Efficient way to tune a network by changing hyperparameters?

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#Machine Learning #fine-tuning #experiment-track #pretrained-models #Deep Learning

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  • finetuner

    :dart: Task-oriented embedding tuning for BERT, CLIP, etc.

    Off the top of my head you can either use Grid Search to test hyperparam combinations, Random Search to randomize hyperparams and Neural search uses ML to optimize hyperparameter tuning. You can use finetuners for this as well.

  • wandb

    🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

    Wandb is the best! https://wandb.ai/

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    Updating dependencies is time-consuming.. Solutions like Dependabot or Renovate update but don't merge dependencies. You need to do it manually while it could be fully automated! Add a Merge Queue to your workflow and stop caring about PR management & merging. Try Mergify for free.

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