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Deep Learning
Fundamentals
Learning with deep neural networks containing many parameters.
Deep Learning uses multiple layers of nonlinear transformations to capture complex patterns in text, images, audio, or multimodal data.
- Strengths: High performance with large datasets, automatic feature extraction.
- Weaknesses: Data- and compute-intensive, hard to interpret, potentially vulnerable.
- Common architectures: CNNs, RNNs/LSTMs, Transformers.
- Practice: Transfer learning and fine-tuning reduce cost and data requirements.