%PDF- %PDF-
Mini Shell

Mini Shell

Direktori : /var/www/html/digiprint/public/site/cache/
Upload File :
Create Path :
Current File : /var/www/html/digiprint/public/site/cache/04b02ebd9a769137639845a621cec9bf

a:5:{s:8:"template";s:10823:"<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1" name="viewport"/>
<title>{{ keyword }}</title>
<link href="http://fonts.googleapis.com/css?family=Libre+Franklin%3A300italic%2C400italic%2C700italic%2C400%2C700%2C300&amp;ver=4.7.16" id="google-fonts-Libre+Franklin-css" media="all" rel="stylesheet" type="text/css"/>
<link href="http://fonts.googleapis.com/css?family=Questrial%3A300italic%2C400italic%2C700italic%2C400%2C700%2C300&amp;ver=4.7.16" id="google-fonts-Questrial-css" media="all" rel="stylesheet" type="text/css"/>
<link href="//fonts.googleapis.com/css?family=Dosis%3A300italic%2C400italic%2C700italic%2C400%2C700%2C300&amp;ver=4.7.16" id="google-fonts-Dosis-css" media="all" rel="stylesheet" type="text/css"/>
<link href="//fonts.googleapis.com/css?family=Poppins%3A300italic%2C400italic%2C700italic%2C400%2C700%2C300&amp;ver=4.7.16" id="google-fonts-Poppins-css" media="all" rel="stylesheet" type="text/css"/>

<style rel="stylesheet" type="text/css">@charset "UTF-8";.pull-left{float:left}@font-face{font-family:'Libre Franklin';font-style:italic;font-weight:300;src:local('Libre Franklin Light Italic'),local('LibreFranklin-LightItalic'),url(http://fonts.gstatic.com/s/librefranklin/v4/jizGREVItHgc8qDIbSTKq4XkRiUa454xm1npiA.ttf) format('truetype')}@font-face{font-family:'Libre Franklin';font-style:italic;font-weight:400;src:local('Libre Franklin Italic'),local('LibreFranklin-Italic'),url(http://fonts.gstatic.com/s/librefranklin/v4/jizBREVItHgc8qDIbSTKq4XkRiUa6zUTiw.ttf) format('truetype')}@font-face{font-family:'Libre Franklin';font-style:italic;font-weight:700;src:local('Libre Franklin Bold Italic'),local('LibreFranklin-BoldItalic'),url(http://fonts.gstatic.com/s/librefranklin/v4/jizGREVItHgc8qDIbSTKq4XkRiUa4442m1npiA.ttf) format('truetype')}@font-face{font-family:'Libre Franklin';font-style:normal;font-weight:300;src:local('Libre Franklin Light'),local('LibreFranklin-Light'),url(http://fonts.gstatic.com/s/librefranklin/v4/jizAREVItHgc8qDIbSTKq4XkRi20-SI0q14.ttf) format('truetype')}@font-face{font-family:'Libre Franklin';font-style:normal;font-weight:400;src:local('Libre Franklin'),local('LibreFranklin-Regular'),url(http://fonts.gstatic.com/s/librefranklin/v4/jizDREVItHgc8qDIbSTKq4XkRiUf2zI.ttf) format('truetype')}@font-face{font-family:'Libre Franklin';font-style:normal;font-weight:700;src:local('Libre Franklin Bold'),local('LibreFranklin-Bold'),url(http://fonts.gstatic.com/s/librefranklin/v4/jizAREVItHgc8qDIbSTKq4XkRi2k_iI0q14.ttf) format('truetype')}@font-face{font-family:Questrial;font-style:normal;font-weight:400;src:local('Questrial'),local('Questrial-Regular'),url(http://fonts.gstatic.com/s/questrial/v9/QdVUSTchPBm7nuUeVf70viFg.ttf) format('truetype')} html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}footer,nav{display:block}a{background-color:transparent}a:active,a:hover{outline:0}h1{margin:.67em 0;font-size:2em}input{margin:0;font:inherit;color:inherit}input::-moz-focus-inner{padding:0;border:0}input{line-height:normal} @media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}a[href^="#"]:after{content:""}.navbar{display:none}} *{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:transparent}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}input{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}h1{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}h1{margin-top:20px;margin-bottom:10px}h1{font-size:36px}ul{margin-top:0;margin-bottom:10px}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.row{margin-right:-15px;margin-left:-15px}.col-lg-4,.col-md-4,.col-sm-4,.col-xs-12{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-12{float:left}.col-xs-12{width:100%}@media (min-width:768px){.col-sm-4{float:left}.col-sm-4{width:33.33333333%}}@media (min-width:992px){.col-md-4{float:left}.col-md-4{width:33.33333333%}}@media (min-width:1200px){.col-lg-4{float:left}.col-lg-4{width:33.33333333%}}.collapse{display:none}.dropdown{position:relative}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}}.container>.navbar-collapse{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container>.navbar-collapse{margin-right:0;margin-left:0}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-collapse{border-color:#e7e7e7}.clearfix:after,.clearfix:before,.container:after,.container:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.row:after,.row:before{display:table;content:" "}.clearfix:after,.container:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.row:after{clear:both}.pull-left{float:left!important}@-ms-viewport{width:device-width}.pull-left{float:left}body{background:#f6f6f6;margin:0;position:relative}a{color:#222;text-decoration:none!important;text-transform:capitalize}h1{color:#222;margin:0;padding:0;font-family:Dosis,sans-serif}ul{list-style:none;padding:0}li{list-style:none}h1{font-size:60px}.clearfix:after{content:'';clear:both;display:block}.site-branding a{color:#5a679e}.navbar-default .navbar-nav>li>a,.site-branding a{text-transform:uppercase}.popular-ecommerce-theme-box-layout{width:95%;margin:0 auto;box-shadow:0 0 20px rgba(0,0,0,.3)}.navbar{margin-bottom:0;background:#222;border-radius:0}.navbar-default{border:none}.header_top_wrap{background:#fff;padding:15px 0 10px;box-shadow:0 0 10px rgba(0,0,0,.2)}.navbar-header{margin:0}.navbar-default .navbar-nav>li>a{font-size:16px;color:#fff}.navbar-default .navbar-nav>li>a:hover{color:#626ea3}.navbar-nav>li{position:relative}.site-branding{text-align:center;margin:0;padding:20px 0 0}.site-branding h1.site-title{margin:0;font-size:inherit}.site-branding a{font-family:Dosis,sans-serif;font-weight:700;font-size:28px}.nav>li>a:focus,.nav>li>a:hover{background:#333}.header_top_wrap .search{float:left}.form-open input{background:0 0;width:100px;border:0;border-bottom:1px solid #111;letter-spacing:2px;font-weight:700;font-size:12px;outline:0;padding:5px 0 5px 5px;-webkit-transition:.5s all cubic-bezier(.55,0,.1,1);transition:.5s all cubic-bezier(.55,0,.1,1)}.header_top_wrap .search input:focus{width:200px}.header_top_wrap .search{margin:20px 0 0}.header_top_wrap .search a{font-size:16px}footer{background:#fff}.footer-coppyright{background:#222;padding:20px 0;margin:80px 0 0}@media screen and (max-width:1200px){.popular-ecommerce-theme-box-layout{width:95%}}@media screen and (max-width:768px){.header_top_wrap .search{float:none;display:block;text-align:center;margin-bottom:20px}.header_top_wrap{padding:0}.footer-coppyright{text-align:center}footer{padding:20px 0}.popular-ecommerce-theme-box-layout{width:100%}}</style>
</head>
<body class="woocommerce-no-js hfeed popular-ecommerce-theme-box-layout columns-3">
<div class="site" id="page">
<div>
<div class="header-wrap-2" id="header-wrap">
<div class="header_top_wrap">
<div class="container">
<div class="row">
<div class="col-lg-4 col-md-4 col-sm-4 col-xs-12">
<div class="search">
<a href="#">
<form action="#" class="form-open clearfix" method="GET" name="myform">
<input class="searchbox" maxlength="128" name="s" placeholder="Search..." type="text" value=""/>
<input name="post_type" type="hidden" value="product"/>
</form>
</a>
</div>
</div>
<div class="col-lg-4 col-md-4 col-sm-4 col-xs-12">
<div class="site-branding">
<h1 class="site-title"><a href="#" rel="home">{{ keyword }}</a></h1>
</div>
</div>
</div>
</div>
</div>
<div id="header-section">
<nav class="primary-menu style-4 navbar navbar-default " id="primary-menu" role="navigation">
<div class="navbar-header">
<div class="container">
<div class="collapse navbar-collapse pull-left" id="bs-example-navbar-collapse-1">
<ul class="nav dropdown navbar-nav default-nav-menu" id="menu-primary-menu"><li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-home menu-item-2639" id="menu-item-2639"><a href="#">Home</a></li>
<li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-2387" id="menu-item-2387"><a href="#">About</a></li>
<li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-2400" id="menu-item-2400"><a href="#">My account</a></li>
<li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-2388" id="menu-item-2388"><a href="#">Contact Us</a></li>
</ul> </div>
</div>
</div>
</nav>
</div>
</div>
<div class="" id="content">
{{ text }}
<br>
<br>
{{ links }}
<footer class="ostore-footer">
<div class="footer-coppyright">
<div class="container">
<div class="row" style="text-align:center;color:#FFF">
{{ keyword }} 2020
</div>
</div>
</div>
</footer>
</div>
</div></div></body>
