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Machine Learning Video Library - Learning From Data (Abu-Mostafa) Machine Learning Cheat Sheet (for scikit-learn) No free hunch | the sport of data science. Machine Learning Repository: Data Sets.

Neural Networks for Machine Learning - View Thread. Learning From Data - Fall Session. More resources for deep learning. Gnumpy. (Russian / Romanian / Belarussian translations by various people) Gnumpy is free software, but if you use it in scientific work that gets published, you should cite this tech report in your publication. Download: (also be sure to have the most recent version of Cudamat) Documentation: here. Do you want to have both the compute power of GPU's and the programming convenience of Python numpy? Gnumpy + Cudamat will bring you that. Gnumpy is a simple Python module that interfaces in a way almost identical to numpy, but does its computations on your computer's GPU.

Gnumpy runs on top of, and therefore requires, the excellent cudamat library, written by Vlad Mnih. Gnumpy can run in simulation mode: everything happens on the CPU, but the interface is the same. See also this presentation by Xavier Arrufat, introducing numpy at the Python for Data Analysis meetup in Barcelona, 2013. Recent changes: Home page. The Next Generation of Neural Networks. U.S. Census Return Rate Challenge (Visualization Competition) Note: The prediction phase of this competition has ended. Please join the visualization competition which ends on Nov. 11, 2012. This challenge is to develop a statistical model to predict census mail return rates at the Census block group level of geography. The Census Bureau will use this model for planning purposes for the decennial census and for demographic sample surveys.

The model-based estimates of predicted mail return will be publicly released in a later version of the Census "planning database" containing updated demographic data. Participants are encouraged to develop and evaluate different statistical approaches to proposing the best predictive model for geographic units. The intent is to improve our current predictive analytics.

Please note also that as described in the rules, only US citizens and residents are eligible for prizes. GE Quest - Let the Quests Begin! Neural Networks for Machine Learning - View Thread. Neural Networks for Machine Learning - View Thread. Neural Networks for Machine Learning - View Thread. Introduction to Information Retrieval. Neural Networks for Machine Learning - View Thread. Resources for nural networks. Machine Learning | CosmoLearning Computer Science. Machine Learning Repository. Kaggle: making data science a sport. PyBrain. Deep Belief Networks. A tutorial on Deep Learning. Complex probabilistic models of unlabeled data can be created by combining simpler models. Mixture models are obtained by averaging the densities of simpler models and "products of experts" are obtained by multiplying the densities together and renormalizing.

A far more powerful type of combination is to form a "composition of experts" by treating the values of the latent variables of one model as the data for learning the next model. The first half of the tutorial will show how deep belief nets -- directed generative models with many layers of hidden variables -- can be learned one layer at a time by composing simple, undirected, product of expert models that only have one hidden layer. It will also explain why composing directed models does not work. Would you like to put a link to this lecture on your homepage? Deep Learning. Backpropagation. The project describes teaching process of multi-layer neural network employing backpropagation algorithm. To illustrate this process the three layer neural network with two inputs and one output,which is shown in the picture below, is used: Each neuron is composed of two units.

First unit adds products of weights coefficients and input signals. The second unit realise nonlinear function, called neuron activation function. Signal e is adder output signal, and y = f(e) is output signal of nonlinear element. Signal y is also output signal of neuron. To teach the neural network we need training data set.

Propagation of signals through the hidden layer. Propagation of signals through the output layer. In the next algorithm step the output signal of the network y is compared with the desired output value (the target), which is found in training data set. It is impossible to compute error signal for internal neurons directly, because output values of these neurons are unknown. PyBrain. Introduction to Neural Networks. CS-449: Neural Networks Fall 99 Instructor: Genevieve Orr Willamette University Lecture Notes prepared by Genevieve Orr, Nici Schraudolph, and Fred Cummins [Content][Links] Course content Summary Our goal is to introduce students to a powerful class of model, the Neural Network.

We then introduce one kind of network in detail: the feedforward network trained by backpropagation of error. Lecture 1: Introduction Lecture 2: Classification Lecture 3: Optimizing Linear Networks Lecture 4: The Backprop Toolbox Lecture 5: Unsupervised Learning Lecture 6: Reinforcement Learning Lecture 7: Advanced Topics [Top] Review for Midterm: Links Tutorials: The Nervous System - a very nice introduction, many pictures Neural Java - a neural network tutorial with Java applets Web Sim - A Java neural network simulator. a book chapter describing the Backpropagation Algorithm (Postscript) A short set of pages showing how a simple backprop net learns to recognize the digits 0-9, with C code Reinforcement Learning - A Tutorial.

An introduction to neural networks. Andrew Blais, Ph.D. ( Mertz, Ph.D. ( Gnosis Software, Inc. June 2001 A convenient way to introduce neural nets is with a puzzle that they can be used to solve. Suppose that you are given, for example, 500 characters of code that you know to be either C, C++, Java or Python. Now, construct a program that identifies the code's language. One solution is to construct a neural net that learns to identify these languages. According to a simplified account, the human brain consists of about ten billion neurons, and a neuron is, on average, connected to several thousand other neurons. Threshold logic units The first step toward neural nets is to abstract from the biological neuron, and to focus on its character as a threshold logic unit (TLU).

