Deep Learning Tutorial Series

Download code and watch video series to learn and implement deep learning techniques
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Actualizado 5 dic 2017

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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.

Citar como

MathWorks Deep Learning Toolbox Team (2024). Deep Learning Tutorial Series (https://www.mathworks.com/matlabcentral/fileexchange/62990-deep-learning-tutorial-series), MATLAB Central File Exchange. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2017a
Compatible con cualquier versión
Compatibilidad con las plataformas
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Más información sobre Recognition, Object Detection, and Semantic Segmentation en Help Center y MATLAB Answers.
Agradecimientos

Inspiración para: TFCNN-BiGRU, Training 3D CNN models

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Versión Publicado Notas de la versión
1.1.0.0

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

1.0.0.0

+ Fixed typo in code.