[D] Efficient ways of choosing number of layers/neurons in a neural network

This page summarizes the projects mentioned and recommended in the original post on /r/statistics

Judoscale - Save 47% on cloud hosting with autoscaling that just works
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  1. hyperopt

    Distributed Asynchronous Hyperparameter Optimization in Python

    optuna, hyperopt, nni, plenty of less-known tools too.

  2. Judoscale

    Save 47% on cloud hosting with autoscaling that just works. Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.

    Judoscale logo
  3. nni

    Discontinued An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

    optuna, hyperopt, nni, plenty of less-known tools too.

  4. optuna

    A hyperparameter optimization framework

    optuna, hyperopt, nni, plenty of less-known tools too.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

Suggest a related project

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