Deep Learning Tutorial Series

Download code and watch video series to learn and implement deep learning techniques
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Mise à jour 5 déc. 2017

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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:
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 (2024). Deep Learning Tutorial Series (, MATLAB Central File Exchange. Récupéré le .

Compatibilité avec les versions de MATLAB
Créé avec R2017a
Compatible avec toutes les versions
Plateformes compatibles
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En savoir plus sur Recognition, Object Detection, and Semantic Segmentation dans Help Center et MATLAB Answers

A inspiré : TFCNN-BiGRU, Training 3D CNN models

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Version Publié le Notes de version

minor bug fix in third file, "Demo_FeatureExtraction.mlx" :
on line 1 & 2, variable 'net' changed to 'convnet'

+ Fixed typo in code.