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Nheyo dzeAI
Nzwisisa kuti AI chii, masisitimu anodzidza sei, kwavanokundikana, uye maitiro ekutonga zvirevo pasina hype.
Raibhurari yemahara yeAI
Basics NhungamiroKusununguka nekusingaperi.
84 magwara eChirungu, nzira dzakarongeka dzekudzidza, uye raibhurari yakavhurika — yakavakwa neyakazvimiririra 501(c)(3) isingabatsiri kuitira kuti chero munhu anzwisise AI yemazuva ano.
84Mazano emahara
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Tanga pano
Vashanu Mhedzisiro-yakavakirwa makosi
Kosi yega yega inosanganisira mhedzisiro yakajeka, hunyanzvi hwemepu, zviitiko zvekudzidzira, uye yakashandiswa capstone.
02
Responsible AI Mushandisi
Shandisa AI zvine mutsindo uchidzivirira zvakavanzika, kutarisa zvinobuda, uye kuchengetedza kuzvidavirira kwevanhu.
03
AI pabasa
Ongorora zviitiko zvekushandisa pabasa, mhanyisa vatyairi vakachengeteka, kuyera kukosha, uye kutaurirana shanduko zvine mutsindo.
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AI Policy & Society
Ongorora AI masisitimu kuburikidza nekodzero, kuenzana, hutongi, kuchengetedzeka, uye mhedzisiro yekufarira veruzhinji.
05
Kuvaka neAI Systems
Nzwisisa mamodheru emitauro, kudzoreredza, vamiririri, kuongorora, mutengo, uye kuendesa zvidziviriro kuburikidza nehurongwa hwehurongwa.
Musoro wenyaya
Bhurawuza nekumhanya
Svetukira munharaunda yaunofarira. Nzira yega yega ine akawanda akajeka-Chirungu magwaro.
BasicsYakazara nzira kuchinyorwa: Linux Vakapindwa muropa » General » Noticias » AI inoshanda sei padanho repamusoro?84 Nhungamiro Mutauro AILLMs, NLP, ChatGPT, kukurudzira, uye kunzwisisa kwemavara.155 Nhungamiro Visual AIChizvarwa chemifananidzo, kuona kwemakomputa, vhidhiyo, uye dhizaini.133 Nhungamiro Audio AIIzwi, mimhanzi, podcasting, uye odhiyo-yekutanga AI.117 Nhungamiro MakambaniProfiles ye OpenAI, Anthropic, Google, Meta, nezvimwe.121 Nhungamiro ZvikumbiroAI inoratidzira sei mukukodha, kutsvaga, vamiririri, uye hupenyu hwezuva nezuva.111 Nhungamiro MaindasitiriAI mune zvehutano, mari, dzidzo, simba, nezvimwe.91 Nhungamiro TekinorojiChips, kugadzirisa-kwakanaka, RAG, vectors, uye AI zvivakwa.177 Nhungamiro NzangaKusarudzika, kuvanzika, copyright, kuchengetedzeka, uye ramangwana reAI.30 Nhungamiro
Raibhurari yakazara
Zvese zvinotungamira
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Basics
Self-Play Fine-Tuning
Kuzvigadzirisa pachako kunonatsiridza modhi nekuita kuti ikwikwidze kana kudzidza kubva kune yayo yakapfuura yakabuda, ichigadzira yayo chiratidzo chekudzidzisa.
BasicsKudzidzira Nguva Yekuedza
Kudzidzira-nguva yekudzidziswa (TTT) inoita kuti modhi irambe ichidzidza kubva kune imwe neimwe nyowani yekuisa panguva yainofanotaura, pane kugara yakaoma nechando mushure mekudzidziswa.
BasicsGrokking uye Kunonoka Generalization
Grokking chinhu chinokatyamadza apo neural network inotanga kubata nemusoro data rayo rekudzidziswa, inogara padhuze-zero chokwadi chechokwadi kwenguva yakareba, uyezve…
BasicsOverfitting uye Underfitting
Overfitting ndeye apo modhi inoyeuka data yayo yekudzidziswa uye inokundikana pamienzaniso mitsva; underfitting ndipo payakanyanya kupusa kutora iyo chaiyo pateni.
BasicsRegularization
Regularization seti yematekiniki anomanikidza nemaune modhi kuti ienderane kune itsva data pachinzvimbo chekurangarira seti yekudzidziswa.
BasicsBackpropagation
Backpropagation ndiyo algorithm inoita kuti neural network idzidze kubva mukukanganisa kwayo nekuverenga zvine huremu huremu hwega hwega hwakakonzera kukanganisa.
BasicsGradient Descent
Gradient descent ndiyo nzira yekuwedzera iyo inofambisa huremu hwemodhi kudzika kuenda kukanganiso yakaderera, nhanho diki panguva.
BasicsKurasikirwa Mabasa
Basa rekurasikirwa ndiyo nhamba imwe chete inoudza modhi kuti kufanotaura kwakashata sei, kushandura chinangwa chisina kujeka kuita chimwe chinhu math chinogona kukwirisa.
BasicsActivation Mabasa
Activation mabasa ndiwo madiki asina mutsara magedhi mukati meuron yega yega anoita kuti neural network idzidze yakaoma, yakakombama mapatani panzvimbo yekungotwasanuka mitsetse.
BasicsConvolutional Neural Networks
Convolutional Neural Networks (CNNs) ndiyo dhizaini yekuvaka yekunzwisisa mifananidzo.
BasicsRecurrent Neural Networks
Recurrent Neural Networks (RNNs) yakavakirwa kubata kutevedzana senge zvinyorwa, kutaura, uye nguva dzakateedzana.
BasicsGenerative Adversarial Networks
Generative Adversarial Networks (GANs) inogadzira data nyowani nekuisa maviri neural network vachipesana mumakwikwi.
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