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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