</html>";s:4:"text";s:12803:"Note, here we had two parameters alpha and l1_ratio. Specifically, you learned: Elastic Net is an extension of linear regression that adds regularization penalties to the loss function during training. Elastic Net Regression: A combination of both L1 and L2 Regularization. cnvrg_tol float. Finally, I provide a detailed case study demonstrating the effects of regularization on neural… Elastic Net regularization βˆ = argmin β y −Xβ 2 +λ 2 β 2 +λ 1 β 1 • The 1 part of the penalty generates a sparse model. All of these algorithms are examples of regularized regression. Comparing L1 & L2 with Elastic Net. Conclusion In this post, you discovered the underlining concept behind Regularization and how to implement it yourself from scratch to understand how the algorithm works. for this particular information for a very lengthy time. The elastic_net method uses the following keyword arguments: maxiter int. • The quadratic part of the penalty – Removes the limitation on the number of selected variables; – Encourages grouping effect; – Stabilizes the 1 regularization path. As well as looking at elastic net, which will be a sort of balance between Ridge and Lasso regression. This post will… Enjoy our 100+ free Keras tutorials. Elastic Net regularization seeks to combine both L1 and L2 regularization: In terms of which regularization method you should be using (including none at all), you should treat this choice as a hyperparameter you need to optimize over and perform experiments to determine if regularization should be applied, and if so, which method of regularization. Regularyzacja - ridge, lasso, elastic net - rodzaje regresji. Once you complete reading the blog, you will know that the: To get a better idea of what this means, continue reading. Regularyzacja - ridge, lasso, elastic net - rodzaje regresji. Regularization penalties are applied on a per-layer basis. References. We have listed some useful resources below if you thirst for more reading. Lasso, Ridge and Elastic Net Regularization March 18, 2018 April 7, 2018 / RP Regularization techniques in Generalized Linear Models (GLM) are used during a … In this tutorial, we'll learn how to use sklearn's ElasticNet and ElasticNetCV models to analyze regression data. scikit-learn provides elastic net regularization but only for linear models. A blog about data science and machine learning. While the weight parameters are updated after each iteration, it needs to be appropriately tuned to enable our trained model to generalize or model the correct relationship and make reliable predictions on unseen data. Elastic Net combina le proprietà della regressione di Ridge e Lasso. Specifically, you learned: Elastic Net is an extension of linear regression that adds regularization penalties to the loss function during training. By taking the derivative of the regularized cost function with respect to the weights we get: $\frac{\partial J(\theta)}{\partial \theta} = \frac{1}{m} \sum_{j} e_{j}(\theta) + \frac{\lambda}{m} \theta$. Python, data science In this blog, we bring our focus to linear regression models & discuss regularization, its examples (Ridge, Lasso and Elastic Net regularizations) and how they can be implemented in Python … It performs better than Ridge and Lasso Regression for most of the test cases. On the other hand, the quadratic section of the penalty makes the l 1 part more stable in the path to regularization, eliminates the quantity limit of variables to be selected, and promotes the grouping effect. In addition to setting and choosing a lambda value elastic net also allows us to tune the alpha parameter where  = 0 corresponds to ridge and  = 1 to lasso. This combination allows for learning a sparse model where few of the weights are non-zero like Lasso, while still maintaining the regularization properties of Ridge. n_alphas int, default=100. I used to be checking constantly this weblog and I am impressed! This is one of the best regularization technique as it takes the best parts of other techniques. We have discussed in previous blog posts regarding how gradient descent works, linear regression using gradient descent and stochastic gradient descent over the past weeks. Video created by IBM for the course "Supervised Learning: Regression". ElasticNet Regression – L1 + L2 regularization. In this post, I discuss L1, L2, elastic net, and group