The lesson I learnt is to explore and be updated so that we can apply suitable models for suitable situations. Imagine yourself into that problem, so that it will be easier to make assumptions, analogies, situations etc. Considering Heathcare Industry finding a suitable model is not enough it is also important to focus on efficiency like train time, performance and interpretability, because it is the question of ones life.
with 2M parameters |
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with 2M parameters |
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with 7M parameters |
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with 7M parameters |
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with AveragePooling,Dropouts,softmax 10M parameters |
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with AveragePooling,Dropouts,softmax 10M parameters |
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with 7M parameters |
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with AveragePooling,Dropouts,softmax 18M parameters |
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with BatchNormalization,Dropouts,softmax 18M parameters |
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with GlobalAveragePooling,Dropouts,softmax 18M parameters |
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with 54M parameters |
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with AveragePooling,Dropouts,softmax 54M parameters |
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with AveragePooling,Dropouts,softmax 54M parameters |
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with AveragePooling,Dropouts,softmax 54M parameters |
The lesson I learnt is to explore and be updated so that we can apply suitable models for suitable situations. Imagine yourself into that problem, so that it will be easier to make assumptions, analogies, situations etc. Considering Heathcare Industry finding a suitable model is not enough it is also important to focus on efficiency like train time, performance and interpretability, because it is the question of ones life.
Training Description | Best Parameters | RMSLE |
---|---|---|
Feature Set 1 | – | 0.75897 |
Feature Set 2 | – | 0.68443 |
Feature Set 3 | – | 0.60311 |
Feature Set 3 95% data |
– | 0.43815 |
The lesson I learnt is take more time to look into raw data and imagine yourself into that problem, so that it will be easier to make assumptions, analogies, situations etc.