A = (X1 * W1)+(X2 * W2)+... The threshold is called theta. Figure 1: Threshold logic unit, with sigma function (top) and cutoff function (bottom) A TLU can classify. Learning stuff During training, a neural net inputs: Neural Network Basics. David Leverington Associate Professor of Geosciences The Feedforward Backpropagation Neural Network Algorithm Although the long-term goal of the neural-network community remains the design of autonomous machine intelligence, the main modern application of artificial neural networks is in the field of pattern recognition (e.g., Joshi et al., 1997). In the sub-field of data classification, neural-network methods have been found to be useful alternatives to statistical techniques such as those which involve regression analysis or probability density estimation (e.g., Holmström et al., 1997). The potential utility of neural networks in the classification of multisource satellite-imagery databases has been recognized for well over a decade, and today neural networks are an established tool in the field of remote sensing.

The most widely applied neural network algorithm in image classification remains the feedforward backpropagation algorithm. 1 Neural Network Basics 2 McCulloch-Pitts Networks. 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. FAQ, Part 1 of 7: Introduction. Learning From Data - Online Course. 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. What is learning? Live Lectures This course was broadcast live from the lecture hall at Caltech in April and May 2012. The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Is Learning Feasible? The Linear Model I - Linear classification and linear regression. Error and Noise - The principled choice of error measures. Deep Learning Tutorial - Slides Updated Version of Tutorial at NAACL 2013 See Videos High quality video of the 2013 NAACL tutorial version are up here: quality version of the 2012 ACL version: on youtube Abstract Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. Outline References All references we referred to in one pdf file Further Information A very useful assignment for getting started with deep learning in NLP is to implement a simple window-based NER tagger in this exercise we designed for the Stanford NLP class 224N.

For your comments, related questions or errata: Save your text first, then fill out captcha, then save again. Wenbo? Hi Richard, I am a big fan of your C S224d? Gebre? Hi Richard, I am building NER System for Tigrigna, one of under resourced Semitic language like Arabic. Thoughts on Machine Learning – the statistical software R | Florian Hartl. This is going to be an ongoing article series about various aspects of Machine Learning. In the first post of the series I’m going to explain why I decided to learn and use R , and why it is probably the best statistical software for Machine Learning at this time. R vs. popular programming languages like Java Implementing Machine Learning algorithms is not an easy task because it requires a deep understanding of the inner workings of the algorithm.

Furthermore, it can make a big difference how the function of a Machine Learning algorithm is implemented in detail because the application of advanced mathematical tricks can enhance the performance of the algorithm substantially. R already provides sophisticated implementations of various Machine Learning algorithms and therefore relieves you from the tedious and error-prone task of implementing your own algorithms. R vs. R vs. R vs. Other reasons for R A great advantage of R is that scientists adopted it as their de facto standard. Machine learning in Python — scikit-learn 0.12.1 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 [...] " "It allows us to do AWesome stuff we would not otherwise accomplish" "scikit-learn makes doing advanced analysis in Python accessible to anyone. " A Brief Introduction to Neural Networks · D. Kriesel. Manuscript Download - Zeta2 Version Filenames are subject to change. Thus, if you place links, please do so with this subpage as target. Original Version? EBookReader Version? The original version is the two-column layouted one you've been used to. The eBookReader optimized version on the other hand has one-column layout. In addition, headers, footers and marginal notes were removed. For print, the eBookReader version obviously is less attractive.

During every release process from now on, the eBookReader version going to be automatically generated from the original content. Further Information for Readers Provide Feedback! This manuscript relies very much on your feedback to improve it. Send emails to me or place a comment in the newly-added discussion section below at the bottom of this page. How to Cite this Manuscript There's no official publisher, so you need to be careful with your citation. This reference is, of course, for the english version. Terms of Use Roadmap I think, this is it … Linear Classification - The Perceptron (Abu-Mostafa) Neural Networks. Artificial Neural Networks: Architectures and Applications by Kenji Suzuki (ed.) - InTech , 2013Artificial neural networks may be the single most successful technology in the last two decades. The purpose of this book is to provide recent advances in architectures, methodologies, and applications of artificial neural networks.(3885 views) Artificial Neural Networks - Wikibooks , 2010Neural networks excel in a number of problem areas where conventional von Neumann computer systems have traditionally been slow and inefficient.

This book is going to discuss the creation and use of artificial neural networks.(4960 views) An Introduction to Computational Neuroscience by Todd Troyer - University of Texas at San Antonio , 2005These notes have three main objectives: to present the major concepts of computational neuroscience, to present the basic mathematics that underlies these concepts, and to give the reader some idea of common approaches taken by neuroscientists.(2095 views) CSC321 Winter 2012: lectures. Tuesday January 8 First class meeting. Explaining how the course will be taught. For the rest of this schedule, students are to study the listed material before the class meeting. The 'lecture' meeting will always be a discussion of the material of that day. The 'tutorial' meetings are used for a variety of purposes.

Map of Campus Buildings [ Home | Lectures, Readings, & Due Dates | Optional Readings | The Tutorials | Computing | Assignments | Tests | ] CSC321 - Computation In Neural Networks: || Python Vs. Octave | setiQuest. [Python] Neuron Module. Basic Perceptron Learning for AND Gate. Mathesaurus. Deep Learning Tutorials — DeepLearning v0.1 documentation.