Optimizing Network Performance with Hyperparameters and Temporal Features
Explore the intricate world of hyperparameter optimization, combining temporal and non-temporal features in network construction. Dive into learning rates, optimizers, network architecture, activation functions, and more for building your first efficient network. Get ready to train a network and submit your work in this exciting homework assignment.
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Presentation Transcript
Outilne Temporal feature construction Combining temporal and non temporal features in networks Hyperparameters in networks Hyperparameter optimization Homework: build and submit your first network
Hyperparameters Learning rate Constant, exponential, step decay Optimizers Momentum, Adam, Adagrad Mini batch size Early stopping Network architecture Number of hidden units, structure, residual networks Regularization: L2, L1
Hyperparameters Activation functions (Relu, Tanh) Random seeds Model averaging Data preprocessing Element wise standardization, PCA, uniformization, log, square root
Hyperparameter optimization Grid search Random search Further reading: https://arxiv.org/pdf/1206.5533v2.pdf
Announcements Next class 30.4.16 Home work : train a network and submit at least once Automatic tests Instructions Example in Predictor.py Problems tracking update this file
