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522d3898-0e55-4690-aac5-4e1c02a58fe9 | 5b0a1a1b-1040-4699-a0cb-4a3ec42520b2 | 4c43e234-0e9c-4f0d-8780-4dd3803602c6 | 2023-02-28T15:02:06.143731+00:00 | Leres! Duanana panjangna jeung nempatkeun sababaraha variasi deui. | prompter | sun_Latn | 3 | true | false | null | false | null | {
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579219f8-26a8-4633-9774-3fff9ce53873 | null | 1b055173-7560-45f9-bbac-86c6f7b7e3fd | 2023-02-07T03:54:16.822693+00:00 | Mangga nyarankeun tilu aplikasi mangpaat nu bisa dihontal ngagunakeun autohotkey jeung irfanview. | prompter | sun_Latn | 3 | true | false | null | false | null | {
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611ef05e-4d61-42dc-88a1-7d8ba55cb64c | 579219f8-26a8-4633-9774-3fff9ce53873 | 1aaa9be1-0d07-4cb2-a2d7-4301cbfc4ecc | 2023-02-16T23:27:55.921885+00:00 | AutoHotkey mangrupakeun basa skrip bébas jeung open-source nu ngidinan pamaké pikeun ngajadikeun otomatis tugas repetitif gambar ngedit dina duanana IrfanView. Contona, Anjeun bisa nulis skrip nu ngarankeun grup gambar, ngarobah ka format husus, sarta nerapkeun set filter ka unggal gambar. Ieu bisa nyimpen loba waktu pikeun fotografer, desainer grafis, sarta professional séjén anu gawé bareng jeung jumlah badag gambar. 2. Screen capture jeung éditingScreamHotkey: Ieu bisa dipaké pikeun nyieun gambar gambar jeung gambar gambar otomatis. Ieu bisa dipaké pikeun nyieun gambar jeung gambar gambar otomatis, nyieun gambar jeung gambar. Ieu bisa dipaké pikeun nyieun sababaraha cara pikeun nyieun gambar jeung gambar. Ieu bisa dipaké pikeun nyieun gambar jeung gambar. Ieu bisa dipaké pikeun nyieun gambar jeung gambar. Ieu bisa dipaké pikeun nyieun gambar jeung gambar, utamana pikeun nyieun gambar jeung gambar. Ieu bisa dipaké pikeun nyieun gambar jeung gambar. Ieu bisa dipaké pikeun nyieun gambar | assistant | sun_Latn | 3 | true | false | 0 | false | null | {
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1db16d5a-deb8-4ecb-9277-78dc68d475c1 | 611ef05e-4d61-42dc-88a1-7d8ba55cb64c | 2f98ab23-d103-4670-ab44-afa6f3ad51da | 2023-02-17T10:59:20.330606+00:00 | Dupi anjeun masihan kuring conto husus kumaha carana ngalakukeun salah sahiji di luhur? | prompter | sun_Latn | 3 | true | false | null | false | null | {
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da729b86-88e2-4af5-b07e-b94fc820b103 | 1db16d5a-deb8-4ecb-9277-78dc68d475c1 | abf4b2aa-80ec-497f-82ba-77eaf1851253 | 2023-02-17T22:13:19.672813+00:00 | Pasti! Ieu conto kumaha ngagunakeun AutoHotkey jeung IrfanView pikeun ngedit gambar batch: Hayu urang nyebutkeun anjeun boga folder kalawan saratus gambar nu kudu ngarobah ukuran, nambahkeun wates, sarta nyimpen salaku format file béda. Anjeun bisa make AutoHotkey pikeun ngajadikeun otomatis prosés ieu dina IrfanView kalawan ngan sababaraha garis kode: sql Code: Copy; Setelan jalur ka folder nu ngandung gambar ImagePathview= "C:\Images"; Setelan format file sasaran jeung kualitas FormatBox= "JPG" := 80; Setelan gambar sasaran jeung ukuran wates: 800 Height= 600 Border: :Smile: 10 Looped ngaliwatan unggal file dina folder jeung editan Looped, %ImagePathFile: %ImageFile: %ImageFile: %Image: %Image: %Image: %Image: %Image | assistant | sun_Latn | 3 | true | false | 0 | false | null | {
