This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
Repository Index
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Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.
R-CNN: Regions with Convolutional Neural Network Features
This program converts MATLAB®/Octave figures to TikZ/pgfplots figures for smooth integration into LaTeX.
- matlab
- tikz
- pgfplots
- octave
Structured Edge Detection Toolbox
Chebfun: numerical computing with functions.
Object detection system using deformable part models (DPMs) and latent SVM (voc-release5). You may want to use the latest tarball on my website. The github code may include code changes that have not been tested as thoroughly and will not necessarily reproduce the results on the website.
Fashion Detection in the Wild (Deep Clothes Detector)
- computer-vision
- deep-learning
- clothes-detection
- clothes-detector
HDR Toolbox for processing High Dynamic Range (HDR) images into MATLAB and Octave
- hdr
- tone-mapping
- inverse-tonemapping
- tonemapping
SPP_net : Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Python implementation of the Short Term Objective Intelligibility measure
TUM Roborace Team Software Stack - Path tracking control, velocity control, curvature control and state estimation.
Hierarchical Convolutional Features for Visual Tracking (ICCV 2015)
A Network Integration Approach for Drug-Target Interaction Prediction
TUM Roborace Team Software Stack - Vehicle Simulation
Instructions for building an almost consumer hardware based prototype of a hearing aid
- hearing
- hearing-aids
- signal-processing
- jack
No description provided by the repository host.
No description provided by the repository host.
Matlab codes that convert a RGBD image into a cad like model. This code is released as a part of my PhD dissertation.
DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.
Histology Topography Cytometry Analysis Toolbox