Pinned Repositories
BING-Objectness
Python implementation of BING Objectness method from "BING: Binarized Normed Gradients for Objectness Estimation at 300fps".
caffe
Caffe: a fast open framework for deep learning.
Data_Scientist_for_a_Day
This is a practical codelab with the aim to guide beginners into the discipline of machine learning and classification, by means of open source tools. If you do not have both great practical and theorical skills of machine learning, here you can find a tutorial that explain you how to apply those tools, and guide you through the implementation of them. The tutorial lab explains you the tools that you need to understand in order to develop a classification solution of this kind. By completion of the source code, you will have a working solution for classifying on a kaggle competition dataset. The kaggle dataset competition is the London Data Science competition.
Deep-learning-An-Historical-perspective-slides
elliptic-fourier-descriptors
Fast python/numpy implementation of the elliptic fourier descriptors for shapes recognition.
GeodesicDistance
HackingSift
Codelab for implementing image stitching with Sift using Python, OpenCv and Numpy
pyImageStitching
This code implements a python implementation for panorama picture, using OpenCV and numpy
R-CNN-Object-detection
Python-caffe simplified implementation of the R-CNN object detection method. I have taken as a starting point the caffe ipython-notebook available at http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb . The tutorial requires the use of matlab code for calculating selective search bounding boxes. This make it hard to use and slow. Thus, I have implemented bounding boxes proposal with a pythonized BING that I have implemented in another repository. I have also changed the interaction with the script so that the result is a nicer demo.
StockCrawler
alessandroferrari's Repositories
alessandroferrari/BING-Objectness
Python implementation of BING Objectness method from "BING: Binarized Normed Gradients for Objectness Estimation at 300fps".
alessandroferrari/R-CNN-Object-detection
Python-caffe simplified implementation of the R-CNN object detection method. I have taken as a starting point the caffe ipython-notebook available at http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb . The tutorial requires the use of matlab code for calculating selective search bounding boxes. This make it hard to use and slow. Thus, I have implemented bounding boxes proposal with a pythonized BING that I have implemented in another repository. I have also changed the interaction with the script so that the result is a nicer demo.
alessandroferrari/elliptic-fourier-descriptors
Fast python/numpy implementation of the elliptic fourier descriptors for shapes recognition.
alessandroferrari/HackingSift
Codelab for implementing image stitching with Sift using Python, OpenCv and Numpy
alessandroferrari/pyImageStitching
This code implements a python implementation for panorama picture, using OpenCV and numpy
alessandroferrari/Data_Scientist_for_a_Day
This is a practical codelab with the aim to guide beginners into the discipline of machine learning and classification, by means of open source tools. If you do not have both great practical and theorical skills of machine learning, here you can find a tutorial that explain you how to apply those tools, and guide you through the implementation of them. The tutorial lab explains you the tools that you need to understand in order to develop a classification solution of this kind. By completion of the source code, you will have a working solution for classifying on a kaggle competition dataset. The kaggle dataset competition is the London Data Science competition.
alessandroferrari/GeodesicDistance
alessandroferrari/StockCrawler
alessandroferrari/caffe
Caffe: a fast open framework for deep learning.
alessandroferrari/Deep-learning-An-Historical-perspective-slides
alessandroferrari/defeatcovid19-net-pytorch
Pytorch solution for predictions on X-ray images of COVID-19 patients
alessandroferrari/gdbn
George Dahl's gdbn: Pre-trained deep neural networks
alessandroferrari/InDepthDeepLearning
alessandroferrari/Intelligenza-artificiale--e-libera--Linux-Day-2015-Brescia-LugBS
alessandroferrari/microbia_demo
alessandroferrari/MuHi
Eye traking/multi functional project
alessandroferrari/nolearn
scikit-learn compatible wrappers for neural net libraries, and other utilities.
alessandroferrari/Numpy_GetRidOfTheMathesaurus
Codelab for introducing Python and Numpy to scientists and engineers
alessandroferrari/Objectness
BING Objectness proposal estimator linux/mac/windows version implementation, runs at 1000 FPS. More in http://mmcheng.net/bing/
alessandroferrari/timus
Fun and C++ exercises solving acm.timus.ru algorithmical challenges.
alessandroferrari/ufldl_tutorial
Stanford Unsupervised Feature Learning and Deep Learning Tutorial