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Train and evaluate TensorFlow models v1.0.0

Use this resource to build and train deep learning models with TensorFlow

New

  • 104 downloads
  • 106 likes
  • 193 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install tensorflow-deep-learning-rule

First time: connect the CLI with

npx --yes palatekit@latest login

Cursor follows these rules when it writes TensorFlow code: separation of data, model, training and evaluation, tf.data pipelines, callbacks, choice of metrics, and saving the best checkpoint.

Mostly picked by

Updated November 2, 2026

Card for “Train and evaluate TensorFlow models”

Details

Works in
Install guides
Generates with
Your agent's model
Licence
CC0-1.0

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What you get

  • The .mdc file (in English): project structure, training, evaluation and export
  • Guidelines on seeds, mixed precision and data validation

Good fit for

  • A classification or forecasting project starting with Keras
  • A notebook repository that must become reusable training code

Not for

    Tags

    • tensorflow
    • keras
    • deep learning
    • python

    About

    You say

    “Set up the training pipeline and add saving of the best model.”

    Card for “Train and evaluate TensorFlow models”

    Rule published by PatrickJS under the CC0-1.0 license, delivered in its original version.

    Show more

    How to use it

    1. Copy the .mdc file into your project's .cursor/rules/ folder
    2. Ask Cursor to build and train deep learning models with TensorFlow
    3. Adjust the rule to your conventions

    Content preview

    Files

    • LICENSE6.5 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • tensorflow-deep-learning-rule.mdc2.5 KB

    Origin and licence

    Listed unmodified

    Author
    PatrickJS
    Source
    github.com/PatrickJS/awesome-cursorrules/blob/b044f956f021b6e8877f16781bcfc466a6a120e9/rules/tensorflow-deep-learning.mdc
    Licence
    CC0-1.0

    Notice

    « tensorflow-deep-learning » (PatrickJS, CC0-1.0) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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