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Lub neej yav tom ntej nyob ntawm no. Thiab, nyob rau hauv lub neej yav tom ntej cov cav tov to taub lub ntiaj teb no nyob ib ncig ntawm lawv nyob rau hauv tib txoj kev uas tib neeg ua. Cov khoos phis tawj tuaj yeem tsav tsheb, kuaj xyuas cov kab mob, thiab ntsuas qhov tseeb rau yav tom ntej.
Qhov no yuav zoo li kev tshawb fawb tsis tseeb, tab sis cov qauv kev kawm tob ua rau nws muaj tseeb.
Cov sophisticated algorithms no nthuav qhia cov secrets ntawm artificial txawj ntse, tso cai rau cov khoos phis tawj los kawm tus kheej thiab txhim kho. Hauv tsab xov xwm no, peb yuav delve rau hauv lub cheeb tsam ntawm kev kawm tob.
Thiab, peb yuav tshawb xyuas lub peev xwm loj heev uas lawv muaj rau kev hloov pauv peb lub neej. Npaj los kawm txog cov cuab yeej siv thev naus laus zis uas hloov pauv tib neeg lub neej yav tom ntej.
Dab tsi yog Cov Qauv Kawm Sib Sib Tham?
Koj puas tau ua si qhov kev ua si uas koj yuav tsum txheeb xyuas qhov sib txawv ntawm ob daim duab?
Nws yog kev lom zem txawm li cas los xij, nws tuaj yeem nyuaj, txoj cai? Xav txog tej yam uas muaj peev xwm qhia lub computer ua si qhov kev ua si thiab yeej txhua zaus. Cov qauv kev kawm tob ua tiav qhov ntawd!
Cov qauv kev kawm sib sib zog nqus zoo ib yam li cov tshuab super-ntse uas tuaj yeem tshuaj xyuas ntau cov duab thiab txiav txim siab seb lawv muaj dab tsi. Lawv ua tiav qhov no los ntawm disassembled cov duab thiab kawm txhua tus ntawm tus kheej.
Tom qab ntawd lawv siv qhov lawv tau kawm los txheeb xyuas cov qauv thiab ua kev kwv yees txog cov duab tshiab uas lawv tsis tau pom dua ua ntej.
Cov qauv kev kawm sib sib zog nqus yog cov khoom siv hluav taws xob neural uas tuaj yeem kawm thiab rho tawm cov qauv nyuaj thiab cov yam ntxwv los ntawm cov ntaub ntawv loj. Cov qauv no yog tsim los ntawm ob peb txheej ntawm cov txuas txuas, lossis cov neurons, uas txheeb xyuas thiab hloov cov ntaub ntawv nkag los tsim cov khoom tsim tawm.
Cov qauv kev kawm sib sib zog nqus yog qhov tshwj xeeb zoo rau cov haujlwm uas xav tau qhov tseeb thiab qhov tseeb, xws li kev txheeb xyuas cov duab, kev paub txog kev hais lus, kev ua cov lus ntuj, thiab cov neeg hlau.
Lawv tau siv rau hauv txhua yam los ntawm kev tsav tsheb tus kheej mus rau kev kuaj mob, kev pom zoo, thiab predictive analytics.
Ntawm no yog ib qho yooj yim version ntawm kev pom los qhia cov ntaub ntawv ntws hauv cov qauv kev kawm tob.
Cov ntaub ntawv tawm tswv yim ntws mus rau hauv tus qauv cov txheej txheem nkag, uas tom qab ntawd dhau cov ntaub ntawv los ntawm ntau cov txheej txheem zais ua ntej muab qhov kev kwv yees tso tawm.
Txhua txheej zais ua haujlwm ua lej ua haujlwm ntawm cov ntaub ntawv nkag ua ntej dhau mus rau txheej tom ntej, uas muab qhov kev twv ua ntej kawg.
Tam sim no, cia saib dab tsi yog cov qauv kev kawm tob thiab peb tuaj yeem siv lawv li cas hauv peb lub neej.
1. Convolutional Neural Networks (CNNs)
CNNs yog cov qauv kev kawm sib sib zog nqus uas tau hloov pauv thaj tsam ntawm lub computer tsis pom kev. CNNs yog siv los faib cov duab, paub txog cov khoom, thiab ntu cov duab. Cov qauv thiab kev ua haujlwm ntawm tib neeg qhov muag pom cortex qhia txog kev tsim ntawm CNNs.