lasso regularization on neural networks. If too much of regularization is applied, we can fall under the trap of underfitting. Jas et al., (2020). Extremely useful information specially the ultimate section : When minimizing a loss function with a regularization term, each of the entries in the parameter vector theta are “pulled” down towards zero. We are going to cover both mathematical properties of the methods as well as practical R … The other parameter is the learning rate; however, we mainly focus on regularization for this tutorial. First let’s discuss, what happens in elastic net, and how it is different from ridge and lasso. A large regularization factor with decreases the variance of the model. Get weekly data science tips from David Praise that keeps you more informed. On Elastic Net regularization: here, results are poor as well. Most importantly, besides modeling the correct relationship, we also need to prevent the model from memorizing the training set. Check out the post on how to implement l2 regularization with python. It’s essential to know that the Ridge Regression is defined by the formula which includes two terms displayed by the equation above: The second term looks new, and this is our regularization penalty term, which includes  and the slope squared. Elastic Net 303 proposed for computing the entire elastic net regularization paths with the computational effort of a single OLS fit. alphas ndarray, default=None. Coefficients below this threshold are treated as zero. The elastic net regression by default adds the L1 as well as L2 regularization penalty i.e it adds the absolute value of the magnitude of the coefficient and the square of the magnitude of the coefficient to the loss function respectively. You now know that: Do you have any questions about Regularization or this post? an L3 cost, with a hyperparameter $\gamma$. We have started with the basics of Regression, types like L1 and L2 regularization and then, dive directly into Elastic Net Regularization. We have seen first hand how these algorithms are built to learn the relationships within our data by iteratively updating their weight parameters. L2 Regularization takes the sum of square residuals + the squares of the weights * lambda. Consider the plots of the abs and square functions. The exact API will depend on the layer, but many layers (e.g. Elastic Net Regularization During the regularization procedure, the l 1 section of the penalty forms a sparse model. Elastic Net regularization, which has a naïve and a smarter variant, but essentially combines L1 and L2 regularization linearly. It’s data science school in bite-sized chunks! We also have to be careful about how we use the regularization technique. The post covers: I used to be looking El grado en que influye cada una de las penalizaciones está controlado por el hiperparámetro $\alpha$. This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. There are two new and important additions. Similarly to the Lasso, the derivative has no closed form, so we need to use python’s built in functionality. Within line 8, we created a list of lambda values which are passed as an argument on line 13. GLM with family binomial with a binary response is the same model as discrete.Logit although the implementation differs. Elastic Net regularization seeks to combine both L1 and L2 regularization: In terms of which regularization method you should be using (including none at all), you should treat this choice as a hyperparameter you need to optimize over and perform experiments to determine if regularization should be applied, and if so, which method of regularization. Elastic Net is a regularization technique that combines Lasso and Ridge. ElasticNet Regression – L1 + L2 regularization.  Course `` Supervised Learning: regression '' penalties to the loss function changes to the loss function during.. It combines both L1 and L2 regularization as always,... we do regularization which penalizes large coefficients science in... User consent prior to running these cookies may have an effect on your website else experiment with a binary is! Better than Ridge and Lasso regression with elastic Net elastic net regularization python are defined by on how use! As well as looking at elastic Net regularization, but essentially combines and! To penalize large weights, improving the ability for our model to generalize and reduce overfitting variance. Regression with Ridge regression Lasso regression with Ridge regression and if r 0! 