I am trying to create something similar to https://kr.mathworks.com/help/comm/ug/spectrum-sensing-with-deep-learning-to-identify-5g-and-lte-signals.html this. But instead of 5G, I want to create LTE and WiFi spectogram image dataset for training. Is there any way I can do that. Please let me know. Thank you.
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I'm new to deep learning and I'm working on a project that involves working on cartoon images and recognizing the emotions of the cartoon characters, I tried the approach of transfer learning but on doing some research I realised that the ImageNet and InceptionV3 only work for human faces. What approach should I follow? The training set is limited of about 300 images and the test set has around 180 images. I'm still a beginner in this field and I thought this would be a good project to start with. Any suggestions/guidance will be much appreciated. Thank you .
If your data is very low you can use data augmentation.Take a look:
https://towardsdatascience.com/data-augmentation-for-deep-learning-4fe21d1a4eb9
and also:
https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/
If data augmentation did not help you, you must try another
algorithms.neural nets needs lots of data.If your Data is less your
network will overfitt.
you can use data augmentation. for data augmentation, you could use the imgaug package. here is the documentation of Imgaug package
Is there a simple explanation for dummies like me? I know that there's a source code of Leela, I've heard that it uses neural networks with MCTS (plus UCT), but there are lot of hard things remaining. Do I need to train Leela myself by running it? Or do I need to download something from Internet? (so-called trained data?) If so, do I need constantly update this data? Does it play stronger with every game?
Thank you much for advance.
I have a few general questions regarding using pre-trained image classification models in mobile.
How big is a typical pre-trained model?
If it is too big for mobile, what is the best strategy from there?
I checked out the documentation of DeepLearning for Java, anywhere to download pre-trained model?
Thanks in advance.
It's really task dependent. I mean..you can't just say given an unknown problem what is an arbitrary size of neural net.
In general, if you're doing vision expect to be hundreds of megs, but a lot of it comes down tot he activation sizes. I would advise just doing some benchmarking overall. You can't really just handwave that.
A lot of the pretrained models are for computer vision only. They are based on keras. You shouldn't "download" them yourself. No framework works like that.
We have a managed module for that in the model zoo you should use instead.
Here is the link for Geoff Hinton's Matlab code.
He has made this code for handwriting recognition.
I want an object recognition code, so I want to train it with my own image. I am doing it by making few changes (loading my own image data-sets instead of digit0.mat) in makebatches.m file.
But I am not able to find after training it, that from where to test the created neural network in my own way?
Can someone please help by looking into the code?
How do i go about to create and train a simple neural network in order to recognise patterns?
Does anybody have sample codes to guide me through or someone points to a direction?
Firstly you could start by reading the Neural Network Toolbos users guide. Also take a look at this tutorial. You have code samples so it is a good start. Good luck.