8 Deep Learning Architectures Data Scientists Must Master

1. Convolutional Neural Networks (CNNs): For image recognition tasks using spatial hierarchies.

2. Recurrent Neural Networks (RNNs): Ideal for sequential data, like time series or text, due to memory.

3. Long Short-Term Memory Networks (LSTMs): RNN variant managing long-term dependencies.

4. Generative Adversarial Networks (GANs): Create synthetic data through adversarial training.

5. Autoencoders: Unsupervised learning for data compression and feature extraction.

6. Transformer Networks: Efficient for natural language processing with self-attention mechanisms.

7. Capsule Networks: Handle spatial hierarchies more effectively than CNNs.

8. Deep Q-Networks (DQNs): Reinforcement learning using deep neural networks for decision-making.

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