Transfer Learning

Summary

Reusing a model already trained on a large dataset (like ImageNet) and fine-tuning it on your smaller, specific dataset — dramatically reduces the data and compute needed for strong results.

How to Learn This

1Fine-tune a pretrained image model (e.g. ResNet) on a small custom dataset.
2Learn the difference between feature extraction and full fine-tuning.
3Understand why transfer learning is the default approach for most real-world deep learning projects.

Recurrent Neural Networks (RNNs) & LSTMs

Regularization in Deep Learning (Dropout, Batch Norm)

More topics in Deep Learning Fundamentals

Neural Network Basics Activation Functions Backpropagation & Gradient Descent Building Networks With TensorFlow / Keras Building Networks With PyTorch Convolutional Neural Networks (CNNs)Recurrent Neural Networks (RNNs) & LSTMs Regularization in Deep Learning (Dropout, Batch Norm)GPUs & Training Basics

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