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Code First Machine Learning (An Experiment-First Guide for Programmers)
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$59.99
| Expected release date is Jul 13th 2027 |
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Product Details
Author:
Ronald T. Kneusel
Format:
Paperback
Pages:
304
Publisher:
No Starch Press (July 13, 2027)
Imprint:
No Starch Press
Release Date:
July 13, 2027
Language:
English
Audience:
General/trade
ISBN-13:
9781718505087
ISBN-10:
1718505086
Weight:
13oz
Dimensions:
7" x 9.25"
File:
RandomHouse-PRH_Book_Company_PRH_PRT_Onix_delta_active_D20261106T230942_158485375-20261106.xml
Folder:
RandomHouse
List Price:
$59.99
Country of Origin:
United States
Pub Discount:
65
Case Pack:
24
As low as:
$46.19
Publisher Identifier:
P-RH
Discount Code:
A
QuickShip:
Yes
Overview
Learn to read, modify, and evaluate real machine-learning code by examining the lines that matter, running the programs, and understanding why models succeed or fail.
Read the code. Run the model. Know why it works.
AI can write machine-learning code in seconds. But understanding why it works (or why it fails) is another matter.
Code First Machine Learning teaches you to read the code that makes a model behave the way it does. Instead of burying the crucial logic in downloadable scripts, Ronald T. Kneusel brings the important lines onto the page and shows you how to interpret them. You'll see the code, understand the choices it makes, run it, and discover what the results really mean.
Start with neural networks and handwritten-digit classification. Then investigate how data preparation, class balance, and test splits change the conclusions you can draw from a model. Build on that foundation with convolutional networks, transfer learning, object detection, zero-shot and few-shot learning, and techniques behind modern language models.
Each example gives you a practical way to ask better questions: Which lines matter? What assumptions are hiding here? Why did the result change? What would happen if you modified the code?
Anyone, including an AI assistant, can hand you a script. This book teaches you to read it critically, modify it deliberately, and decide whether its output deserves your trust.
YOU’LL LEARN TO:
Covers: Python-based ML and deep learning, datasets, model evaluation, neural networks, CNNs, transfer learning, object detection, CLIP, zero-shot/few-shot learning, and language-model concepts.
Python familiarity recommended.
Read the code. Run the model. Know why it works.
AI can write machine-learning code in seconds. But understanding why it works (or why it fails) is another matter.
Code First Machine Learning teaches you to read the code that makes a model behave the way it does. Instead of burying the crucial logic in downloadable scripts, Ronald T. Kneusel brings the important lines onto the page and shows you how to interpret them. You'll see the code, understand the choices it makes, run it, and discover what the results really mean.
Start with neural networks and handwritten-digit classification. Then investigate how data preparation, class balance, and test splits change the conclusions you can draw from a model. Build on that foundation with convolutional networks, transfer learning, object detection, zero-shot and few-shot learning, and techniques behind modern language models.
Each example gives you a practical way to ask better questions: Which lines matter? What assumptions are hiding here? Why did the result change? What would happen if you modified the code?
Anyone, including an AI assistant, can hand you a script. This book teaches you to read it critically, modify it deliberately, and decide whether its output deserves your trust.
YOU’LL LEARN TO:
- Read the essential code in a machine-learning example and explain the choices it makes
- Run and modify models to test how inputs and design decisions affect results
- Recognize misleading accuracy measures and problems with training and test data
- Explore CNNs, transfer learning, object detection, and zero-shot and few-shot approaches
- Interpret what a model’s output proves, and what it doesn’t
Covers: Python-based ML and deep learning, datasets, model evaluation, neural networks, CNNs, transfer learning, object detection, CLIP, zero-shot/few-shot learning, and language-model concepts.
Python familiarity recommended.
