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Image segmentation is the task of partitioning an image based on the objects present. Lets understand image segmentation for data science
AlexNet architecture: 8 layers, 62.3M parameters, ReLU, dropout, convolution, and deep learning advancements for image recognition.
See the implementation of Model checkpointing and you're required to have a little bit of prior knowledge about creating models using Keras
Lenet-5 is one of the earliest pre-trained models proposed by Yann LeCun and others in the year 1998. Learn about Lenet 5 architecture here.
Generative Adversarial Networks is a subclass of machine learning frameworks that was designed by Ian Goodfellow in 2014.
Learn about pixel values and find out how images are stored on a computer in the two most common image storage formats - Grayscale and RGB.
Let's talk about the process for license plate recognition using Convolutional Neural Network in this article and get an intuition
Learn about edge image processing and how it helps identify boundaries in images using techniques like Canny and Sobel.
Image augmentation techniques help in altering the existing image data to create some more data for the model training process
Vision Transformers is one of the prominent tranformers for Image Recognition at Scale. Let'd deep dive into Vision transformers
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