⇦ Back to Convolutional Neural Networks (CNNs)

Deep Learning is a subfield of Machine Learning that focuses on training artificial neural networks to perform complex tasks. It has revolutionized the field of computer vision, natural language processing, and speech recognition. In this lesson, we will focus on the importance of preprocessing and data augmentation in Convolutional Neural Networks (CNNs).

Preprocessing

Preprocessing is a crucial step in preparing data for training a CNN. It involves transforming the raw input data into a format that is suitable for the neural network. One common preprocessing technique is normalization, which involves scaling the pixel values to a range of 0 to 1. This helps to reduce the impact of lighting and contrast variations in the input images.

Another preprocessing technique is resizing, which involves resizing the input images to a fixed size. This is important because CNNs require input images to be of the same size. Resizing also helps to reduce the computational cost of training the neural network.

Cropping is another preprocessing technique that involves removing the edges of the input images. This is useful when the object of interest is located in the center of the image. Cropping helps to reduce the amount of background noise in the input images.

Data Augmentation

Data augmentation is a technique used to increase the size of the training dataset by generating new images from the existing ones. This helps to prevent overfitting and improve the generalization performance of the neural network. One common data augmentation technique is flipping, which involves flipping the input images horizontally or vertically.

Another data augmentation technique is rotation, which involves rotating the input images by a certain angle. This helps to increase the robustness of the neural network to variations in the orientation of the input images.

Other data augmentation techniques include zooming, shearing, and adding noise to the input images. These techniques help to increase the diversity of the training dataset and improve the performance of the neural network.

Conclusion

Preprocessing and data augmentation are important techniques in training CNNs. Preprocessing helps to prepare the input data for the neural network, while data augmentation helps to increase the size and diversity of the training dataset. By using these techniques, we can improve the performance of the neural network and achieve better results in computer vision tasks.


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⇦ 4.2 Understanding Convolutional Neural Networks (CNNs) 4.4 Training and Optimization ⇨