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Δ ℚuantitative √ourney∑ My experiences in learning quantitative applications
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Brandon Brown
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Brandon Brown
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Ranch●●●●●monga , California, 91701
United States
View this contact
Brandon Brown
6387 S●●●●●●re St.
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United States
View this contact
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∑ My experiences in learning quantitative applications
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Simple Genetic Algorithm In 15 Lines Of Python – Δ ℚuantitative √ourney – ∑ My experiences in learning quantitative applications
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My experiences in learning quantitative applications. Simple Genetic Algorithm In 15 Lines Of Python. Mini GA and NN. Genetic Algorithm in 15 lines of Python code. A simple yet effective genetic algorithm implementation used to train a neural network in 15 lines of code. This is a spinoff of a really great tutorial called "A Neural Network in 11 lines of Python" found here: http:/ iamtrask.github.io/2015/07/12/basic-python-network/. For a neural net (e.g. the net architecture). Just Give Me The Code:.
Simple(r) RNN in Julia – Δ ℚuantitative √ourney – ∑ My experiences in learning quantitative applications
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My experiences in learning quantitative applications. Simple(r) RNN in Julia. I assume you know how basic feedforward neural nets work and can implement and train at least a 2-layer neural net using backpropagation and gradient descent. I'm using the same ultra-simple problem found in this article: http:/ iamtrask.github.io/2015/07/12/basic-python-network/. So I encourage you to go through that first if you need some more background. Summary and Motivations ¶. Additionally, I wanted to try out Julia.
Gradient Descent with Backpropagation – Δ ℚuantitative √ourney – ∑ My experiences in learning quantitative applications
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My experiences in learning quantitative applications. Gradient Descent with Backpropagation. Summary and Motivations ¶. But I did not get backpropagation. I understood the general principle, but I didn't really get. Let's begin with Gradient Descent ¶. Let's imagine we have a function $f(x) = x 4 - 3x 3 2$ and we want to find the minimum of this function using gradient descent. Here's a graph of that function:. Plotting f(x) = x 4 - 3x 3 2, showing -2 x 4. Let's imagine I pick a random x value, say x = 4.
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My experiences in learning quantitative applications.
Simple Recurrent Neural Network – Δ ℚuantitative √ourney – ∑ My experiences in learning quantitative applications
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My experiences in learning quantitative applications. Simple Recurrent Neural Network. I originally submitted an RNN post but realized I made some major mistakes (I'm learning as I go). This is almost a complete re-do. One of the issues was that the RNN was not training properly, and I have not been able to get it to reliably train with my own implementation of gradient descent, so here I will calculate the gradients and hand those off to a scipy optimizer to find the weights. In an RNN, we're going to a...
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Δ ℚuantitative √ourney
Our experiences in learning quantitative applications. Apr 26, 2017. Some announcements about this blog's migration to a different static site generator as well as some new GitHub repos I've created. By Brandon Brown · Announcement. Click to read and post comments. Feb 26, 2017. Persistent Homology (Part 5). In part 5 we combine everything we've learned and compute persistent homology barcodes from raw data. By Brandon Brown · Topological Data Analysis. Click to read and post comments. Feb 23, 2017.
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時間の使い方が、下手らしく、時間が無い・・・(＠ ＠ ). 腕が、最低・・・. 浮く・・・. 浮く・・・. 超苦しめられました・・・. 今年中に、もう１機くらいは、完成したいものですね /. まったく、うまく行ってませんでした(；一 一). 12300;岳を、読め」. こちらから、終わらせようと、思ふ・・・. メサイア・・・. この調子で、また頑張ろうと思ふ・・・. 車の整備士をしてまして・・・. 連チャンモードに入るらしく←もっといい連チャンならいいのだが(；一 一). やっと一息と言う感じです(・。・. 小説版 逆襲のシャア ベルトーチカ チルドレン. 気がつくと、折れてた(;O;). また、折られるのが見えてるので・・・. でも、牛歩状態・・・. 自分は・・・. 友人の、岡山男さんのところへいていましたが・・・. ここも、超～田舎でしたね～、びっくりです＼(◎o◎)／！ 自分の、ケシュアは、小窓が１個？ 風通しは良くないですが、値段的には、仕方ないでしょうね。 次回は、朝出発で、時間を合したいですね～. 激ヤバの、プラです(・。・. 違うか・・・. 頑張ります(＠ ＠ ).
