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===== Approximate Optimization ===== | ===== Approximate Optimization ===== | ||
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Large-scale machine learning was first approached as an engineering problem. For instance, to leverage a | Large-scale machine learning was first approached as an engineering problem. For instance, to leverage a | ||
larger training set, we can use a parallel computer to run a known machine learning algorithm | larger training set, we can use a parallel computer to run a known machine learning algorithm | ||
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takes into account the effect of approximate | takes into account the effect of approximate | ||
optimization on learning algorithms. | optimization on learning algorithms. | ||
+ | |||
The analysis shows distinct tradeoffs for the | The analysis shows distinct tradeoffs for the | ||
case of small-scale and large-scale learning problems. | case of small-scale and large-scale learning problems. | ||
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complexity of the underlying optimization | complexity of the underlying optimization | ||
algorithms in non-trivial ways. | algorithms in non-trivial ways. | ||
- | |||
- | ===== Learning with Stochastic Gradient Descent ===== | ||
- | |||
For instance, [[: | For instance, [[: | ||
- | appear to be mediocre optimization algorithms | + | appear to be mediocre optimization algorithms and yet are shown to |
- | and yet are shown to perform extremely well on large-scale learning problems. | + | [[: |
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* NIPS 2007 tutorial " | * NIPS 2007 tutorial " | ||
+ | ===== Related ===== | ||
+ | |||
+ | * [[: | ||
===== Papers ===== | ===== Papers ===== | ||
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</ | </ | ||
- | ===== See also ===== | + | <box 99% orange> |
+ | Léon Bottou and Yann LeCun: | ||
- | * [[stochastic|Learning with Stochastic Gradient Descent]]. | + | [[: |
+ | </ | ||
+ | <box 99% orange> | ||
+ | Léon Bottou: | ||
+ | [[: | ||
+ | </ | ||
+ | |||
+ | <box 99% blue> | ||
+ | Léon Bottou: | ||
+ | |||
+ | [[: | ||
+ | </ | ||