Lawv Ua Haujlwm Li Cas?
Lub CNN yog tsim los ntawm ntau cov txheej txheem convolutional, txheej txheej txheej, thiab cov txheej txheem txuas tag nrho. Cov tswv yim yog ib daim duab, thiab cov zis yog qhov kev twv ua ntej ntawm chav kawm ntawv ntawm daim duab.
CNN cov khaubncaws sab nraud povtseg tsim ib daim ntawv qhia tshwj xeeb los ntawm kev ua cov khoom lag luam ntawm cov duab nkag thiab cov txheej lim dej. Cov khaubncaws sab nraud povtseg txo qhov loj ntawm daim ntawv qhia feature los ntawm downsampling nws.
Thaum kawg, daim ntawv qhia tshwj xeeb yog siv los ntawm cov khaubncaws sab nraud povtseg tag nrho los kwv yees daim duab daim ntawv teev npe.
Vim li cas CNNs tseem ceeb?
CNNs yog qhov tseem ceeb vim tias lawv tuaj yeem kawm paub cov qauv thiab cov yam ntxwv hauv cov duab uas tib neeg pom tsis yooj yim. CNNs tuaj yeem qhia kom paub txog cov yam ntxwv zoo li cov npoo, cov ces kaum, thiab textures siv cov ntaub ntawv loj. Tom qab kawm cov khoom no, CNN tuaj yeem siv lawv los txheeb xyuas cov khoom hauv cov duab tshiab. CNNs tau ua kom pom qhov ua tau zoo tshaj plaws ntawm ntau yam kev txheeb xyuas cov duab.
Qhov twg peb siv CNNs
Kev kho mob, kev lag luam tsheb, thiab khw muag khoom tsuas yog qee qhov haujlwm uas ntiav CNNs. Hauv kev lag luam kho mob, lawv tuaj yeem muaj txiaj ntsig zoo rau kev kuaj mob, kev txhim kho tshuaj, thiab kev tshuaj xyuas cov duab kho mob.
Nyob rau hauv lub tsheb sector, lawv pab nrog txoj kev nrhiav pom, nrhiav pom khoom, thiab autonomous tsav tsheb. Lawv kuj tseem siv tau zoo heev hauv khw muag khoom rau kev tshawb nrhiav pom, cov duab-raws li cov khoom pom zoo, thiab kev tswj cov khoom muag.
Piv txwv li; Google ntiav CNNs hauv ntau daim ntawv thov, suav nrog Google Lens, zoo-nyiam duab txheeb xyuas cov cuab yeej. Qhov kev zov me nyuam siv CNNs los ntsuas cov duab thiab muab cov ntaub ntawv rau cov neeg siv.
Piv txwv li, Google Lens tuaj yeem paub cov khoom hauv cov duab thiab muab cov ntsiab lus hais txog lawv, xws li hom paj.
Nws kuj tseem tuaj yeem txhais cov ntawv uas tau muab rho tawm los ntawm ib daim duab rau ntau hom lus. Google Lens tuaj yeem muab cov ntaub ntawv tseem ceeb rau cov neeg siv khoom vim tias CNNs kev pabcuam hauv kev txheeb xyuas cov khoom kom raug thiab rho tawm cov yam ntxwv ntawm cov duab.
2. Long Short-Term Memory (LSTM) tes hauj lwm
Long Short-Term Memory (LSTM) tes hauj lwm yog tsim los daws cov teeb meem ntawm cov tsis tu ncua tsis tu ncua neural networks (RNNs). LSTM tes hauj lwm yog qhov zoo tagnrho rau cov haujlwm uas xav tau kev ua cov ntaub ntawv sib lawv liag sijhawm.
Lawv ua haujlwm los ntawm kev ua haujlwm ntawm lub cim xeeb tshwj xeeb thiab peb lub rooj vag mechanisms.
Lawv tswj cov dej ntws ntawm cov ntaub ntawv mus rau hauv thiab tawm ntawm lub cell. Lub rooj vag nkag, tsis nco qab rooj vag thiab qhov rooj tso zis yog peb lub rooj vag.
