Search results
People also ask
How do CNNs work?
What are the steps involved in a convolutional neural network (CNN)?
How do deep layers of a CNN work?
What is the difference between a CNN and a regular neural network?
Nov 14, 2023 · A Convolutional Neural Network (CNN), also known as ConvNet, is a specialized type of deep learning algorithm mainly designed for tasks that necessitate object recognition, including image classification, detection, and segmentation.
- Motivation
- Convolutional Neural Network (CNN) Architecture Components
- Convolutional Blocks and Pooling Layers
- Fully Connected Classifier
- Summary of Key Points
In an earlier post on image classification, we used a densely connected Multilayer Perceptron (MLP) network to classify handwritten digits. However, one problem with using a fully connected MLP network for processing images is that image data is generally quite large, which leads to a substantial increase in the number of trainable parameters. This...
VGG-16 CNN Architecture
At a high level, CNN architectures contain an upstream feature extractor followed by a downstream classifier. The feature extraction segment is sometimes referred to as the “backbone” or “body” of the network. The classifier is sometimes referred to as the “head” of the network. In this section, we will introduce all the layer types that form the basis of both network components. To facilitate the discussion, we will refer to VGG-16 CNN architecture, as shown in the figure below. The model be...
The figure below is another way to depict the layers in a network visually. In the case of VGG-16, there are five convolutional blocks(Conv-1 to Conv-5). The specific layers within a convolutional block can vary depending on the architecture. However, a convolutional block typically contains one or more 2D convolutional layers followed by a pooling...
The fully connected (dense) layers in a CNN architecture transform features into class probabilities. In the case of VGG-16, the output from the last convolutional block (Conv-5) is a series of activation maps with shape (7x7x512). For reference, we have indicated the number of channels at key points in the architecture. Before the data from the la...
We covered a lot of material in this notebook, so let’s summarize the key points. 1. CNNs designed for a classification task contain an upstream feature extractor and a downstream classifier. 2. The feature extractor comprises convolutional blocks with a similar structure composed of one or more convolutional layers followed by a max pooling layer....
Mar 17, 2019 · The tutorial is designed in a way that gets you started with deep learning skills from the beginning to the end―from perceptron to deep learning. In this tutorial, we’ll touch base on the aspects of neural networks, models, and algorithms, some use cases, libraries to be used, and of course, the scope of deep learning.
Sep 9, 2024 · 2: How do CNNs work? CNNs work by applying a series of convolution and pooling layers to an input image or video. Convolution layers extract features from the input by sliding a small filter, or kernel, over the image or video and computing the dot product between the filter and the input.
Aug 26, 2020 · A Convolutional Neural Network, also known as CNN or ConvNet, is a class of neural networks that specializes in processing data that has a grid-like topology, such as an image.
Feb 9, 2024 · Introduction. If you had to pick one deep learning technique for computer vision from the plethora of options out there, which one would you go for? For a lot of folks, including myself, convolutional neural network is the default answer. But what is a convolutional neural network and why has it suddenly become so popular?
Feb 4, 2021 · CNNs work by applying filters to your input data. What makes them so special is that CNNs are able to tune the filters as training happens. That way the results are fine-tuned in real time, even when you have huge data sets, like with images.
