| TECH |
Engadget Japanese |
アップル、ついにBoot Campで高精度タッチパッド設定に対応。MSの導入から8年遅れ |
https://japanese.engadget.com/apple-bootcamp-precision-touchpad-090553673.html
|
bootcamp |
2021-06-13 09:05:53 |
| python |
Pythonタグが付けられた新着投稿 - Qiita |
[随時更新]コーディング対策～こんな時は、こうする～ |
https://qiita.com/LaO/items/a9731e5fbd8ca2e3d75c
|
随時更新コーディング対策こんな時は、こうするこの記事は筆者がコーディング対策で学習する際の備忘録これから、もしくは今絶賛コーディング対策中の方に向けてお助けとなる記事にしたいと思います。 |
2021-06-13 18:26:18 |
| js |
JavaScriptタグが付けられた新着投稿 - Qiita |
GitHubで「javascript web application youtube」で検索して出てきた59件のソフトを試してみる。 |
https://qiita.com/ShinsukeSutou/items/d76fc1b816d7e4952c68
|
また、このウェブアプリは、YoutubeDataAPIを使用して、特定のビデオのコメントを取得します。 |
2021-06-13 18:55:23 |
| Program |
[全てのタグ]の新着質問一覧｜teratail（テラテイル） |
アルゴリズム java サイコロの問題 |
https://teratail.com/questions/343804?rss=all
|
サイコロを任意の回数振って、偶数が出た場合にカウントを行うことを実現したいです。 |
2021-06-13 18:56:28 |
| Program |
[全てのタグ]の新着質問一覧｜teratail（テラテイル） |
GASからBasic認証をして、LIVEDOOR BLOGに記事をPOSTしたい |
https://teratail.com/questions/343803?rss=all
|
GASからBasic認証をして、LIVEDOORBLOGに記事をPOSTしたい前提・実現したいことGASからAtomPubnbspAPInbspにてBasic認証をして、LIVEDOORnbspBLOGに記事をPOSTしたいのですが、Basic認証をパスすることができません。 |
2021-06-13 18:51:41 |
| Program |
[全てのタグ]の新着質問一覧｜teratail（テラテイル） |
Python3でのモジュールインポート時エラーについて |
https://teratail.com/questions/343802?rss=all
|
Pythonでのモジュールインポート時エラーについて前提・実現したいことpythonを独学中の者です。 |
2021-06-13 18:45:40 |
| Program |
[全てのタグ]の新着質問一覧｜teratail（テラテイル） |
<!DOCTYPE html>で起こり得る不具合について解決したい |
https://teratail.com/questions/343801?rss=all
|
ltDOCTYPEhtmlgtで起こり得る不具合について解決したい前提・実現したいことdeviseを使ってuser管理をし、ログイン等を行えるようにしたい発生している問題・エラーメッセージdeviseを使用してuser管理する流れでログイン画面とサインイン画面のviewを作成し反映できたのですが、代わりに登録ボタンやログインボタンをクリックしても反応しなくなってしまいました。 |
2021-06-13 18:39:53 |
| Program |
[全てのタグ]の新着質問一覧｜teratail（テラテイル） |
速度を一定に保ちたい |
https://teratail.com/questions/343800?rss=all
|
速度を一定に保ちたいブロック崩しゲームを作っています。 |
2021-06-13 18:32:06 |
| Program |
[全てのタグ]の新着質問一覧｜teratail（テラテイル） |
Unityのprobuilderで、progridsに関係なく辺移動したい。 |
https://teratail.com/questions/343799?rss=all
|
オブジェクトの移動をするときはグリッドに関係なく移動できました。 |
2021-06-13 18:30:18 |
| Program |
[全てのタグ]の新着質問一覧｜teratail（テラテイル） |
N 行 M 列のデータの入力 python |
https://teratail.com/questions/343798?rss=all
|
N行M列のデータの入力pythonpythonでとある問題を解いているのですが、解き方がわかりません。 |
2021-06-13 18:22:55 |
| Program |
[全てのタグ]の新着質問一覧｜teratail（テラテイル） |
java 重複していない値のみをリストに残す |
https://teratail.com/questions/343797?rss=all
|
java重複していない値のみをリストに残す前提・実現したいことリストに入っている値で、重複していない値のみをリストに残したい。 |
2021-06-13 18:18:39 |
| Program |
[全てのタグ]の新着質問一覧｜teratail（テラテイル） |
sh: mix: command not found / npm ERR! |
https://teratail.com/questions/343796?rss=all
|
shmixcommandnotfoundnpmERR前提・実現したいことLaravelアプリにnbspVuenbspを反映させるため、npmnbsprunnbspdevを実行したいのですが、噛み合わないパッケージをインストールしたせいか、また、慌ててnbspnpmnbspのコマンドを実行しまくったせいか、どんな方法を試してみても上手くいきません。 |
2021-06-13 18:13:38 |
| Ruby |
Railsタグが付けられた新着投稿 - Qiita |
Herokuにデプロイ後にCSVで作成したデータをテーブルに登録したい Rails |
https://qiita.com/hedgehog-genki/items/7a09cced3a5e54fab327
|
これは要はHerokuにデプロイした後に、herokurunrakedbseedを実行して、インポートしようという算段です。 |
2021-06-13 18:24:09 |
| 海外TECH |
DEV Community |
Generative Adversarial Networks |
https://dev.to/geekquad/generative-adversarial-networks-430e
|
Generative Adversarial NetworksEver wondered how Mona Lisa would have looked in real life Or have you ever wanted to create new faces so well that most people can t distinguish the faces it generates from real photos How would you feel if I would say that you can predict future frames of a video Fascinated right Each of these is possible with the power of GANs Let s understand what these are and how they work Generative Adversarial Network or GANs are deep generative models These are a combination of two networks that are opposed against each other and are neural network architectures that are capable of generating new data So let us break down the word and understand them Generative means capable of production or reproduction Adversarial means two sides who oppose each other and Network means a system of interconnected things GANs are actually two different networks joined together and are composed of two halves GeneratorDiscriminatorBut before understanding what these two are let us know what is Loss Function Loss Function The loss function describes how far the results produced by our network are from the expected result how far an estimated value is from its true value Its objective isn t to make the model good but is to keep it from going wrong Loss Function gives us the direction of the optimal solution Generator The generator is the neural network architecture that takes in some input and reshapes it to get a recognizable structure that is close to the target The main aim of the generator is to make the output look as close as possible to the real data But to make this possible the generator network needs to be trained heavily Let s understand how Loss Function for Generator Network works We want to fool the Discriminator into believing that the output from the Generator is actually real So this term is going to be if we are successfully able to fool the