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Math and Architectures of Deep Learning
book

Math and Architectures of Deep Learning

by Krishnendu Chaudhury
May 2024
Intermediate to advanced content levelIntermediate to advanced
552 pages
18h 3m
English
Manning Publications
Content preview from Math and Architectures of Deep Learning

3 Classifiers and vector calculus

We took a first look at the core concept of machine learning in section 1.3. Then, in section 2.8.2, we examined classifiers as a special case. But so far, we have skipped the topic of error minimization: given one or more training examples, how do we adjust the weights and biases to make the machine closer to the desired ideal? We will study this topic in this chapter by discussing the concept of gradients.

NOTE The complete PyTorch code for this chapter is available at http://mng.bz /4Zya in the form of fully functional and executable Jupyter notebooks.

3.1 Geometrical view of image classification

To fix our ideas, consider a machine that classifies whether an image contains a car or a giraffe. Such classifiers, ...

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