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099d14da-6f70-4fc5-bf72-a04224a8eaf0 | 1db16d5a-deb8-4ecb-9277-78dc68d475c1 | 2d40e94c-40ed-4e00-a66e-454c0401c8ad | 2023-02-18T01:13:59.006624+00:00 | Pasti! Ieu conto kumaha carana nyieun skrip basajan dina AutoHotkey pikeun ngajadikeun otomatis ngédit gambar batch kalawan IrfanView: Buka Notepad atawa editor téks biasa séjén. Salin jeung lengket kode di handap ieu kana editor: #NoEnv SendMode SetWorkingDiriew %A_ScriptDir% ; ngartikeun polder input jeung output InputFolder = C:\input\folder OutputFolder = C:\output\output\folder ; Setékeun parameter garis paréntah IrfanView pikeun ngédit jeung ngafilter batchViewPath = C:\Program Files\IrfanView\i_view64.exe ConversionParam = /convertFam /Fam /Fam /Fam /Fam /Fam /Fam /Fam /Fam /Fam /F | assistant | sun_Latn | 3 | true | false | 1 | false | null | {
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523fb426-759c-4bd2-82a4-922659910e95 | 579219f8-26a8-4633-9774-3fff9ce53873 | 2d40e94c-40ed-4e00-a66e-454c0401c8ad | 2023-02-17T02:14:13.342293+00:00 | AutoHotkey nyaéta basa skrip anu ngamungkinkeun anjeun pikeun ngajadikeun otomatis tugas anu berulang sareng ngaropea pangalaman Windows anjeun. IrfanView mangrupikeun pamandangan gambar sareng éditor anu nawiskeun sababaraha fitur pikeun ngawasa sareng ngatur gambar. Ieu tilu aplikasi anu gunana anu tiasa kahontal nganggo AutoHotkey sareng IrfanView: Pangolahan gambar batch: AutoHotkey tiasa dianggo pikeun ngajadikeun otomatis prosés muka sareng ngawasa sababaraha gambar di IrfanView. Salaku conto, anjeun tiasa nyerat skrip anu muka folder gambar sareng ngarobih ukuran, hasilna, atanapi nerapkeun saringan pikeun unggal gambar dina folder, sadayana ku sababaraha pencét tombol. Manipulasi gambar dumasar kana panas: AutoHotKey tiasa dianggo pikeun nyetél konci kustomisasi pikeun fungsi HotFanView anu sering dianggo, sapertos muterkeun, crop, atanapi ngédit ukuran. Prosés ieu tiasa ngadamel s | assistant | sun_Latn | 3 | true | false | 1 | false | null | {
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c699b8df-d30c-4c09-8c08-642a7575d6fd | 523fb426-759c-4bd2-82a4-922659910e95 | cd66a8cb-e5b8-4c6c-98e2-ad887284d201 | 2023-02-17T22:43:57.469992+00:00 | Tulis hiji skrip autohotkey nu ngarobah sakabéh gambar dina folder ayeuna kana hiji slideshow. | prompter | sun_Latn | 3 | true | false | null | false | null | {
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5b262f1a-d927-4749-b10e-a2228dbd9aca | c699b8df-d30c-4c09-8c08-642a7575d6fd | 9efde4c4-41c5-46b2-9c54-27fa73c5ae6b | 2023-02-18T00:58:49.247423+00:00 | Pasti! Ieu skrip AutoHotkey anu bakal nembongkeun sadaya gambar dina folder ayeuna salaku slideshow. Ieu ngagunakeun Windows Photo Viewer pikeun nembongkeun gambar. ```autohotkey #NoEnv #SingleInstance, Force Loop, *.jpg; Ganti.jpg ku éksténsi file gambar anjeun { ImageList.= A_LoopFileName. "`n" } Gui, Add, Add,View, r10 w600 h400, %ImageList% Gui, Add, Button, x5 y410 w120 h30 gStartSlideshow, Start Slideshow Gui, Show, w610 h450, Slideshow return StartSlideshow: Gui, Submit, ParmitSlideList, `nl, Rundnl {Gw2} Gv2 } Cv3System\S | assistant | sun_Latn | 3 | true | false | null | false | null | {
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827e8ba2-35f8-444c-9dfa-ae595365604f | 579219f8-26a8-4633-9774-3fff9ce53873 | 17e57f40-b5f0-47a5-a5ca-75e73e4850aa | 2023-02-16T23:47:49.878077+00:00 | AutoHotkey mangrupakeun basa skrip pikeun otomatisasi tugas dina Windows, sedengkeun IrfanView mangrupa sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora sora | assistant | sun_Latn | 3 | true | false | 2 | false | null | {
