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.
Is there any specific requirement for minimum version of MATLAB ? I am having errors saying undefined function, may be my MATLAB version don't has that function implemented.
I think, you need to change convnet to net Demo_FeatureExtraction.
thanks for your video. I have one question. For running this simulation, DO I need to have GPU on my computer?
thanks for so excellent video. I have a question for the transfer learning demo. In Matlab, all layers except the last three(any other number) are extracted from the pretrained network, and the last three layers are replaced by new one. There are a few finetune ways, such as finetune the whole network, finetune the last classifier layer, or finetune from any specificed layer. So, I wonder what is the finetune ways in Matlab. finetune the whole network or finetune the last classifier layer? the trainNetwork function implements the retrain( finetune the whole network) ?
I am sorry, THIS is the error I am getting:
Undefined function or variable 'convnet'.
+ Fixed typo in code.