Lub rooj vag input tswj qhov ntws ntawm cov ntaub ntawv mus rau hauv lub cim xeeb ntawm tes, lub qhov rooj tsis nco qab tswj kev tshem tawm cov ntaub ntawv los ntawm lub xovtooj, thiab lub rooj vag tso zis tswj cov ntaub ntawv ntws tawm ntawm lub xovtooj.
Lawv Qhov Tseem Ceeb yog dab tsi?
LSTM tes hauj lwm muaj txiaj ntsig zoo vim tias lawv tuaj yeem ua tiav sawv cev thiab kwv yees cov ntaub ntawv sib txuas nrog kev sib raug zoo mus ntev. Lawv tuaj yeem sau thiab khaws cov ntaub ntawv hais txog cov khoom siv yav dhau los, tso cai rau lawv ua qhov kev kwv yees ntau dua ntawm cov khoom siv yav tom ntej.
Kev paub txog kev hais lus, kev sau ntawv txhais tes, kev ua cov lus zoo, thiab kev sau cov duab tsuas yog qee qhov ntawm cov ntawv thov uas tau siv LSTM networks.
Peb Siv LSTM Networks Qhov twg?
Ntau daim ntawv thov software thiab thev naus laus zis siv LSTM cov tes hauj lwm, suav nrog cov tshuab paub hais lus, cov cuab yeej ua hom lus zoo li txoj kev xav hauv nruab siab, tshuab txhais lus tshuab, thiab cov ntawv sau thiab duab tsim tshuab.
Lawv kuj tau siv rau hauv kev tsim cov tsheb tsav tus kheej thiab cov neeg hlau, nrog rau hauv kev lag luam nyiaj txiag txhawm rau txheeb xyuas kev dag thiab kev cia siab. Tshuag lag luam taw.
3. Generative Adversarial Networks (GANs)
GANs yog a kawm tob cov txheej txheem uas yog siv los tsim cov qauv ntaub ntawv tshiab uas zoo ib yam li cov ntaub ntawv muab. GANs yog tsim los ntawm ob Neural networks: ib qho uas kawm tsim cov qauv tshiab thiab ib qho uas kawm paub qhov txawv ntawm qhov tseeb thiab cov qauv tsim.
Hauv ib txoj hauv kev zoo sib xws, ob lub network no tau kawm ua ke kom txog thaum lub tshuab hluav taws xob tuaj yeem tsim cov qauv uas tsis txawv ntawm qhov tseeb.
Vim Li Cas Peb Thiaj Siv GANs
GANs yog qhov tseem ceeb vim lawv lub peev xwm los tsim cov khoom zoo cov ntaub ntawv hluavtaws uas tej zaum yuav siv tau rau ntau yam kev siv, suav nrog kev tsim duab thiab video, tiam ntawv, thiab txawm tias tsim suab paj nruag.
GANs kuj tau siv rau cov ntaub ntawv augmentation, uas yog tiam ntawm cov ntaub ntawv hluavtaws txhawm rau ntxiv cov ntaub ntawv tiag tiag hauv ntiaj teb thiab txhim kho kev ua haujlwm ntawm cov qauv kev kawm tshuab.
Tsis tas li ntawd, los ntawm kev tsim cov ntaub ntawv hluavtaws uas tuaj yeem siv los qhia cov qauv thiab xyaum kev sim, GANs muaj peev xwm los hloov cov haujlwm xws li tshuaj thiab kev tsim tshuaj.
Kev siv ntawm GANs
GANs tuaj yeem ntxiv cov ntaub ntawv, tsim cov duab tshiab lossis cov yeeb yaj kiab, thiab tseem tsim cov ntaub ntawv hluavtaws rau kev simulations. Tsis tas li ntawd, GANs muaj peev xwm ua haujlwm rau ntau yam kev siv xws li kev lom zem mus rau kev kho mob.
hnub nyoog thiab cov yeeb yaj duab. NVIDIA's StyleGAN2, piv txwv li, tau siv los tsim cov duab zoo nkauj ntawm cov neeg ua yeeb yam thiab kos duab.
4. Deep Belief Networks (DBNs)
Deep Belief Networks (DBNs) yog artificial txawj ntse systems uas tuaj yeem kawm pom cov qauv hauv cov ntaub ntawv. Lawv ua tiav qhov no los ntawm segmenting cov ntaub ntawv mus rau hauv me me thiab me chunks, tau txais kev nkag siab zoo ntawm txhua qib.