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cc9020bb-f494-48fe-a6de-3a6d4ed1456a | 1ddca93e-7ccd-419d-8ef8-ce7271e758f6 | 1edc66f0-57e3-49e5-93d1-d39a1884918f | 2023-03-12T21:58:35.148090+00:00 | Bisa Anjeun mangga masihan kuring sababaraha conto istilah ékonomi séjén kawas nu kuring disadiakeun ka anjeun saméméhna? | prompter | sun_Latn | 3 | true | false | null | false | null | {
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a9d03da6-4da1-4f93-9ed2-526599709acd | cc9020bb-f494-48fe-a6de-3a6d4ed1456a | 2d40e94c-40ed-4e00-a66e-454c0401c8ad | 2023-03-14T06:30:19.332864+00:00 | Tangtu, di dieu aya sababaraha istilah jeung conto ékonomi: 1. Diskriminasi harga: Lamun hiji pausahaan milih méakkeun harga béda ka konsumén béda pikeun produk atawa jasa anyar dumasar kana karep maranéhna mayar. Contona, maskapai mindeng ngecas harga béda pikeun korsi sarua gumantung kana faktor kawas waktu poé, poé minggu, jeung sabaraha jauh sateuacanna tiket ieu dibeuli. 2. Biaya kasempetan: Biaya milih hiji pilihan leuwih ti sejen, nu mangrupa nilai alternatif pangalusna nu teu dipilih. Contona, lamun anjeun milih méakkeun duit pikeun telepon anyar, biaya kasempetan nu bisa Anjeun meuli jeung duit nu, kawas laptop anyar atawa pakansi. 3. Monopolybeurat: Lamun hiji pausahaan tunggal dominan pasar sarta boga kakuatan pikeun ngeset harga jeung suplai. Contona, listrik di hiji kota leutik bisa jadi hiji bédana ékonomi alatan nu teu aya harga produk séjén anu dipilih. Ieu bisa ngabalukarkeun hiji éféktifitas atawa | assistant | sun_Latn | 3 | true | false | 1 | false | null | {
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6371394f-0f6f-4fb4-a327-c6503d1210ff | null | 9518485c-4d00-44d6-b4e7-c76c20bb9ca1 | 2023-02-07T15:17:02.388772+00:00 | Sabaraha téhna anu diinum ku jalma rata-rata unggal taun? Kuring pikir yén tilu nepi ka lima cangkir tea sapopoé téh séhat, tapi kuring teu yakin. Kuring geus nembe mimiti nginum lima nepi ka tujuh cangkir téh, sarta ngarasa mual jeung bengkak. Naha kuring kudu ka dokter, atawa ngan saukur ngurangan téhna? | prompter | sun_Latn | 3 | true | false | null | false | null | {
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01f7abf2-53c8-4ea5-85b3-34f5bb6b21a6 | 946d76db-159f-46b9-94ed-c4916172639e | 86b33e88-895b-4179-8e72-629adc7e3ba4 | 2023-02-08T21:15:13.949678+00:00 | Kumaha jumlah téh nu diinum unggal poé ku unggal jalma béda-béda di unggal buana utama di dunya? | prompter | sun_Latn | 3 | true | false | null | false | null | {
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7fbc4899-ca30-41d4-a9c9-0d598dfcf5a6 | 01f7abf2-53c8-4ea5-85b3-34f5bb6b21a6 | 0a041bc2-b30a-49a6-8c5c-bca62f92d74a | 2023-02-09T01:44:32.492032+00:00 | Turki resep tea paling, salaku juara dunya anu ngawasa aranjeunna nginum rata-rata 4 gelas sapoé. Ieu langkung ti Irlandia anu resep cangkir teh bahkan langkung ti Inggris. Di tungtung sanés skala urang Méksiko bahkan henteu terang naon bentukna teapot, sareng ampir sadayana di kidul aranjeunna resep kopi. Kahiji tea nyaéta budaya sareng sajarah. Nagara anu ngagaduhan karajaan di Asia atanapi anu aya hubunganana sareng Asia umumna resep tea, sareng Amérika Kidul condong resep kopi. Dominasi budaya AS ogé ngagaduhan pangaruh anu lumayan, pangaruh média parantos ningali parobahan laun tina tea kana kopi. | assistant | sun_Latn | 3 | true | false | null | false | null | {
