Transfer Learning

Summary

Reusing a model pretrained on a large dataset and adapting it to your specific task — how nearly all practical deep learning is done today rather than training from scratch.

How to Learn This

1Fine-tune a pretrained image or text model on a small custom dataset.
2Learn the difference between feature extraction (frozen backbone) and full fine-tuning.
3Understand why transfer learning drastically reduces the data and compute you need.

Positional Encoding

Autoencoders

More topics in Deep Learning Architectures

Convolutional Neural Networks (CNNs)Pooling Layers Recurrent Neural Networks (RNNs)LSTMs & GRUs Sequence-to-Sequence Models Attention Mechanism Transformer Architecture Positional Encoding Autoencoders Generative Adversarial Networks (GANs, Overview)

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