Comparing the Top Five Computer Vision APIs. Clarifai | Image & Video Recognition API. Le Deep Learning pas à pas : l'implémentation. L’engouement actuel pour le Deep Learning ne repose pas sur les seules avancées conceptuelles de Hinton et al. mais aussi sur des avancées technologiques. Après l’introduction aux concepts présentés dans la partie I de cet article, nous abordons ici les questions liées à l’implémentation de ces réseaux. L’enjeu majeur de la performance Pour un informaticien, l’implémentation d’un DBN repose principalement sur le calcul de la formule (7) Malgré les simplifications astucieuses obtenues en utilisant l’algorithme de Contrastive Divergence et en choisissant les RBM comme briques élémentaires, les expressions mathématiques à évaluer restent très coûteuses en temps de calcul.
Les GPU ont des fréquences d’horloge bien moindres que celles des CPU (20x) mais ils possèdent de nombreux cœurs (unités de calcul). Figure 1: Architecture GPU vs CPU Figure 2 : Photos extraites du catalogue ILSRVC. Une offre pléthorique d’outils et langages Theano, outil par excellence pour coder un DBN ? Conclusion. Best Machine Learning Resources for Getting Started. This was a really hard post to write because I want it to be really valuable. I sat down with a blank page and asked the really hard question of what are the very best libraries, courses, papers and books I would recommend to an absolute beginner in the field of Machine Learning. I really agonised over what to include and what to exclude. I had to work hard to put my self in the shoes of a programmer and beginner at machine learning and think about what resources would best benefit them. I picked the best for each type of resource.
Programming Libraries I am an advocate of “learn just enough to be dangerous and start trying things”. This is how I learned to program and I’m sure many other people learned that way too. Find a library and read the documentation, follow the tutorials and start trying things out. Start with a library in a language you know well then move on to other more powerful libraries.
Pick a platform and use it to do your practical machine learning education. Video Courses. Best Machine Learning Resources for Getting Started. Best Machine Learning Resources for Getting Started.
GitHub - GemHunt/CoinSorter: Sorts coins by solenoid on a conveyor by classifying images with Caffe & DIGETS. Kaggle: The Home of Data Science. No free hunch | blog for the home of data science. Datascience. Challenge data - Home. Audio Fingerprinting with Python and Numpy · Will Drevo. Published November 15, 2013 The first day I tried out Shazam, I was blown away.
Next to GPS and surviving the fall down a flight of stairs, being able to recognize a song from a vast corpus of audio was the most incredible thing I'd ever seen my phone do. This recognition works though a process called audio fingerprinting. Examples include: After a few weekends of puzzling through academic papers and writing code, I came up with the Dejavu Project, an open-source audio fingerprinting project in Python. On my testing dataset, Dejavu exhibits 100% recall when reading an unknown wave file from disk or listening to a recording for at least 5 seconds. Following is all the knowledge you need to understand audio fingerprinting and recognition, starting from the basics.
Music as a signal As a computer scientist, my familiarity with the Fast Fourier Transform (FFT) was only that it was a cool way to mutliply polynomials in O(nlog(n)) time. Sampling Spectrograms 1. Caffe | Deep Learning Framework.