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ac715a4a-9fdc-49e7-a1ff-91796154509a | 882b85e6-811b-4452-b200-91fc738266d0 | b4374137-7b26-4bea-94d0-c195d4b4a05a | 2023-02-07T06:53:40.943108+00:00 | Naon fungsi RNA dina konteks ieu? | prompter | sun_Latn | 3 | true | false | null | false | null | {
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dcc8eb8c-f182-4288-8fbb-f93da6e6123b | ac715a4a-9fdc-49e7-a1ff-91796154509a | 2c96e467-66f0-4be7-9693-bda51356a424 | 2023-02-07T15:23:02.021726+00:00 | Pasti, di dieu aya penjelasan tina Detrended Fluctuation Analysis (DFA) sareng RNA dina istilah saderhana: Detrended Fluctuation Analysis (DFA): DFA mangrupikeun metode anu dianggo pikeun nganalisis fluktuasi sareng mendeteksi pola dina data anu sigana acak. Éta dianggo ku ngahijikeun data heula pikeun ngungkabkeun tren anu mendasar. Tren teras dihapus (ieu disebat "detrending"). Fluktuasi data anu dikurangan teras dianalisis pikeun ngitung éksponén skaling. Éksponén skaling caket ka 0.5 nunjukkeun acak anu henteu patali, sedengkeun éksponén skaling caket ka 1 nunjukkeun korélasi jangka panjang anu teras-terasan. DFA sering dianggo pikeun nganalisis séks DNA, variasi denyut jantung, sareng sajabana pikeun ngadeteksi séks RNA sareng kerangka. RNA: RNA: RNA pikeun Ribonucleic. Éta ngandung | assistant | sun_Latn | 3 | true | false | 0 | false | null | {
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0519e75e-bd38-4652-86a0-24518440e1fa | ac715a4a-9fdc-49e7-a1ff-91796154509a | 89aec957-f5be-46e6-a159-17ef316a471e | 2023-02-07T16:09:20.281349+00:00 | Pikeun ngajelaskeun, analisis fluktuasi anu diganggu nyaéta metode dina domain anu tangtu, sapertos prosés stokastik sareng analisis séri waktos, pikeun nganalisis sinyal sareng nangtoskeun kaitanana sacara statistik. Naha anjeun hoyong terang langkung seueur? | assistant | sun_Latn | 3 | true | false | 1 | false | null | {
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8d18a0a1-1f38-44dd-adc2-5e67aa008195 | ac715a4a-9fdc-49e7-a1ff-91796154509a | 3270b8da-8f86-4590-8729-8b8d464d5ab9 | 2023-02-07T16:01:11.841001+00:00 | Saenyana roman | assistant | sun_Latn | 3 | false | false | null | false | null | {
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8a7b34b1-d1e6-4ec9-98d9-392323437247 | null | b4168ef8-6d5e-4f7c-9d36-6eb9928ad118 | 2023-02-07T04:44:41.430652+00:00 | Tulis abstrak tina kertas bodas learning mesin "Jaringan Generative Adversarial". | prompter | sun_Latn | 3 | true | false | null | false | null | {
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0507040a-1503-40b7-aa50-76819a4d64e1 | 8a7b34b1-d1e6-4ec9-98d9-392323437247 | b034affa-fe49-44a2-8d01-36236badf7ce | 2023-02-07T08:52:57.849405+00:00 | Hiji abstrak pikeun kertas generik ngeunaan Generative Adversarial Networks bisa jadi kieu: Generative Adversarial Networks (GANs) mangrupakeun kerangka pembelajaran jero nu ngalibetkeun latihan dua jaringan saraf, hiji generator, jeung hiji discriminator, dina cara game-teori. generator ngahasilkeun conto anyar, sedengkeun discriminator ngaevaluasi authenticity conto ieu, nyoba pikeun ngabédakeun antara data nyata jeung palsu. Dua model anu dilatih paralel, jeung generator nyoba ngahasilkeun conto nu bisa ngabohong discriminator jeung discriminator nyoba ngaidentipikasi conto nyata ti nu dihasilkeun bener. ngaliwatan latihan ieu tantangan antagonis, GANs bisa diajar pikeun ngahasilkeun conto konseptif ti data set husus pisan jeung geus hasil dilarapkeun dina rupa-rupa domain, kaasup gambar sumed, augmentation data, sarta paménta kertas bisa nyadiakeun hiji gambaran ngeunaan proyék GAN sarta téhnik utama sarta bisa nyadiakeun informasi tambahan dina widang | assistant | sun_Latn | 3 | true | false | 0 | false | null | {
