Top 10 CNN Architectures Every Machine Learning Engineer Should Know (2024)

Data Science

A convolutional neural network (CNN or ConvNet) is a specific kind of deep learning architecture. At the moment, there are many tech companies have developed active research groups for exploring new architectures of CNN such as Google, Microsoft, and Facebook and they demonstrated that CNNs are one of the best learning algorithms for understanding and analyzing image content that has shown high performance in image segmentation, classification, detection, and retrieval related tasks.

CNNs were designed for image recognition tasks were originally applied to the challenge of handwritten digit recognition¹ ². The basic design goal of CNNs was to create a network where the neurons in the early layer of the network would extract local visual features, and neurons in later layers would combine these features to form higher-order features.

There are three main types of layers that you will see in almost every CNNs which are convolutional layer, pooling layer, and fully connected layer.

Over the years, there are many variants of CNN architectures have been developed to solve real-world problems. LeNet is the first successful application of CNNs and was developed by Yann Lecun in the 1990s that was used to read zip codes, digits, etc. The latest work is called LeNet-5 which a 5-layer CNN that reaches 99.2 % accuracy on insolated character recognition.

In this article, we will discuss the top 10 CNN architectures every machine learning engineer should know that have provided that boost to the field of deep learning over the world.

AlexNet

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton won the ImageNet Large Scale Visual…