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Note de l’éditeur : This file was selected as MATLAB Central Pick of the Week
The code provides hands-on examples to implement convolutional neural networks (CNNs) for object recognition. The three demos have associated instructional videos that will allow for a complete tutorial experience to understand and implement deep learning techniques.
The demos include:
- Training a neural network from scratch
- Using a pre-trained model (transfer learning)
- Using a neural network as a feature extractor
The corresponding videos for the demos are located here: https://www.mathworks.com/videos/series/deep-learning-with-MATLAB.html
The use of a GPU and Parallel Computing Toolbox™ is recommended when running the examples. Demo 3 requires Statistics and Machine Learning Toolbox™ in addition to the required products below.
Citation pour cette source
MathWorks Deep Learning Toolbox Team (2026). Deep Learning Tutorial Series (https://fr.mathworks.com/matlabcentral/fileexchange/62990-deep-learning-tutorial-series), MATLAB Central File Exchange. Extrait(e) le .
Remerciements
A inspiré : TFCNN-BiGRU, Training 3D CNN models
Catégories
En savoir plus sur Recognition, Object Detection, and Semantic Segmentation dans Help Center et MATLAB Answers
Informations générales
- Version 1.1.0.0 (23,3 ko)
Compatibilité avec les versions de MATLAB
- Compatible avec toutes les versions
Plateformes compatibles
- Windows
- macOS
- Linux
| Version | Publié le | Notes de version | Action |
|---|---|---|---|
| 1.1.0.0 | minor bug fix in third file, "Demo_FeatureExtraction.mlx" :
|
||
| 1.0.0.0 | + Fixed typo in code. |