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1b3df7e5-4760-40df-bb11-0942fe6ae46f | 0507040a-1503-40b7-aa50-76819a4d64e1 | aa148c79-06e8-47a8-8579-adbd5b487ad2 | 2023-02-07T12:24:18.815049+00:00 | mangga nulis deui makalah ieu jeung fokus kana dampak sosiologis ieu "GANs" | prompter | sun_Latn | 3 | true | false | null | false | null | {
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b285f490-90a4-4a11-857d-9594d54d9ad2 | 1b3df7e5-4760-40df-bb11-0942fe6ae46f | 02fd196a-c0c3-4bb7-ba95-35106c526779 | 2023-02-07T17:11:44.480818+00:00 | Dina ieu makalah, urang difokuskeun dampak sosiologis tina GANs sarta nyadiakeun gambaran ngeunaan sababaraha tantangan konci jeung masalah nu patali jeung pamakéan sistem ieu. Khususna, urang nalungtik cara nu GANs bisa dipaké pikeun nyieun eusi digital realistis nu bisa jadi hésé dibédakeun ti kanyataanana. Ieu boga potensi pikeun blur garis antara informasi nyata jeung palsu, ngakibatkeun tantangan anyar pikeun perlindungan intelektual harta sarta auténtikasi eusi digital. Salaku tambahan, urang nyadiakeun gambaran ngeunaan GANs jeung masalah konci jeung masalah nu patali jeung pamakéan sistem ieu. | assistant | sun_Latn | 3 | true | false | 0 | false | null | {
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8eb3fa7d-0163-4e51-a716-e8330954ec95 | 1b3df7e5-4760-40df-bb11-0942fe6ae46f | e0cefb0b-bb3f-435c-aecc-6408f5b1f1df | 2023-02-07T17:38:13.181507+00:00 | Jaringan kontradiksi generatif (GANs) parantos ngabalikeun deui widang pembelajaran mesin ku ngamungkinkeun generasi data anyar dumasar kana pola anu diajar tina data anu aya. Pangaruh GANs ngaleuwihan bidang élmu komputer sareng ngagaduhan implikasi anu signifikan pikeun masarakat sacara umum. Makalah bodas ieu museurkeun kana pangaruh sosiologis GANs, ngajalajah kumaha aranjeunna tiasa dianggo pikeun ngungkulan sababaraha tantangan dina bidang sapertos ubar, seni, sareng pendidikan. Salah sahiji kaunggulan utama GANs nyaéta kamampuanana pikeun ngahasilkeun data sintétik anu ampir teu tiasa dibédakeun tina data nyata. Ieu ngagaduhan kamampuan pikeun ngabalikeun cara data médis dikumpulkeun, dianalisis, sareng dianggo pikeun nyayogikeun lingkungan anyar. Ku ngahasilkeun data sintétik, GANs tiasa ngabantosan ngungkulan masalah privasi sareng data anu lumangsung, ngamungkinkeun panalungtik pikeun ngahasilkeun data anu langkung ageung sareng ngahasilkeun hasil | assistant | sun_Latn | 3 | true | false | 1 | false | null | {
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1eef7049-6579-4c35-ae98-f3ec4503c439 | 8a7b34b1-d1e6-4ec9-98d9-392323437247 | 0973415f-13fd-4eb9-b0ac-6221928ebe56 | 2023-02-07T09:07:10.964340+00:00 | Aranjeunna ngusulkeun kerangka anyar pikeun estimasi modél generatif ngalangkungan prosés kontradiksi, dimana urang ngalatih dua modél sakaligus: modél generatif G anu nyekel distribusi data, sareng modél diskriminatif D anu ngira-ngira kamungkinan sampel asalna tina data latihan tibatan G. Prosedur latihan pikeun G nyaéta pikeun maksimalkeun kamungkinan D ngalakukeun kasalahan. Kerangka ieu saluyu sareng gim dua pamaén minimum. Dina ruang fungsi sewenang-wenang G sareng D, aya solusi unik, kalayan G pulih distribusi data latihan sareng D sami sareng 1/2 dimana waé. Dina kasus dimana G sareng D ditetepkeun ku perseptron multilayer, sadayana sistem tiasa dilatih ku backpropagasi. Teu aya kabutuhan pikeun ranté Markov atanapi inferensi perkiraan anu teu diunggahkeun salami generasi latihan atanapi jaringan demonstratif. | assistant | sun_Latn | 3 | true | false | 2 | false | null | {
