Beyond Automatic Differentiation

• arb

  11 457 2.2 C

  Arb has been merged into FLINT -- use https://github.com/flintlib/flint/ instead

  

• tuning_playbook

  16 24,742 5.1

  A playbook for systematically maximizing the performance of deep learning models.

  

Batch size can be used for regularisation, but using it for that will limit training performance. From the Google Research Tuning Playbook:

> The batch size governs the training speed and shouldn't be used to directly tune the validation set performance. Often, the ideal batch size will be the largest batch size supported by the available hardware.

> […]

> As long as all hyperparameters are well-tuned (especially the learning rate and regularization hyperparameters) and the number of training steps is sufficient, the same final performance should be attainable using any batch size (see Shallue et al. 2018).

https://github.com/google-research/tuning_playbook#choosing-...

The ideal case is full-batch with tuneable regularisation, just the hardware gets expensive.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

Suggest a related project