7 lines
1.5 KiB
JSON
7 lines
1.5 KiB
JSON
{"messages": [{"role": "user", "content": "Explain what a neural network is."}, {"role": "assistant", "content": "A neural network is a computational model loosely inspired by biological neurons, consisting of layers of interconnected nodes that learn to map inputs to outputs through training."}], "reward": 0.9}
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{"messages": [{"role": "user", "content": "Explain what a neural network is."}, {"role": "assistant", "content": "It's a thing that does AI stuff with nodes and layers."}], "reward": 0.2}
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{"messages": [{"role": "user", "content": "What is backpropagation?"}, {"role": "assistant", "content": "Backpropagation computes gradients of the loss with respect to each parameter by applying the chain rule backwards through the computation graph, enabling gradient descent to update all weights efficiently."}], "reward": 1.0}
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{"messages": [{"role": "user", "content": "What is backpropagation?"}, {"role": "assistant", "content": "Backpropagation is how neural networks learn by going backwards."}], "reward": 0.3}
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{"prompt": "Describe overfitting in machine learning.", "response": "Overfitting occurs when a model learns the training data too closely—capturing noise and idiosyncrasies—resulting in poor generalisation to unseen examples. Regularisation, dropout, and early stopping are common mitigations.", "reward": 0.95}
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{"prompt": "Describe overfitting in machine learning.", "response": "Overfitting is when the model is too good at training data but bad at new data.", "reward": 0.4}
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