Discriminator Hence we will have a log something close to zero So does the generator want to maximize or minimize the loss Generator wants to minimize this loss Discriminator The discriminator is a regular neural network architecture that does the classification job to categorize real data from the fake samples generated by the Generator Discriminator s training data comes from two sources Read data used for training Fake data generated by the Generator Let s understand how Loss Function Discriminator Network works Here Let s divide this equation into terms First term We take the log of D x i where x i real so we want our Discriminator to output here So if we look at log the output is going to be zero Second term log D G z generator is going to take in some random noise and it s gonna output something close to real close to reality and the discriminator is going to output either or and from discriminator s point of view we want the output to be zero here So does the Discriminator want to maximize or minimize the loss Discriminator wants to maximize this loss Combining both of these LHS of this expression means that we want to minimize w r t Generator and maximize w r t Discriminator for some value function V takes to input the Generator and the Discriminator D G In practice the generator is trained to instead to maximize the Generator MaxG because this new expression leads to non saturating gradients which makes it a lot easier for training In simple words the generative and discriminator models play a symmetric opponent game or a zero sum game with each other that is where one side s benefits come at the expense of the other In the end after a lot of training process the generator can make indistinguishable things from real ones and the Discriminator is forced to Guess Both the generator and the discriminator start from scratch without any prior knowledge and are simultaneously trained together Generative models can generate new examples from the sample that are not only similar to the class but real Wondering what are GANs used for GANs have seen major success in the past years They have a wider range of applications Usage of GANs is not limited to these Here are a few use cases Generating New Data Rather than augmenting the data new training data samples can be generated by GANs from the existing data Here is an example of Fashion MNIST samples generated by GANs Super Resolution GANs can be used to enhance the resolution of images and videos Here is an example of video super resolution done by tempoGAN Security GANs can be used for malware detection and intrusion detection GANs can be used in a variety of cybersecurity applications including enhancing existing attacks beyond what a standard detection system can handle Audio Generation GANs can be used to generate high quality audio instrumentals and voice samples MuseGAN and WaveGAN are two such GANS Healthcare GANs can be utilized to detect tumors By comparing photos with a library of datasets of healthy organs the neural network can be used to identify cancers By finding disparities between the patient s scans and photos and the dataset images the network can discover abnormalities in the patient s scans and photographs Using generative adversarial networks malignant tumors can be detected faster and more accurately References Original GAN Paper Ian GoodfellowGenerative Adversarial Network Google DevelopersPlease share your thoughts and comments if you found this post interesting and helpful Follow the links below to get in touch with me LinkedIn GitHub Twitter |
2021-06-13 09:32:46 |
| ニュース |
BBC News - Home |
G7 to agree tough measures on burning coal to tackle climate change |
https://www.bbc.co.uk/news/uk-politics-57456641
|
entire |
2021-06-13 09:58:07 |
| ニュース |
BBC News - Home |
Christian Eriksen: Danish midfielder remains stable in hospital and sends greetings to team-mates |
https://www.bbc.co.uk/sport/football/57458630
|
Christian Eriksen Danish midfielder remains stable in hospital and sends greetings to team matesChristian Eriksen has sent his greetings to his national team mates and remains stable in hospital say Danish football officials |
2021-06-13 09:55:45 |
| ニュース |
BBC News - Home |
In Pictures: World leaders bask in Cornwall sun at G7 summit |
https://www.bbc.co.uk/news/uk-57438878
|
occasional |
2021-06-13 09:20:05 |
| 北海道 |
北海道新聞 |
五輪聖火、道内上陸 ウポポイで点火セレモニー |
https://www.hokkaido-np.co.jp/article/555041/
|
東京五輪 |
2021-06-13 18:11:06 |
| 北海道 |
北海道新聞 |
パラ卓球、竹守が５度目Ｖ 男子複も制し２冠 |
https://www.hokkaido-np.co.jp/article/555055/
|
知的障害 |
2021-06-13 18:07:00 |
| 北海道 |
北海道新聞 |
Ｇ７で拉致提起「賛同得た」 加藤氏、首相の成果強調 |
https://www.hokkaido-np.co.jp/article/555054/
|
加藤勝信 |
2021-06-13 18:02:00 |
| 北海道 |
北海道新聞 |
慶大が３４年ぶり４度目Ｖ 全日本大学野球選手権 |
https://www.hokkaido-np.co.jp/article/555053/
|
大学野球 |
2021-06-13 18:02:00 |
| 北海道 |
北海道新聞 |
自転車の男性はねられ死亡 札幌・厚別区 |
https://www.hokkaido-np.co.jp/article/555048/
|
札幌市厚別区青葉町 |
2021-06-13 18:04:02 |
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