DBNs tuaj yeem kawm los ntawm cov ntaub ntawv yam tsis tau qhia tias nws yog dab tsi (qhov no yog hu ua "kev kawm tsis muaj kev saib xyuas"). Qhov no ua rau lawv muaj txiaj ntsig zoo heev rau kev kuaj xyuas cov qauv hauv cov ntaub ntawv uas ib tus neeg yuav pom qhov nyuaj lossis tsis yooj yim pom.
Dab tsi ua rau DBNs tseem ceeb?
DBNs yog qhov tseem ceeb vim lawv muaj peev xwm kawm cov ntaub ntawv hierarchical sawv cev. Cov sawv cev no tuaj yeem siv rau ntau yam kev siv xws li kev faib tawm, kev kuaj pom tsis zoo, thiab txo qhov loj me.
Lub peev xwm ntawm DBNs los ua cov kev qhia ua ntej uas tsis muaj kev saib xyuas, uas tuaj yeem ua rau muaj kev ua tau zoo ntawm cov qauv kev kawm sib sib zog nqus nrog cov ntaub ntawv me me, yog qhov txiaj ntsig tseem ceeb.
Dab tsi yog Daim Ntawv Thov ntawm DBNs?
Ib qho ntawm cov kev siv tseem ceeb tshaj plaws yog nrhiav pom khoom, uas DBNs tau siv los paub txog qee yam khoom xws li dav hlau, noog, thiab tib neeg. Lawv kuj tseem siv tau rau cov duab tsim thiab kev faib tawm, kev tshawb nrhiav cov lus tsa suab hauv cov yeeb yaj kiab, thiab kev nkag siab ntawm cov lus ntuj rau kev ua suab.
Tsis tas li ntawd, DBNs feem ntau ua haujlwm hauv cov ntaub ntawv los ntsuas tib neeg lub cev. DBNs yog cov cuab yeej zoo rau ntau yam kev lag luam, suav nrog kev kho mob thiab kev lag luam, thiab thev naus laus zis.
5. Deep Reinforcement Learning Networks (DRLs)
Sib sib zog nqus Kev Txhim Kho Kev Kawm Networks (DRLs) integrate sib sib zog nqus neural tes hauj lwm nrog kev txhawb zog kawm cov tswv yim kom tso cai rau cov neeg ua hauj lwm kawm nyob rau hauv ib tug nyuaj ib puag ncig ntawm kev sim thiab yuam kev.
DRLs yog siv los qhia cov neeg sawv cev yuav ua li cas txhawm rau txhim kho qhov khoom plig los ntawm kev cuam tshuam nrog lawv ib puag ncig thiab kawm los ntawm lawv qhov yuam kev.
Dab tsi ua rau lawv zoo kawg nkaus?
Lawv tau siv tau zoo hauv ntau yam kev siv, suav nrog kev ua si, neeg hlau, thiab kev tsav tsheb tsis muaj zog. DRLs yog qhov tseem ceeb vim tias lawv tuaj yeem kawm ncaj qha los ntawm cov khoom siv raw khoom, tso cai rau cov neeg ua haujlwm txiav txim siab raws li lawv cov kev cuam tshuam nrog ib puag ncig.
Cov ntawv thov tseem ceeb
DRLs tau ua haujlwm hauv qhov xwm txheej tiag tiag vim tias lawv tuaj yeem daws teeb meem nyuaj.
DRLs tau suav nrog ntau qhov tseem ceeb software thiab tech platforms, suav nrog OpenAI's Gym, Unity's ML-Agents, thiab Google's DeepMind Lab. AlphaGo, ua los ntawm Google's DeepMind, piv txwv li, ntiav DRL los ua si ntawm pawg thawj coj saib mus ntawm qib sib tw ntiaj teb.
Lwm qhov kev siv DRL yog nyob rau hauv cov neeg hlau, qhov uas nws yog siv los tswj kev txav ntawm cov caj npab neeg hlau los ua haujlwm xws li tuav cov khoom lossis cov khoom sib tsoo. DRLs muaj ntau yam siv thiab yog cov cuab yeej siv tau rau cob qhia cov neeg ua haujlwm kom kawm thiab txiav txim siab hauv cov teeb meem nyuaj.
