and control.
You don't need a machine learning background. If you can run a command and open a browser, you can follow this guide from start to finish. Inside, you'll learn:- What image classification is and how it works (in simple terms)
- How to prepare and organise your own image dataset
- How to train models locally on your own machine
- How to choose the right architecture (PyTorch and TensorFlow)
- How to read accuracy, loss, and training results with confidence
- How to improve your model with better data and tuning
- When to use local training vs cloud or browser-based tools Everything is built around a simple, open-source training tool with a browser-based interface - so you can focus on results, not boilerplate code. No deep math. No unnecessary theory. No black boxes. Whether you're experimenting with AI, building a small project, or working with sensitive data you don't want to upload to the cloud, this guide gives you a clear, practical path. If you want to train AI image models locally - and actually understand what you're doing - this book is for you.
This item is Non-Returnable
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Details
- ISBN-13: 9798257878367
- ISBN-10: 9798257878367
- Publisher: Independently Published
- Publish Date: April 2026
- Dimensions: 9 x 6 x 0.11 inches
- Shipping Weight: 0.18 pounds
- Page Count: 52
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