Raibhurari yemahara yeAI

Dzidza AI.Kusununguka nekusingaperi.

1019 magwara eChirungu, nzira dzakarongeka dzekudzidza, uye raibhurari yakavhurika — yakavakwa neyakazvimiririra 501(c)(3) isingabatsiri kuitira kuti chero munhu anzwisise AI yemazuva ano.

1019Mazano emahara
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Vashanu Mhedzisiro-yakavakirwa makosi

Kosi yega yega inosanganisira mhedzisiro yakajeka, hunyanzvi hwemepu, zviitiko zvekudzidzira, uye yakashandiswa capstone.

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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

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Tekinoroji

Ruzivo Distillation

Kusarurwa kweruzivo kunodzidzisa modhi 'yemudzidzi' mudiki kutevedzera hombe, yakarurama 'mudzidzisi' modhi.

2 VerengaVerenga
Tekinoroji

Musanganiswa Wenyanzvi

Musanganiswa weNyanzvi (MoE) dhizaini yemhando iyo inotsemura network kuita akawanda ehunyanzvi sub-network uye inomisa mashoma chete pakuisa.

2 VerengaVerenga
Basics

Embeddings

Embeddings inoshandura mazwi, mifananidzo, kana imwe dhata kuita rondedzero yenhamba (vectors) kuitira kuti zvinhu zvakafanana zvinopedzisira zvave pedyo pamwe chete munzvimbo yakakwirira-dimensional.

2 VerengaVerenga
Basics

Tokenization

Tokenization inhanho inocheka zvinyorwa kuita zvidimbu zvidiki zvinonzi tokens, iwo mayuniti emhando yemutauro anoverenga uye anofanotaura.

2 VerengaVerenga
Basics

Feature Engineering

Feature engineering inyanzvi yekushandura data rakasvibira kuita ruzivo rwekupinza (maficha) anobatsira modhi kudzidza.

2 VerengaVerenga
Tekinoroji

Model Quantization

Model quantization inodzikisira neural network nekuchengetedza nhamba dzayo mumabhiti mashoma, saka iyo yakafanana modhi inomhanya nekukurumidza uye padiki hardware.

2 VerengaVerenga
Tekinoroji

Federated Learning

Kudzidza kwakabatana kunodzidzisa modhi yakagovaniswa pamidziyo yakawanda kana masangano pasina kuunganidza data ravo rakasvibira munzvimbo imwechete.

2 VerengaVerenga
Basics

Sarudzo Miti uye Masango Masango

Muti wesarudzo unofanotaura nekubvunza mutsara wemibvunzo yakapusa hongu / kwete, seyekuyerera.

2 VerengaVerenga
Basics

Tsigira Vector Machines

Muchina wekutsigira vector (SVM) ndeye classic algorithm inoparadzanisa mapoka maviri nekudhirowa muganho wakakura kwazvo pakati pavo.

2 VerengaVerenga
Basics

Dimensionality Reduction

Dimensionality kudzikisira inodzikisira data kubva kumakoramu akawanda (maficha) kusvika kune mashoma uchichengeta yakakosha chimiro.

2 VerengaVerenga
Basics

Kutamisa Kudzidza

Kutamisa kudzidza kunoshandisa zvakare modhi yakadzidziswa pane yakakura dataset uye inoigadzirisa kune itsva, ine chekuita nebasa.

2 VerengaVerenga
Basics

Kuzvitarisira Kudzidza

Kudzidzira wega-wega kudzidza kunodzidzisa modhi pane isina kunyorwa data nekugadzira basa rine mhinduro yakavanzwa mukati me data pacharo.

2 VerengaVerenga

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