# Hello Deep Learning: Convolutional networks

DevFeed: [Hello Deep Learning: Convolutional networks](<https://devfeed.tech/articles/hello-deep-learning-convolutional-networks-36361.md>)

Original publisher: [Read original article](<https://berthub.eu/articles/posts/dl-convolutional/>)

Published: 2023-03-30T10:00:05Z

Content type: tutorial

Language: en

Sources: [Bert Hubert's writings](<https://devfeed.tech/sources/bert-hubert-s-writings.md>)

Topics: [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [machine learning overfitting](<https://devfeed.tech/topics/machine-learning-overfitting.md>), [generalization in machine learning](<https://devfeed.tech/topics/generalization-in-machine-learning.md>)

Tags: [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>), [validation](<https://devfeed.tech/tags/validation.md>)

## AI overview

This tutorial chapter introduces convolutional neural networks as a way to recognize shapes and use the relative positions of pixels. It explains how convolutional operations, max-pooling, and GELU can improve robustness beyond a basic image-processing network, with examples involving handwritten digits and letters.

## Source excerpt

This page is part of the Hello Deep Learning series of blog posts. You are very welcome to improve this page via GitHub! In the previous chapter we taught a network of linear combinations and 'rectified linear units' to recognize handwritten digits reasonably successfully. But we already noted that the network would be sensitive to the exact location of pixels, and that it does not in any meaningful way "know" what a 7 looks like.