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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

Related:  Deep LearningMLMachine Learning & Artificial Neural Networks

What is Deep Learning? Scyfer is a University of Amsterdam spinoff that specializes in deep learning technology. We build deep neural network solutions for image and speech recognition as well as for recommender systems. But what is deep learning? Deep Learning Deep learning is a subfield within machine learning that deals with developing efficient training algorithms for deep neural networks. Lecture Slides (pdf) For questions on course lectures, homework, tools, or materials for this course, post in the course discussion forum. Post in the Forum Have general questions about Stanford Lagunita? You can find lots of helpful information in the Stanford Lagunita Help Center. Access the Help Center Can't find an answer to your question?

Resurgence in Neural Networks - If you’ve been paying attention, you’ll notice there has been a lot of news recently about neural networks and the brain. A few years ago the idea of virtual brains seemed so far from reality, especially for me, but in the past few years there has been a breakthrough that has turned neural networks from nifty little toys to actual useful things that keep getting better at doing tasks computers are traditionally very bad at. In this post I’ll cover some background on Neural networks and my experience with them. Then go over the recent discoveries I’ve learned about. At the end of the post I’ll share a sweet little github project I wrote that implements this new neural network approach. Background Welcome — Theano 0.6 documentation How to Seek Help¶ The appropriate venue for seeking help depends on the kind of question you have. How do I? – theano-users mailing list or StackOverflowI got this error, why?

Stephen Marsland This webpage contains the code and other supporting material for the textbook "Machine Learning: An Algorithmic Perspective" by Stephen Marsland, published by CRC Press, part of the Taylor and Francis group. The first edition was published in 2009, and a revised and updated second edition is due out towards the end of 2014. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence. The table of contents for the second edition can be found here.

machine learning in Python — scikit-learn 0.13 documentation "We use scikit-learn to support leading-edge basic research [...]" "I think it's the most well-designed ML package I've seen so far." "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]." "For these tasks, we relied on the excellent scikit-learn package for Python." "The great benefit of scikit-learn is its fast learning curve [...]" Overview — Pylearn2 dev documentation This page gives a high-level overview of the Pylearn2 library and describes how the various parts fit together. First, before learning Pylearn2 it is imperative that you have a good understanding of Theano. Before learning Pylearn2 you should first understand: How Theano uses Variables, Ops, and Apply nodes to represent symbolic expressions.What a Theano function is.What Theano shared variables are and how they can make state persist between calls to Theano functions. Once you have that under your belt, we can move on to Pylearn2 itself. Note that throughout this page we will mention several different classes and functions but not completely describe their parameters.

Professor TL McCluskey - Profile Vallati, M., Hutter, F., Chrpa, L. and McCluskey, T. (2015) ‘On the Effective Configuration of Planning Domain Models’. In: International Joint Conference on Artificial Intelligence, 25th - 31st July, 2015, Argentina Chrpa, L., Vallati, M. and McCluskey, T. (2015) ‘On the Online Generation of Effective Macro-operators’. In: International Joint Conference on Artificial Intelligence (IJCAI) 2015, 25th - 31st July, 2015, Buenos Aires, Argentina Mohammad, R., Thabtah, F. and McCluskey, T. (2015) Phishing Websites Dataset [Dataset]

Neural Networks for Machine Learning 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. Reaching Enlightenment I. Cid’s brain is an instance of a Deep Boltzmann Machine (DBM). In a DBM, the connections amongst the visible and hidden units have a particular structure. This structure removes connections from a fully connected model such that layers in the network can be naturally defined. In a DBM, each layer has no connections amongst its units. Each unit in a layer is connected to every unit in both the layers immediately above and immediately below. The DBM type structure is the middle one in the picture here.

Learning From Data - Online Course (MOOC) A real Caltech course, not a watered-down version Free, introductory Machine Learning online course (MOOC) Taught by Caltech Professor Yaser Abu-Mostafa [article]Lectures recorded from a live broadcast, including Q&APrerequisites: Basic probability, matrices, and calculus8 homework sets and a final examDiscussion forum for participantsTopic-by-topic video library for easy review Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications.

A Guide to Deep Learning by YerevaNN When you are comfortable with the prerequisites, we suggest four options for studying deep learning. Choose any of them or any combination of them. The number of stars indicates the difficulty. Hugo Larochelle's video course on YouTube. The videos were recorded in 2013 but most of the content is still fresh.