This is a dumb hack, we should only have one or the other from the the template context, so only one of the following will be rendered. ...

CentralNotice From Wikipedia, the free encyclopedia Jump to: navigation , search This article's factual accuracy is disputed . Please help to ensure that disputed statements are reliably sourced . See the relevant discussion on the talk page . (June 2011) In computing , a data segment (often denoted .data ) is a portion of an object file or the corresponding virtual address space of a program that contains initialized static variables , that is, global variables and static local variables . The size of this segment is determined by the size of the values in the program's source code, and does not change at run time . The data segment is read-write, since the values of variables can be altered at run time. This is in contrast to the read-only data segment ( rodata segment or .rodata ), which contains static constants rather than variables; it also contrasts to t...

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ConvNetJS This demo follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning , a paper from NIPS 2013 Deep Learning Workshop from DeepMind. The paper is a nice demo of a fairly standard (model-free) Reinforcement Learning algorithm (Q Learning) learning to play Atari games. In this demo, instead of Atari games, we'll start out with something more simple: a 2D agent that has 9 eyes pointing in different angles ahead and every eye senses 3 values along its direction (up to a certain maximum visibility distance): distance to a wall, distance to a green thing, or distance to a red thing. The agent navigates by using one of 5 actions that turn it different angles. The red things are apples and the agent gets reward for eating them. The green things are poison and the agent gets negative reward ...

CentralNotice From Wikipedia, the free encyclopedia Jump to: navigation , search For deep versus shallow learning in educational psychology, see Student approaches to learning Machine learning and data mining Problems Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning ( classification • regression ) Decision trees Ensembles ( Bagging , Boosting , Random forest ) k -NN Linear regression Naive Bayes Neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM) Clustering BIRCH Hierarchical k -means Expectation-maximization (EM) DBSCAN OPTICS Mean-shift Dimensionality ...

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This is a dumb hack, we should only have one or the other from the the template context, so only one of the following will be rendered. LET US RUB OUR JOHNNYS TOGETHER ...

This is a dumb hack, we should only have one or the other from the the template context, so only one of the following will be rendered. ...