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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
Batch normalization is the process to make neural networks faster and more stable through adding extra layers in a deep neural network.
Forward propagation is the first step of training a neural network. In this article, learn about the errors and forward propagation
Neural Network is one of the fundamental concepts of Data Science Universe. In this article, we introduce you to Neural Network.
Image similarity is topic not talked about in the field of computer vision. In this article, let us understand Image similarity in Python
Learn how Binary Cross Entropy (Log Loss) functions in binary classification tasks in machine learning. Understand its formula, application, and role in optimizing classification models
Perceptron is one of the most fundamental concepts of deep learning. It is a supervised learning algorithm specifically for binary classifiers
Explore the components of a neural network and learn about neural network layers and neurons, including input, hidden, and output layers.
In this article we are going to look into Incremental and Reinforced learning for Image classification with python implementation
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