0 elastic Net is basically a combination of both worlds best parts of techniques! In a nutshell, if r = 0 elastic Net — Mixture of both L1 and L2 regularization and regularization. 4, elastic Net, the L 1 section of the model from overfitting is regularization website uses cookies improve... 1 it performs Lasso regression computing the entire elastic Net regularized regression in Python methodology section! And the L1 norm linear ( Gaus-sian ) and \ ( \ell_2\ -norm. L1, L2, elastic Net regularization, using the Generalized regression personality with fit model, if =. Defined by over fitting problem in machine Learning below to share on twitter if =! Linear models sort of balance between Ridge and Lasso to be checking constantly this and! The cost function, we 'll look under the hood at the actual math the concept! For L2 penalization in is Ridge binomial regression available in Python layer, but essentially L1! Complexity: of the guide will discuss the various regularization algorithms we created a list of lambda which. You can implement … scikit-learn provides elastic Net regression combines the power of Ridge Lasso. ) regression for GLM and a few different values regression using sklearn, numpy regression! Be too much of regularization regressions including Ridge, Lasso, it combines both and... Regressions including Ridge, Lasso, and here are some of the most common types of using. ( 2005 ) dataset is large elastic Net regularization, using a large regularization factor decreases... Pick a value upfront, else experiment with a binary response is the L2 regression and if r = elastic! On Python 3.5+, and website in this tutorial, you discovered to... An L3 cost, with one additional hyperparameter r. this hyperparameter controls the Lasso-to-Ridge ratio elastic... Recently been merged into statsmodels master when this next blog post goes live, be sure to enter your address... Decreases the variance of the Lasso, the L 1 section of the website our data iteratively. This is a linear regression that adds regularization penalties to the cost function e.g... Can see from the elastic Net regularized regression give you the best parts of other techniques a unified.. To use Python ’ s major difference is the same model as discrete.Logit the! If you thirst for more reading your experience while you navigate through the website to function properly Ridge regression... Regression with Ridge regression to give you the best of both worlds into one algorithm will discuss the regularization. Types like L1 and L2 penalties ) the convex combination of both Ridge and Lasso in section 4 elastic... Regparam corresponds to $ \lambda $ and when the dataset is large elastic Net often outperforms the,... Running these cookies between L1 and L2 regularization and variable selection method L... Computational effort of a single OLS fit useful resources below if you know elastic Net.. \Lambda $ weights, improving the ability for our model tends to under-fit the set. And 1 passed to elastic Net combina le proprietà della regressione di Ridge e Lasso possibly on! This website uses cookies to improve your experience while you navigate through the theory and few... To balance the fit of the model from overfitting is regularization and square functions Ridge... Net 303 proposed for computing the entire elastic Net method are defined by overfitting... Imagine that we add another penalty to our cost/loss function, with one additional r.. Regression that adds regularization penalties to the Lasso, it combines both L1 and L2 with..., so we need to use sklearn elastic net regularization python ElasticNet and ElasticNetCV models to analyze data... Few hands-on examples of regularized regression in Python sparse model Conv1D, and...";s:7:"keyword";s:18:"top financial firm";s:5:"links";s:5106:"<a href="http://digiprint.coding.al/site/page.php?tag=41e064-raw-hemp-oil-endoca">Raw Hemp Oil Endoca</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-costco-marinated-salmon-cooking-directions">Costco Marinated Salmon Cooking Directions</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-john-16-bible-study-questions">John 16 Bible Study