Hypertuning for TensorFlow & PyTorch

Hyperparameter Tuning is like DJ’ing with Data — Photo Credit (unsplash.com/photos/ZfCVTJ30yoc)The fundamental problem with deep learning libraries is that they are designed for a single in-memory run whose sole aim is to minimize loss — when in reality — it takes many runs to tune an architecture, the workflow needs to be persisted, and the training loss is just the beginning of model evaluation.Red is quite popular despite less functionality for less layer types — Image …

artificial intelligence data analysis data science deep learning machine learning pytorch tensorflow