Lilly Tech Systems
RNN & LSTM Architectures
Learn recurrent neural networks, LSTM, GRU, and sequence modeling architectures for temporal data.
Course Lessons
Follow these lessons in order for a complete understanding of rnn & lstm architectures.
1. RNN Fundamentals
Learn about rnn fundamentals in the context of rnn & lstm architectures.
2. Vanishing Gradient Problem
Learn about vanishing gradient problem in the context of rnn & lstm architectures.
3. LSTM Architecture
Learn about lstm architecture in the context of rnn & lstm architectures.
4. GRU Architecture
Learn about gru architecture in the context of rnn & lstm architectures.
5. Bidirectional RNNs
Learn about bidirectional rnns in the context of rnn & lstm architectures.
6. Sequence-to-Sequence Models
Learn about sequence-to-sequence models in the context of rnn & lstm architectures.
7. RNN vs Transformer Comparison
Learn about rnn vs transformer comparison in the context of rnn & lstm architectures.
What You'll Learn
By the end of this course, you will be able to:
Understand Core Concepts
Gain deep understanding of the principles and patterns that define rnn & lstm architectures.
Apply in Practice
Implement real-world solutions using the architectural patterns and code examples from each lesson.
Make Architecture Decisions
Evaluate trade-offs and choose the right approaches for your specific requirements and constraints.
Build Production Systems
Design and implement production-ready AI systems that are reliable, scalable, and maintainable.