Sequence Models
Work with RNNs, LSTMs, and the attention mechanism.
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After this skill you can…
- Implement an LSTM text generator
- Explain vanishing gradients in RNNs
- Describe the attention mechanism intuitively
Prerequisites
Watch (10 videos)
Back Propagation Through Time (BPTT) in RNN | Vanishing Gradient | Exploding Gradient | Explained
→ Build RNN models→ Use BPTT for training RNNs→ Analyze gradient descent issues
RNNs & LSTMs — How AI Remembers Sequences | Visual ML
→ Model sequences with RNNs→ Use LSTMs for long-range dependencies
Applied Deep Learning 2025 - Lecture 4 - Recurrent Neural Networks
→ Model sequential data→ Use RNNs for text processing
Neural Network Part 2: The Corporate Deep-Dive
→ Model sequential data→ Analyze language patterns→ Generate human-like text
Pytorch Seq2Seq Tutorial for Machine Translation
→ Build a Seq2Seq model→ Implement machine translation using PyTorch→ Apply Seq2Seq models to NLP tasks
Build a Neural Network (LIVE)
→ Implement an LSTM Model→ Use Keras for Deep Learning
18: Recurrent Networks - Intro to Neural Computation
→ Implement Sequence Models→ Analyze Neural Computation→ Design Recurrent Networks
Recurrent Neural Networks - EXPLAINED!
→ Build RNNs→ Implement Sequence Modeling→ Analyze Time Series Data
Pytorch Text Generator with character level LSTM
→ Use LSTM for sequence modeling→ Generate text with a character-level model
LSTM Networks - EXPLAINED!
→ Build sequence models→ Implement LSTM networks→ Generate text using Keras
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