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Pretrained models
Models that have already been trained on a large dataset for a general task, improving accuracy and speed for new datasets.
Vision Transformers
A type of deep learning model that uses self-attention mechanisms to process images, capturing long-range dependencies and global context.
Fine-tuning
A transfer learning technique where parameters of a pretrained model are updated by training for additional epochs on a different task.
Fit one cycle
A training schedule that gradually increases and then decreases the learning rate during training.
Half-precision (to_fp16)
A technique using 16-bit floating-point numbers to speed up training and reduce memory usage, while possibly sacrificing precision.
Fastkaggle
A Python library that simplifies working with Kaggle competitions by automating tasks like data downloading and package installation.
CNN (Convolutional Neural Network)
A specialized type of neural network designed for processing grid-like data, particularly effective for image recognition.
Pooling layers
Layers that downsample feature maps produced by convolutional layers, reducing spatial dimensions and making the network more robust.
Learning Rate Finder
A technique used to determine an optimal learning rate for a specific model and dataset during training.
SGD (Stochastic Gradient Descent)
An iterative optimization algorithm that updates neural network weights by minimizing a cost function using a mini-batch of data.