My Github repo

In case anyone is interested:  https://github.com/nyck33

Best vegetarian multivitamin for IT professionals and CS students on a budget

Made in Canada, Webber Men's Most Complete Multi sold at Walmart regularly at 16.99 CAD on sale for 12.99 CAD right now. 

The capsules have green powder in them, you get 90 capsules, take one per day, it just seems wayyy better for me than anything else available at that price point. 

https://webbernaturals.com/en-ca/product/mens-most-complete-multi/

I swear to god that my coding skills in Python or any other language is way better on a vegetarian, vegan or no meat diet. 

Meat just interferes with my chain of thought. 

I finished search, basically maze solving, on AI for Robotics, Sebastien Thurn's course on Udacity and here's the .ipynb notebook:

https://github.com/nyck33/AI4RUdacity/blob/master/Lesson12Search.ipynb

I discovered a new usage in for loops where I decrement the counter in a DFS search where a particular branch leads to a dead end so it goes back to the previous level.

Next is A-star a heuristic algorithm that estimates the distance left to goal assuming there are no obstacles which helps in calculating an f = g +h where f is the term used to choose between nodes to expand, g is the distance travelled up to that point and h is the A-star estimate. 

計算写真学のプロフィール Computational Photography Portfolio

https://drive.google.com/open?id=1bb24v6MoOIJya77iWItHCREjO0IHNPyi

Solved MtnCarContinuous using Udacity project code as boilerplate

episode: 0 score: -41.59034754863541 mean: -41.59 std: 0.0
episode: 1 score: -75.87242108451743 mean: -58.73 std: 17.14
episode: 2 score: 32.01844640263133 mean: -28.48 std: 45.01
episode: 3 score: 132.904319261567 mean: 11.86 std: 80.02
episode: 4 score: 125.81198290946529 mean: 34.65 std: 84.85
episode: 5 score: 84.26163480413017 mean: 42.92 std: 79.63
episode: 6 score: 126.89684490110164 mean: 54.92 std: 79.37
episode: 7 score: 139.18190524840517 mean: 65.45 std: 79.3
episode: 8 score: 100.24481691450521 mean: 69.32 std: 75.56
episode: 9 score: 165.80286734425076 mean: 78.97 std: 77.31
episode: 10 score: 109.29507292352991 mean: 94.05 std: 66.24
episode: 11 score: 209.07900825070152 mean: 122.55 std: 44.84

Solved means getting over 90 for reward.  Scores are well over 90 since I used a modified reward function.  

Repo here:  https://github.com/nyck33/openai_my_implements/tree/master/continuous/mountaincar

食肉の生産と消費が地球を破壊しているがシリコンバレーでは人工肉を開発中

https://www.economist.com/technology-quarterly/2015/03/05/silicon-valley-gets-a-taste-for-food

DQN Cartppole-V1 にてTensorboardを使って学習をグラフ化しました

上記のグラフのLossの下りと同時にスコアは改善されてます：

episode: 0   score: 10.0   memory length: 11   epsilon: 1
episode: 5   score: 76.0   memory length: 154   epsilon: 1
episode: 10   score: 16.0   memory length: 278   epsilon: 1
episode: 15   score: 17.0   memory length: 364   epsilon: 1
episode: 20   score: 17.0   memory length: 491   epsilon: 1
episode: 25   score: 32.0   memory length: 596   epsilon: 1
episode: 30   score: 12.0   memory length: 657   epsilon: 1
episode: 35   score: 20.0   memory length: 742   epsilon: 1
episode: 40   score: 16.0   memory length: 846   epsilon: 1
episode: 45   score: 19.0   memory length: 954   epsilon: 1


self.epsilon 0.999
episode: 50   score: 19.0   memory length: 1126   epsilon: 0.8806777104745716
episode: 55   score: 9.0   memory length: 1238   epsilon: 0.7873207291459607
episode: 60   score: 17.0   memory length: 1389   epsilon: 0.6769247732130653
episode: 65   score: 14.0   memory length: 1545   epsilon: 0.5791040088995179
episode: 70   score: 65.0   memory length: 1821   epsilon: 0.4393709323780249
episode: 75   score: 60.0   memory length: 2000   epsilon: 0.3104958044435009
episode: 80   score: 119.0   memory length: 2000   epsilon: 0.20622457658762192
episode: 85   score: 201.0   memory length: 2000   epsilon: 0.07402874109670564
episode: 90   score: 239.0   memory length: 2000   epsilon: 0.02485123742451863
episode: 95   score: 284.0   memory length: 2000   epsilon: 0.009998671593271896
episode: 100   score: 202.0   memory length: 2000   epsilon: 0.009998671593271896
episode: 105   score: 292.0   memory length: 2000   epsilon: 0.009998671593271896

Tensorboardの使い方はこちらが分かりやすいです：https://stackoverflow.com/questions/42112260/how-do-i-use-the-tensorboard-callback-of-keras

以上でした。

マレーシアのBPOにてフロントエンドエンジニアを募集

マレーシアの首都KLのBPO、Teledirect社がフロントエンドエンジニアを募集中です。
Google Tag Manager, Google Ads, Google Merchant Centerの専門知識が得られます。
JavascriptにてTriggerをHTML内に書き込み（EventListner）、Eventが発生した際いにDataLayer内のVariableにデータを移してそこからGTM Container経由にてGoogle Analytics等に情報を送信します。面接の前に少し勉強する必要性がありますが、主にJavascript及びHTMLのスキルが評価されます。

給料は10000リンギット（267,227.87 円）から14000リンギット（374,119.02 円）ですが、物価が大体日本の3分の1（電化製品などの輸入品以外）なので悪くないと思います。

興味のある方はメールにてご連絡お願いします。

NyckimstrongandfreeアットマークGmail.com