In this article, I will explain CNNs for Image Recognition.
Convolutional Neural Networks (CNNs) are a popular type of neural network for image recognition tasks. They are effective at automatically learning and extracting relevant features from raw image data without the need for manual feature engineering.
In a typical CNN architecture for image recognition, the input image is passed through a series of convolutional layers, each of which applies a set of learnable filters to the image. These filters learn to identify features such as edges, corners, and textures. The output of each convolutional layer is then passed through a non-linear activation function, such as ReLU, to introduce non-linearity into the network.
The output of the final convolutional layer is then passed through one or more fully connected layers, which use the learned features to produce a final output. During training, the network adjusts its weights and biases to minimize a specified loss function, allowing it to make accurate predictions or classifications on new images.
To improve the performance of the network, various techniques such as data augmentation, dropout, and batch normalization can be used. Data augmentation involves randomly transforming the input images to increase the amount of training data and improve the robustness of the network. Dropout randomly drops out nodes from the network during training to prevent overfitting, while batch normalization normalizes the inputs to each layer to improve the stability and performance of the network.
Further Reading
Deep Learning Practice Exercise
How to Use CNNs in Computer Vision?
Deep Learning Methods for Object Detection
Popular Machine Learning Algorithms for Prediction
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