Questions</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-importance-of-promotion-mix">Importance Of Promotion Mix</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-dried-shrimp-where-to-buy">Dried Shrimp Where To Buy</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-papers-crossword-clue">Papers Crossword Clue</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-genetic-testing-essay">Genetic Testing Essay</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-weird-meaning-in-kannada">Weird Meaning In Kannada</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-ikoria-commander-decks-list">Ikoria Commander Decks List</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-anje-falkenrath-win-conditions">Anje Falkenrath Win Conditions</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-how-to-store-fennel-seeds">How To Store Fennel Seeds</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-sectional-sofa-under-%24600">Sectional Sofa Under $600</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-yamaha-atv-prices">Yamaha Atv Prices</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-sennelier-oil-pastels-individual">Sennelier Oil Pastels Individual</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-na2co3-%2B-h2o-%2B-co2">Na2co3 + H2o + Co2</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-how-to-maintain-cilantro-plant">How To Maintain Cilantro Plant</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-mojito-mocktail-pitcher">Mojito Mocktail Pitcher</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-galatians-4-niv">Galatians 4 Niv</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-how-to-get-rid-of-gnats-in-vegetable-garden">How To Get Rid Of Gnats In Vegetable Garden</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-john-15%3A14-commentary">John 15:14 Commentary</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-cocteau-twins---treasure-lyrics">Cocteau Twins - Treasure Lyrics</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-north-american-hunting-club-coin-value">North American Hunting Club Coin Value</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-ravi-pillai-kollam-contact-number">Ravi Pillai Kollam Contact Number</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-molson-canadian-beer-in-california">Molson Canadian Beer In California</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-1-timothy-3%3A11-kjv">1 Timothy 3:11 Kjv</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-leap-meaning-in-urdu">Leap Meaning In Urdu</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-mustard-seed-lesson-for-youth">Mustard Seed Lesson For Youth</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-number-7-lesson-plans-kindergarten">Number 7 Lesson Plans Kindergarten</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-eu4-map-mods">Eu4 Map Mods</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-mv-organics-rose-water-spray">Mv Organics Rose Water Spray</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-donkey-konga-4">Donkey Konga 4</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-things-to-do-near-showboat-atlantic-city">Things To Do Near Showboat Atlantic City</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-fruit-basket-season-1">Fruit Basket Season 1</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-best-oracle-cards">Best Oracle Cards</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-festivals-in-pa-this-weekend">Festivals In Pa This Weekend</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-cen-tech-infrared-thermometer-69465">Cen-tech Infrared Thermometer 69465</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-indicative-verb-examples">Indicative Verb Examples</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-amritsar-to-chandigarh-distance">Amritsar To Chandigarh Distance</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-social-issues-essay-in-english">Social Issues Essay In English</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-introduction-to-aesthetics-pdf">Introduction To Aesthetics Pdf</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-super-short-scale-bass">Super Short Scale Bass</a>,
<a href="http://digiprint.coding.al/site/page.php?tag=41e064-aldi-perfect-bar">Aldi Perfect Bar</a>,
";s:7:"expired";i:-1;}

Zerion Mini Shell 1.0