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斯坦福大学机器学习斯坦福大学机器学习第十课“应用机器学习的建议(Advice for applying machine learning)”学习笔记，本次课程主要包括7部分：
- Deciding what to try next(决定下一步该如何做)
- Evaluating a hypothesis(评估假设)
- Model selection and training/validation/test sets(模型选择和训练/验证/测试集)
- Diagnosing bias vs. variance(诊断偏差和方差)
- Regularization and bias/variance(正则化和偏差/方差)
- Learning curves(学习曲线)
- Deciding what to try next (revisited)(再次决定下一步该做什么)
【30页ppt】Advice for applying Machine Learning
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We use data on 124 batteries released by Stanford University to first try to solve the binary classification problem of determining if a battery is "good" or "bad" given only the first 5 cycles of data (i.e., will it last longer than a certain threshold of cycles), as well as the prediction problem of determining the exact number of cycles a battery will last given the first 100 cycles of data. We approach the problem from a purely data-driven standpoint, hoping to use deep learning to learn the patterns in the sequences of data that the Stanford team engineered by hand. For both problems, we used a similar deep network design, that included an optional 1-D convolution, LSTMs, an optional Attention layer, followed by fully connected layers to produce our output. For the classification task, we were able to achieve very competitive results, with validation accuracies above 90%, and a test accuracy of 95%, compared to the 97.5% test accuracy of the current leading model. For the prediction task, we were also able to achieve competitive results, with a test MAPE error of 12.5% as compared with a 9.1% MAPE error achieved by the current leading model (Severson et al. 2019).
