There are two major approaches here that I am aware of, both of which are related in a kind of abstract way, but which are in practice different
- Finding the right architecture for your nueral net, a.k.a architecture search
- Hyperparameter optimisation which I have made into a separate notebook.
The first one I might cover here.
TODO: work out if this is the same as “meta learning”? I think not; I suspect that of being transfer learning.
Reinforcement learning approaches
Quoc Le & Barret Zoph discuss using reinforcement learning to learn neural models:
Typically, our machine learning models are painstakingly designed by a team of engineers and scientists. This process of manually designing machine learning models is difficult because the search space of all possible models can be combinatorially large — a typical 10-layer network can have ~1010 candidate networks! […]
To make this process of designing machine learning models much more accessible, we’ve been exploring ways to automate the design of machine learning models. […] in this blog post, we’ll focus on our reinforcement learning approach and the early results we’ve gotten so far.
In our approach (which we call “AutoML”), a controller neural net can propose a “child” model architecture, which can then be trained and evaluated for quality on a particular task. That feedback is then used to inform the controller how to improve its proposals for the next round.
Differentiable architecture search
auto-sklearnis an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator:
import autosklearn.classification cls = autosklearn.classification.AutoSklearnClassifier() cls.fit(X_train, y_train) predictions = cls.predict(X_test)
auto-sklearnfrees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction.
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