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b3ffb9e6-cc85-4eb6-81b2-c073e78a56ae | 1eef7049-6579-4c35-ae98-f3ec4503c439 | acdc6c14-8765-4154-b986-22f4bf6d13ca | 2023-02-07T15:47:46.355143+00:00 | Éta sigana aneh pikeun ngamimitian abstrak jeung "Maranéhanana". Mangga ningkatkeun kualitas jadi eta cocog jeung standar konferensi luhur. | prompter | sun_Latn | 3 | true | false | null | false | null | {
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1b7464ab-c35e-46ed-b1ac-75cbcd8eed1c | b3ffb9e6-cc85-4eb6-81b2-c073e78a56ae | 3acc3971-5bf4-4829-ae3d-b2fd55a320d2 | 2023-02-07T19:17:03.604761+00:00 | Di dieu aya jawaban anu ningkat: "Jaringan Adversarial Generative" mangrupikeun makalah pembelajaran mesin seminal anu ngenalkeun pendekatan anyar pikeun modél génératif. Makalahna nampilkeun arsitéktur modél anyar anu dikenal salaku Jaringan Adversarial Generative (GANs), anu diwangun ku dua jaringan saraf: generator sareng discriminator. Jaringan generator dilatih pikeun ngahasilkeun sampel data anu sami sareng data nyata, sedengkeun jaringan discriminator dilatih pikeun ngabédakeun antara data anu dihasilkeun sareng data nyata. Dua jaringan dilatih babarengan ku cara anu kontras, dimana generator nyobian ngahasilkeun sampel anu diskriminator henteu tiasa dibédakeun tina data nyata, sareng discriminator nyobian ngabédakeun akurat antara kertas nyata sareng data anu dihasilkeun. Jaringan generator nunjukkeun efektivitas GAN dina sababaraha jinis tugas, nunjukkeun, sareng nunjukkeun yén aranjeunna tiasa ngahasilkeun karya indilingual anu luhur kalebet gambar anu parantos ngagaduhan | assistant | sun_Latn | 3 | true | false | 0 | false | null | {
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3db81f79-ab08-4b67-a646-5ccb526b88b4 | b3ffb9e6-cc85-4eb6-81b2-c073e78a56ae | 95f63ab3-6c7b-43e1-8e86-fccd8be1465f | 2023-02-07T16:52:36.592166+00:00 | Dina widang pembelajaran mesin, kerangka anyar pikeun estimasi modél generatif diusulkan. Kerangka ieu, disebut "Generative Adversarial Net" (GANs), ngagunakeun prosés kontradiksi pikeun ngalatih dua modél: modél generatif (G) anu néwak distribusi data, sareng modél diskriminatif (D) anu ngira-ngira kamungkinan sampel tina data latihan tibatan G. Prosedur latihan pikeun G ngalibatkeun maksimizing kamungkinan D nyieun kasalahan. Kaulinan minimal dua pamaén anu hasilna ngagaduhan solusi unik dimana G ngadeukeutan distribusi data latihan sareng D sami sareng 1/2 di mana waé. Kerangka GAN tiasa dilaksanakeun nganggo perseptron multilayer sareng dilatih kalayan backpropagasi, tanpa kedah rantai Markov atanapi jaringan inferensi anu teu diunggalkeun. Evaluasi sampel generasi GAN nunjukkeun poténsi kerangka GAN anu diusulkeun nunjukkeun poténsi | assistant | sun_Latn | 3 | true | false | 1 | false | null | {
