The architecture of DeepSeek's R1 model is based on a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), designed to handle complex datasets, particularly aimed at processing sequences in real-time. At its core, the R1 model uses a layered stack of CNNs to initially extract features from the input data. This is effective for capturing spatial hierarchies in data, making it suitable for tasks like image and pattern recognition.
Once the CNN has processed the input, the model transitions to the recurrent network, which is essential for analyzing sequential data. The use of RNNs allows the R1 model to consider previous inputs when making predictions, effectively capturing temporal dependencies. In particular, long short-term memory (LSTM) units may be employed within the RNN structure to mitigate common issues like gradient vanishing during backpropagation. This enables the model to maintain context over longer sequences, enhancing its performance on tasks such as time series forecasting or natural language processing.
Finally, the R1 model incorporates fully connected layers at the output stage, where it integrates the derived features from both the CNN and RNN components. This fusion helps in delivering a final output that is both context-aware and feature-rich. For example, in a task such as video classification, the CNN might extract spatial features from individual frames, while the RNN would analyze the sequence of frames to capture how actions evolve over time. Overall, this architecture balances the strengths of both feedforward and recurrent designs, making the model suitable for a wide range of applications.