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UFLDL Tutorial - Ufldl

UFLDL Tutorial - Ufldl
From Ufldl Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems. This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent). If you are not familiar with these ideas, we suggest you go to this Machine Learning course and complete sections II, III, IV (up to Logistic Regression) first. Sparse Autoencoder Vectorized implementation Preprocessing: PCA and Whitening Softmax Regression Self-Taught Learning and Unsupervised Feature Learning Building Deep Networks for Classification Linear Decoders with Autoencoders Working with Large Images Note: The sections above this line are stable. Miscellaneous Miscellaneous Topics Advanced Topics: Sparse Coding

http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial

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Visual Information Processing and Learning Research topic #5: Lighting Preprocessing Lighting preprocessing aims at achieving illumination-insensitive face recognition by removing uneven illumination (e.g. shadow, underexposure, and overexposure) that appears in face images. Our work on lighting preprocessing includes four main approaches: 1) Image enhancement based methods. Illumination in face images leads to skewed intensity distribution. Therefore, uneven lighting in face images can be normalized by correcting the skewed intensity distribution. Where are the Deep Learning Courses? — Data Community DC This is a guest post by John Kaufhold. Dr. Kaufhold is a data scientist and managing partner of Deep Learning Analytics, a data science company based in Arlington, VA. He presented an introduction to Deep Learning at the March Data Science DC. Why aren't there more Deep Learning talks, tutorials, or workshops in DC2?

Java Machine Learning Are you a Java programmer and looking to get started or practice machine learning? Writing programs that make use of machine learning is the best way to learn machine learning. You can write the algorithms yourself from scratch, but you can make a lot more progress if you leverage an existing open source library. Whitening - Ufldl From Ufldl Introduction We have used PCA to reduce the dimension of the data. Advice on Learning Deep Learning ( Neural Networks) manu prakash wrote: I think of starting with a small project like Digit recognition, and learn the techniques needed to complete that small project. You could use the UFLDL Tutorial by Andrew Ng as a starting point. He even uses MATLAB and a digit recognition task to teach you the main ideas of Unsupervised Feature Learning and Deep Learning.

IPython Notebooks for StatLearning Exercises Earlier this year, I attended the StatLearning: Statistical Learning course, a free online course taught by Stanford University professors Trevor Hastie and Rob Tibshirani. They are also the authors of The Elements of Statistical Learning (ESL) and co-authors of its less math-heavy sibling: An Introduction to Statistical Learning (ISL). The course was based on the ISL book. Each week's videos were accompanied by some hands-on exercises in R. I personally find it easier to work with Python than R.

videofacerec.py example help asked Jul 12 '12 coredumped3 ● 1 ● 2 Hello, I'm currently doing some work in face recognition with small training data samples (typically only one per person). I was initially trying to do face recognition with eigenfaces but getting terrible results, and was guided to use Local Binary Patterns Histograms by this stack overflow post: deeplearning:slides:start On-Line Material from Other Sources A quick overview of some of the material contained in the course is available from my ICML 2013 tutorial on Deep Learning: Q&A about deep learning (Spring 2013 course on large-scale ML) 2012 IPAM Summer School deep learning and representation learning 2014 International Conference on Learning Representations (ICLR 2014) Week 1 2014-01-27 Lecture * Intro to Deep Learning

Infinite Mixture Models with Nonparametric Bayes and the Dirichlet Process Imagine you’re a budding chef. A data-curious one, of course, so you start by taking a set of foods (pizza, salad, spaghetti, etc.) and ask 10 friends how much of each they ate in the past day. Your goal: to find natural groups of foodies, so that you can better cater to each cluster’s tastes.

Welcome — Pylearn2 dev documentation Pylearn2 is still undergoing rapid development. Don’t expect a clean road without bumps! If you find a bug please write to pylearn-dev@googlegroups.com. If you’re a Pylearn2 developer and you find a bug, please write a unit test for it so the bug doesn’t come back! Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. Where to Learn Deep Learning – Courses, Tutorials, Software Deep Learning is a very hot Machine Learning techniques which has been achieving remarkable results recently. We give a list of free resources for learning and using Deep Learning. By Gregory Piatetsky, @kdnuggets, May 26, 2014. Deep Learning is a very hot area of Machine Learning Research, with many remarkable recent successes, such as 97.5% accuracy on face recognition, nearly perfect German traffic sign recognition, or even Dogs vs Cats image recognition with 98.9% accuracy. Many winning entries in recent Kaggle Data Science competitions have used Deep Learning. The term "deep learning" refers to the method of training multi-layered neural networks, and became popular after papers by Geoffrey Hinton and his co-workers which showed a fast way to train such networks.

Recommending music on Spotify with deep learning – Sander Dieleman This summer, I’m interning at Spotify in New York City, where I’m working on content-based music recommendation using convolutional neural networks. In this post, I’ll explain my approach and show some preliminary results. Overview This is going to be a long post, so here’s an overview of the different sections. A comparative study on illumination preprocessing in face recognition a Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, Chinab Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USAc School of Electrical Engineering and Computer Science, Peking University, Beijing 100871, China Received 22 February 2012, Revised 6 November 2012, Accepted 21 November 2012, Available online 1 December 2012 Choose an option to locate/access this article:

Neural Networks for Machine Learning - University of Toronto About the Course Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods.

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