This is a brief summary of my learnt topics in deep learning. Although this is not a tutorial you can still find some usefull codes in the correspodent topic folder.
Along my studies of convolutional neural networks (ConvNet) autoencoders, I reproduced the examples suggested on Keras blog (https://blog.keras.io/building-autoencoders-in-keras.html). The main idea behind autoencoders is first reduce the dimensionality (Encoder) and than later increase back (Decoder).
One of the most interesting examples we used keras to construct an autoencoder capable to denoise the MNIST images. Bellow, the result after 100 epochs training on MNIST data using a ConvNet architecture described on the later figure.
I am currently using ConvNet autoencoders on experiments with Digital Holography Microscopy. Naturally, much complex architecture using batch normalization and other tricks.
Here as part of the exercises proposed for the quantum machine learning reading group (qml-rg) we used the basic LeNet CNN architecture to identify the Einstein or Marie Curie images into a given set of images. Here we tried two ways of training the model: Standard and using Augmentation for the training dataset. Turns out that the use us augmentation improves enormously the accuracy of the model.
| model | Accuracy | Loss |
|---|---|---|
| Standard | 88.89% | 1.21 |
| Augmented | 100% | 0.0 |
Here is my first example of Deep Learning algorithm using Keras. We used the MNIST dataset of handwriting numbers (60.000 images 28x28 px) to train a basic CNN. This example comes from PyImageSearch site.
