Projects for the Intelligent Systems for Pattern Recognition course @ University of Pisa
Implement the convolution of a set of edge detection filters with an image and apply it to one face image and one tree image of your choice from the dataset. Implement Roberts, Prewitt and Sobel filters (see here, Section 5.2, for a reference) and compare the results (it is sufficient to do it visually). You should not use the library functions for performing the convolution or to generate the Sobel filter. Implement your own and show the code!
Implement from scratch an RBM and apply it to MNIST. The RBM should be implemented fully by you (both CD-1 training and inference steps) but you are free to use library functions for the rest (e.g. image loading and management, etc.).
- Train an RBM with 100 hidden neurons (single layer) on the MNIST data (use the training set split provided by the website).
- Use the trained RBM to encode all the images using the corresponding activation of the hidden neurons.
- Train a simple classifier (e.g. any simple classifier in scikit) to recognize the MNIST digits using as inputs their encoding obtained at step 2. Use the standard training/test split. Show the resulting confusion matrices (training and test) in your presentation.
DATASET (PRESIDENTIAL SPEECHES) Pick up one of the available implementations of the Char-RNN and train it on the presidential speech corpora. In particular, be sure to train 2 separate models, one on all the speeches from President Clinton and one on all the speeches from President Trump. Use the two models to generate new speeches and provide some samples of it at your choice. Should you want to perform any other analysis, you are free to do so.
Google DeepMind AlphaGo: Mastering the game of Go with deep neural networks and tree search