6. Autoencoders
Autoencoders yog hom kev nthuav dav neural network uas tau ntes tau qhov kev txaus siab ntawm ob tus kws tshawb fawb thiab cov kws tshawb fawb cov ntaub ntawv. Lawv yog cov hauv paus tsim los kawm yuav ua li cas compress thiab kho cov ntaub ntawv.
Cov ntaub ntawv tawm tswv yim yog pub los ntawm kev sib txuas ntawm cov khaubncaws sab nraud povtseg uas maj mam txo qhov loj ntawm cov ntaub ntawv kom txog rau thaum nws yog compressed rau hauv ib lub raj mis nrog cov nodes tsawg tshaj li cov input thiab tso tawm txheej.
Qhov no compressed sawv cev yog siv los rov tsim cov ntaub ntawv tawm tswv yim qub uas siv cov txheej txheem ntawm cov khaubncaws sab nraud povtseg uas maj mam tsa cov ntaub ntawv qhov dimension rov qab mus rau nws cov duab qub.
Vim Li Cas Nws Tseem Ceeb?
Autoencoders yog ib qho tseem ceeb ntawm cov khoom kawm tob vim lawv ua kom muaj kev rho tawm thiab cov ntaub ntawv txo tau.
Lawv muaj peev xwm txheeb xyuas cov ntsiab lus tseem ceeb ntawm cov ntaub ntawv tuaj thiab txhais rau hauv daim ntawv compressed uas tej zaum yuav raug siv rau lwm yam dej num xws li kev faib tawm, pab pawg, lossis tsim cov ntaub ntawv tshiab.
Peb Siv Autoencoders qhov twg?
Kev kuaj pom tsis meej, kev ua cov lus ntuj, thiab lub computer tsis pom kev tsuas yog qee qhov kev qhuab qhia uas siv autoencoders. Autoencoders, piv txwv li, tuaj yeem siv rau cov duab compression, duab denoising, thiab duab synthesis hauv computer tsis pom kev.
Peb tuaj yeem siv Autoencoders hauv cov haujlwm xws li kev tsim cov ntawv sau, sau ntawv categorization, thiab sau cov ntawv sau hauv cov lus ua haujlwm. Nws tuaj yeem txheeb xyuas qhov kev ua haujlwm tsis zoo hauv cov ntaub ntawv uas deviates los ntawm tus qauv hauv kev txheeb xyuas qhov tsis zoo.
7. Capsule Networks
Capsule Networks yog qhov kev kawm sib sib zog nqus tshiab uas tau tsim los hloov pauv rau Convolutional Neural Networks (CNNs).
Capsule Networks yog raws li kev xav ntawm pawg hlwb hu ua capsules uas yog lub luag haujlwm rau kev lees paub qhov muaj nyob ntawm qee yam khoom hauv daim duab thiab encoding nws cov cwj pwm, xws li kev taw qhia thiab txoj hauj lwm, rau hauv lawv cov zis vectors. Capsule Networks tuaj yeem tswj hwm kev sib cuam tshuam ntawm thaj chaw thiab kev pom qhov hloov pauv zoo dua li CNNs.
Vim li cas peb thiaj xaiv Capsule Networks tshaj CNN's?
Capsule Networks muaj txiaj ntsig zoo vim tias lawv kov yeej CNN cov teeb meem hauv kev ntes hierarchical kev sib raug zoo ntawm cov khoom hauv daim duab. CNNs tuaj yeem paub txog ntau qhov ntau thiab tsawg tab sis nyuaj kom nkag siab tias cov khoom no txuas mus rau ib leeg li cas.
Capsule Networks, ntawm qhov tod tes, tuaj yeem kawm paub txog yam khoom thiab lawv cov khoom, nrog rau yuav ua li cas lawv tau muab tso rau hauv cov duab, ua rau lawv muaj txiaj ntsig zoo rau kev siv computer tsis pom kev.
Cov cheeb tsam ntawm daim ntawv thov
Capsule Networks twb tau ua pov thawj tau txais txiaj ntsig zoo hauv ntau yam kev siv, suav nrog kev faib cov duab, kev txheeb xyuas cov khoom, thiab cov duab segmentation.
