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Hacker's guide to Neural Networks

Hacker's guide to Neural Networks
Hi there, I’m a CS PhD student at Stanford. I’ve worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS - a Javascript library for training Neural Networks. Javascript allows one to nicely visualize what’s going on and to play around with the various hyperparameter settings, but I still regularly hear from people who ask for a more thorough treatment of the topic. This article (which I plan to slowly expand out to lengths of a few book chapters) is my humble attempt. It’s on web instead of PDF because all books should be, and eventually it will hopefully include animations/demos etc. My personal experience with Neural Networks is that everything became much clearer when I started ignoring full-page, dense derivations of backpropagation equations and just started writing code. “…everything became much clearer when I started writing code.” Chapter 1: Real-valued Circuits Base Case: Single Gate in the Circuit f(x,y)=xy The Goal

http://karpathy.github.io/neuralnets/

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Caffe Tutorial Caffe is a deep learning framework and this tutorial explains its philosophy, architecture, and usage. This is a practical guide and framework introduction, so the full frontier, context, and history of deep learning cannot be covered here. While explanations will be given where possible, a background in machine learning and neural networks is helpful. Philosophy In one sip, Caffe is brewed for Expression: models and optimizations are defined as plaintext schemas instead of code. James Caverlee Howdy! My research is generally in the areas of web-scale information management, distributed data-intensive systems, and social computing. Most recently, I've been working on (i) spam and crowdturfing threats to social media and web systems; and (ii) geo-social systems that leverage large-scale spatio-temporal footprints in social media. Recent and Upcoming Activities

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.

Getting started with Latent Dirichlet Allocation in Python — chris' sandbox In this post I will go over installation and basic usage of the lda Python package for Latent Dirichlet Allocation (LDA). I will not go through the theoretical foundations of the method in this post. However, the main reference for this model, Blei etal 2003 is freely available online and I think the main idea of assigning documents in a corpus (set of documents) to latent (hidden) topics based on a vector of words is fairly simple to understand and the example (from lda) will help to solidify our understanding of the LDA model. Machine Learning Course materials Lectures This course is taught by Nando de Freitas. Practicals Please click on Timetables on the right hand side of this page for time and location of the practicals. The instructors are Brendan Shillingford and Marcin Moczulsky.

Regular Expression HOWTO Introduction The re module was added in Python 1.5, and provides Perl-style regular expression patterns. Earlier versions of Python came with the regex module, which provided Emacs-style patterns. The regex module was removed completely in Python 2.5. Python Numpy Tutorial This tutorial was contributed by Justin Johnson. We will use the Python programming language for all assignments in this course. Python is a great general-purpose programming language on its own, but with the help of a few popular libraries (numpy, scipy, matplotlib) it becomes a powerful environment for scientific computing. We expect that many of you will have some experience with Python and numpy; for the rest of you, this section will serve as a quick crash course both on the Python programming language and on the use of Python for scientific computing.

Python Map Reduce on Hadoop - A Beginners Tutorial November 17 2013 Share Tweet Post This article originally accompanied my tutorial session at the Big Data Madison Meetup, November 2013. The goal of this article is to: Understanding Convolution in Deep Learning Convolution is probably the most important concept in deep learning right now. It was convolution and convolutional nets that catapulted deep learning to the forefront of almost any machine learning task there is. But what makes convolution so powerful? How does it work? In this blog post I will explain convolution and relate it to other concepts that will help you to understand convolution thoroughly. There are already some blog post regarding convolution in deep learning, but I found all of them highly confusing with unnecessary mathematical details that do not further the understanding in any meaningful way.

Finding the natural number of topics for Latent Dirichlet Allocation - Christopher Grainger Update (July 13, 2014): I’ve been informed that I should be looking at hierarchical topic models (see Blei’s papers here and here). Thanks to Reddit users /u/GratefulTony and /u/EdwardRaff for bringing this to my attention. However, Redditor /u/NOTWorthless says HDPs do not provide a ‘posterior on the correct number of topics in any meaningful sense’. I’ll do more research and do a follow-up post. You can follow the conversation on Reddit here.

Computer Vision: Algorithms and Applications © 2010 Richard Szeliski, Microsoft Research Welcome to the Web site ( for my computer vision textbook, which you can now purchase at a variety of locations, including Springer (SpringerLink, DOI), Amazon, and Barnes & Noble. The book is also available in Chinese and Japanese (translated by Prof. Toru Tamaki). This book is largely based on the computer vision courses that I have co-taught at the University of Washington (2008, 2005, 2001) and Stanford (2003) with Steve Seitz and David Fleet. You are welcome to download the PDF from this Web site for personal use, but not to repost it on any other Web site.

Arun et al measure with NPR data · GitHub Skip to content Learn more Please note that GitHub no longer supports old versions of Firefox. 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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