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newbiettn - Standing on the shoulders of giantsA great hideout, where I am personally delighted to discuss topics of interest mostly in Computer Science
http://www.ngoctran.me/
A great hideout, where I am personally delighted to discuss topics of interest mostly in Computer Science
http://www.ngoctran.me/
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newbiettn - Standing on the shoulders of giants | ngoctran.me Reviews
https://ngoctran.me
A great hideout, where I am personally delighted to discuss topics of interest mostly in Computer Science
ngoctran.me
Classification using Rough Set theory - newbiettn
http://ngoctran.me/2016/09/06/classification-using-roughset-theory
Standing on the shoulders of giants. September 06, 2016. Classification using Rough Set theory. Rough Set (RS) algorithm targets the vagueness. Of the knowledge, e.g., the boundary between observations is not strong enough to set them apart. Look at Figure 1, we can see that we have no clue how to correctly classify items $x 3$ and $x 4$ when the attribute values of those items are identical. Figure 1: it is impractical to classify x3 and x4. II Primary concepts of RS. Figure 2: Sample dataset. Values (i...
Unconstrained optimization methods - newbiettn
http://ngoctran.me/2016/11/23/uncontrained-optimization-methods
Standing on the shoulders of giants. November 23, 2016. 22 Minimization and maximization problem. In this post, we are going to review classical unconstrained optimization algorithms, including gradient descent, Newton’s and Gauss-Newton. Those three algorithms have been heavily used as optimization methods of different machine learning algorithms, such as linear regression, logistic regression, neural network, and so on. All R codes of experiments can be found here. In my opinion, Newton’s method (or Ne...
Prelude to feedforward neural networks (part 1) - newbiettn
http://ngoctran.me/2017/01/02/perceptron
Standing on the shoulders of giants. January 02, 2017. Prelude to feedforward neural networks (part 1). I From simple threshold neurons to perceptrons. I From simple threshold neurons to perceptrons. Fig 1 Illustration of a threshold neuron. Where $f( cdot)$ is given by. In contrast to threshold neurons, perceptrons are able to adjust their weights during learning. At the beginning, we initialise the weight vector $ mathbf{w}$ by a random weight value ${w {(0)} $, and the learning rate $ eta$ by any ...
HTML5 canvas - newbiettn
http://ngoctran.me/2015/11/05/183
Standing on the shoulders of giants. November 05, 2015. One of different things that makes. Interesting is we are able to imitate physical model. In this pen, I create infinite number of particles and put them on spiral movement. It reminds me of old equations about classical mechanics I learnt in the high school. See the Pen Spiral Particles. By newbiettn ( @newbiettn. See the Pen Foggy tree. By newbiettn ( @newbiettn.
Singleton pattern discussion - newbiettn
http://ngoctran.me/2015/02/05/singleton-pattern-discussion
Standing on the shoulders of giants. February 05, 2015. Pattern was used widely across in my projects. Back in time, I was confused when it came to an issue: if I should define. Method with supplied parameters. My $002 is definitely not. Since you intend to pass arguments to. Method, singleton is no longer itself. I think it would be much more like a factory by which you can instantiate many sorts of instances depending on passing arguments. Let’s get hands dirty by making ourselves a singleton class.
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Blog | Maximum Entropy | My notebook about mathematics and statistics
https://maikolsolis.wordpress.com/blog
Estadística Actuarial I (CA-303). II Ciclo, 2014. Estadística Actuarial II (CA-403). I Ciclo, 2015. Optimizing the binwidth for the histogram using cross-validation. April 26, 2014. Are the simplest and, sometimes, the most effective tool to describe quickly a dataset density. Suppose you have an independent and identically distributed random sample. From some unknown continuous distribution called. Recall in a past post. Are a uniform partition of size. Using a technique called. Remark that we can estim...
Maikol Solís | Maximum Entropy
https://maikolsolis.wordpress.com/author/maikolsolis
Estadística Actuarial I (CA-303). II Ciclo, 2014. Estadística Actuarial II (CA-403). I Ciclo, 2015. All posts by Maikol Solís. Optimizing the binwidth for the histogram using cross-validation. April 26, 2014. Are the simplest and, sometimes, the most effective tool to describe quickly a dataset density. Suppose you have an independent and identically distributed random sample. From some unknown continuous distribution called. Recall in a past post. Are a uniform partition of size. Using a technique called.
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Thứ Tư, ngày 06 tháng 11 năm 2013. Mì quảng tôm thịt. Thèm mì quảng bữa giờ, mà lại làm biếng đi mua mì quảng. Vì mỗi lần đi xa quá nên mình ngán, sực nhớ chị Mulan có công thức làm mì quảng, vậy là qua nhà chị nịnh nọt rồi xin xỏ nói chung đủ kiểu, chỉ để có cái công thức hehehe. Còn đây là cái công thức chị Mulan gửi cho mình, mình copy lại để sau này sử dụng. Liều lượng này làm được 2 tô nha, nếu thích nhiều tăng lên nha. 1 Cup bột gạo. 1/4 cup bột năng. 1/4 cup bột bắp. Khi ăn: bỏ rau vô trước, bỏ cọ...
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newbiettn - Standing on the shoulders of giants
Standing on the shoulders of giants. Prelude to feedforward neural networks (part 2). Prelude to feedforward neural networks (part 1). Multivariate imputation with mice package in R. Drawing neural nets using Tikz. Classification using Rough Set theory. Precision, Recall, Sensitivity and Specificity. Maximum Likelihood Estimation, Maximum a Posteriori Estimation and Naive Bayes (part 2). Maximum Likelihood Estimation, Maximum a Posteriori Estimation and Naive Bayes (part 1). To hold the sun.
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I started my job in 2002 as Graphic Designer. I have 11 years experience in Advertising, Interior,. Printing company and I’ve also worked for. Event and PR Agency. Graphic design: Corporate branding, Logo, brochure,. Catalogue, packaging, billboard, poster , POSM. Photography: Event, product, fashion, landscape, streetlife. Skype / ym / msn : ngocpainter. Email : ngocpainter@gmail.com. Cell phone : (84) 0903 4004 86. Teamworks Communications / Managing Director. KT Investement co.ltd., /Chairman.
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