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acc28a07-edd7-4fa3-8035-c2c00bc16dfd | b3ffb9e6-cc85-4eb6-81b2-c073e78a56ae | 2c96e467-66f0-4be7-9693-bda51356a424 | 2023-02-07T16:33:17.694129+00:00 | Urang nyarankeun kerangka jaringan adversarial generative (GAN) pikeun estimasi modél generative. GANs ngalatih dua modél sakaligus: generator G anu néwak distribusi data, sareng discriminator D anu estimasi kamungkinan sampel asalna tina data latihan tibatan G. Generator dilatih pikeun maksimalkeun kamungkinan D nyieun kasalahan. Kerangka ieu dirumuskeun salaku gim minimal dua pamaén. Dina fungsi kontinyu G sareng D, kasaimbangan Nash unik aya dimana G pulih distribusi data latihan sareng D henteu tiasa ngabédakeun sampel nyata vs palsu. Nalika G sareng D mangrupikeun perseptron multilayer, latihan tiasa dilakukeun ku backpropagation. Teu aya ranté Markov atanapi jaringan inferensi anu teu digulung anu diperyogikeun nalika latihan atanapi generasi. Ékspérimén nunjukkeun poténsi GANs ngalangkungan evaluasi kuantitatif sareng générasi sampel anu dievaluasi. | assistant | sun_Latn | 3 | true | false | 2 | false | null | {
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6f709ca0-45b1-4a78-b673-7867015e48cf | acc28a07-edd7-4fa3-8035-c2c00bc16dfd | 9afdd5a5-fa93-4489-b9af-63634f9ed126 | 2023-02-07T20:25:03.516360+00:00 | Jawabanna téh panjang teuing, bisa nulis hiji tl;dr eta? | prompter | sun_Latn | 3 | true | false | null | false | null | {
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befcb0e3-df10-4feb-bf4b-4d6d1bff5067 | c8975f3e-90b4-4e78-b430-95668ceafeb0 | 6201b247-6d61-4cf0-9317-4a7018b40e5e | 2023-04-09T07:30:07.130838+00:00 | Naha intelijen jieunan anu ngahasilkeun téh mangrupa subset tina AI? | prompter | sun_Latn | 3 | true | false | null | false | null | {
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dec954aa-1c7b-41c2-8053-f4dde6857981 | fff310be-e26d-4ee7-b2bb-5338d0f28183 | 99d788e6-7eb4-42c7-9dda-4cb8301dabf7 | 2023-03-06T05:24:57.613026+00:00 | Upami anjeun milarian nyiapkeun lapangan pikeun hoki lapangan di bumi anjeun, ieu sababaraha léngkah anu tiasa anjeun laksanakeun pikeun mastikeun pertandingan anu aman sareng adil. Upami anjeun milih nganggo rumput, pastikeun yén éta dijaga sareng disiramkeun leres pikeun nyayogikeun permukaan maén anu lancar sareng bahkan. 3. Pasang garis wates sareng tujuan: Anggo garis wates pikeun mérek pinggiran lapangan sareng mastikeun yén pamaén tetep di daérah anu ditunjuk. Tambihkeun aturan pikeun mastikeun yén para pamaén dina tungtung lapangan nganggo alat-alat anu cocog pikeun ngajaga kaamanan. 4. Upami anjeun milih nganggo rumput, pastikeun éta dijaga sareng disiramkeun leres pikeun nyayogikeun permukaan maén anu lancar sareng bahkan. 3. Pasang garis wates sareng tujuan: Anggo garis wates pikeun mérek pinggiran lapangan sareng mastikeun yén pamaén tetep di daérah anu ditunjuk. Tambihkeun aturan pikeun mastikeun yén para pamaén dina tung | assistant | sun_Latn | 3 | true | false | null | false | null | {
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3e86de00-0905-4df1-a44b-936f55c937f9 | dec954aa-1c7b-41c2-8053-f4dde6857981 | d152cc81-57da-4461-a213-48771c6dcd31 | 2023-03-06T15:43:52.737164+00:00 | kumaha kuring bisa nungkulan sababaraha pamaén lamun aranjeunna kapanggih keur maén tanpa nuturkeun aturan | prompter | sun_Latn | 3 | true | false | null | false | null | {
