Recurrent Neural Networks (RNNs) & LSTMs

Summary

Architectures designed for sequential data (text, time series) that maintain a 'memory' of previous inputs — LSTMs specifically solve RNNs' tendency to forget long-range dependencies.

How to Learn This

1Read a conceptual explanation of why sequence order matters for these architectures.
2Build a simple LSTM for a basic sequence prediction task.
3Learn why Transformers (Phase 10) have largely replaced RNNs/LSTMs for most NLP tasks today.

Convolutional Neural Networks (CNNs)

Transfer Learning

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)Transfer Learning Regularization in Deep Learning (Dropout, Batch Norm)GPUs & Training Basics

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