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86397035-db7c-465e-b551-c21e766a9b6b | 23c4b363-879d-4a09-b1fc-959cef8a9212 | 9e9664c2-a77d-4e52-978d-21cabf691987 | 2023-03-05T00:12:14.256786+00:00 | Hockey lapangan dimaénkeun dina lapangan persegi panjang kalayan dua tim masing-masing 11 pamaén. Tujuanana pikeun nyetak gol ku pencét bal kana jaring tim lawan nganggo tongkat hoki. Pamaén henteu diidinan nganggo suku atanapi bagian awakna pikeun ngendalikeun bal, sareng aranjeunna ogé kedah nyingkahan kontak fisik sareng pamaén sanés. Kaulinan dibagi kana dua bagian, masing-masing 35 menit, kalayan istirahat 10 menit di antara. Upami pertandingan réngsé dina hasil imbang, éta tiasa lebet kana overtime atanapi shooting kanggo nangtoskeun juara. | assistant | sun_Latn | 3 | true | false | 2 | false | null | {
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e330a6b6-1311-40dd-8bd2-08315b15651d | 86397035-db7c-465e-b551-c21e766a9b6b | 4a4dca35-374d-4d72-b777-641848b553d0 | 2023-03-06T06:13:21.863919+00:00 | naon bédana antara balik jam lembur jeung shooting | prompter | sun_Latn | 3 | true | false | null | false | null | {
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383d2e56-2cc6-409f-a378-229caee34e81 | e330a6b6-1311-40dd-8bd2-08315b15651d | 4c43e234-0e9c-4f0d-8780-4dd3803602c6 | 2023-03-06T16:54:52.192484+00:00 | **Overtime**: Jumlah pamaén dikirangan tina 11 ka 7 anu teras maénkeun 2 satengah 7,5 menit masing-masing. **Shootout**: A shot-death penalty shoot-out. Tim silih coba stroke penalty dugi ka hiji tim nyetak sarta tim séjén henteu. Duanana métode nyobian megatkeun hasilna sareng mutuskeun juara, sanaos biasana shooting-out ngan dimaénkeun * saatos overtime masih hasilna dina hasilna. | assistant | sun_Latn | 3 | true | false | 0 | false | null | {
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3eb9c1e8-70bf-4b86-85c4-c4850546e10e | e330a6b6-1311-40dd-8bd2-08315b15651d | 1aaa9be1-0d07-4cb2-a2d7-4301cbfc4ecc | 2023-03-06T14:12:24.490391+00:00 | Dina loba olahraga, kaasup hoki lapangan, asupkeun kana lembur hartina maénkeun waktos tambahan saluareun durasi normal pertandingan pikeun nangtukeun juara. Periode lembur tiasa bénten-bénten dina panjangna sareng tiasa teraskeun dugi ka tim nyetak gol atanapi dugi ka wates waktos anu ditetepkeun. Shootout, di sisi anu sanésna, mangrupikeun jinis khusus prosedur tie-breaking anu dianggo dina sababaraha olahraga, kalebet hoki lapangan. Dina tembakan, unggal tim milih jumlah pamaén anu ditunjuk pikeun silih gantian nyobian nyetak gol ngalawan kiper tim lawan. Tim anu ngagaduhan tujuan pangageungna dina akhir tembakan diumumkeun juara. nembakan biasana dianggo nalika pertandingan tetep diikat saatos periode lembur parantos dimaénkeun, sareng sering dianggo salaku cara anu gancang sareng pikaresepeun pikeun nangtukeun juara dina waktos anu pondok. | assistant | sun_Latn | 3 | true | false | 1 | false | null | {
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dc63966e-0de7-4ba4-8cc5-99762dba415b | e330a6b6-1311-40dd-8bd2-08315b15651d | c226e598-2f5d-4ee1-a390-ddafa00b5ca5 | 2023-03-06T15:10:30.542638+00:00 | Lamun hiji pertandingan hoki ends in a draw, game can be decided with overtime or with a shootout (depending on the format of the competition). pindah ka overtime hartina pertandingan hoki diperpanjang ku jumlah waktu nu tangtu. A shootout hartina aya mutuskeun saha anu meunang pertandingan ku nyokot penalty shoot-outs, atawa ku ngajalankeun dina hiji-hiji kaayaan ngalawan kiper lawan. | assistant | sun_Latn | 3 | true | false | 2 | false | null | {
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