Lawv tau siv los paub qhov txawv ntawm cov duab kho mob, paub txog tib neeg hauv cov yeeb yaj kiab, thiab tseem tsim cov qauv 3D tawm ntawm 2D duab.
Txhawm rau kom lawv cov kev ua tau zoo, Capsule Networks tau ua ke nrog lwm cov kev kawm sib sib zog nqus xws li Generative Adversarial Networks (GANs) thiab Variational Autoencoders (VAEs). Capsule Networks tau kwv yees tias yuav ua lub luag haujlwm tseem ceeb hauv kev txhim kho lub computer tsis pom kev thev naus laus zis raws li kev tshawb fawb ntawm kev kawm tob zuj zus.
Piv txwv; Nibabel yog tus paub zoo Python cov cuab yeej rau kev nyeem ntawv thiab sau cov ntaub ntawv neuroimaging hom. Rau cov duab segmentation, nws ntiav Capsule Networks.
8. Cov qauv saib xyuas
Cov qauv kev kawm sib sib zog nqus hu ua qauv kev saib xyuas, tseem hu ua cov txheej txheem saib xyuas, sib zog ua kom qhov tseeb ntawm tshuab kev kawm ua qauv. Cov qauv no ua haujlwm los ntawm kev tsom mus rau qee yam nta ntawm cov ntaub ntawv tuaj, ua rau muaj txiaj ntsig zoo thiab ua haujlwm zoo.
Nyob rau hauv tej yam ntuj tso lus ua hauj lwm xws li tshuab txhais lus thiab kev soj ntsuam kev xav, kev saib xyuas tau pom tias ua tau zoo heev.
Lawv Qhov Tseem Ceeb Yog Dab Tsi?
Cov qauv kev saib xyuas yog qhov muaj txiaj ntsig zoo vim tias lawv ua kom muaj txiaj ntsig zoo thiab ua haujlwm zoo ntawm cov ntaub ntawv nyuaj.
Ib txwm neural networks ntsuas tag nrho cov ntaub ntawv tawm tswv yim raws li qhov tseem ceeb sib npaug, ua rau kev ua haujlwm qeeb thiab txo qhov raug. Cov txheej txheem saib xyuas tsom mus rau qhov tseem ceeb ntawm cov ntaub ntawv nkag, tso cai rau kev kwv yees sai dua thiab raug dua.
Cov cheeb tsam ntawm kev siv
Nyob rau hauv lub tshav pob ntawm kev txawj ntse, kev saib xyuas mechanisms muaj ntau yam kev siv, nrog rau kev ua cov lus ntuj, kev paub txog cov duab thiab lub suab, thiab txawm tias muaj tsheb tsis muaj tsheb.
Kev saib xyuas, piv txwv li, tuaj yeem siv los txhim kho tshuab txhais lus hauv kev ua cov lus zoo los ntawm kev tso cai rau lub kaw lus tsom rau qee cov lus lossis kab lus uas tseem ceeb rau cov ntsiab lus.
Cov txheej txheem kev saib xyuas hauv cov tsheb tsis muaj zog tuaj yeem ua haujlwm los pab lub kaw lus hauv kev tsom mus rau qee yam khoom lossis cov teeb meem hauv nws qhov chaw ib puag ncig.
9. Transformer Networks
Transformer tes hauj lwm yog cov qauv kev kawm tob uas tshuaj xyuas thiab tsim cov ntaub ntawv ua ntu zus. Lawv ua haujlwm los ntawm kev ua cov txheej txheem tawm tswv yim ib ntu ntawm ib lub sijhawm thiab tsim cov khoom tsim tawm ntawm tib lossis qhov ntev sib txawv.
Transformer tes hauj lwm, tsis zoo li cov qauv sib txuas-rau-ib ntu ua qauv, tsis ua cov kab ke siv cov kev sib txuas hauv nruab nrab (RNNs). Hloov chaw, lawv ntiav cov txheej txheem kev saib xyuas tus kheej kom kawm txog kev sib txuas ntawm qhov sib lawv liag.
Dab tsi yog qhov tseem ceeb ntawm Transformer Networks?
Transformer tes hauj lwm tau loj hlob nyob rau hauv muaj koob meej nyob rau hauv xyoo tas los no los ntawm lawv cov kev ua tau zoo nyob rau hauv tej yam ntuj tso lus ua hauj lwm.
Lawv tshwj xeeb tshaj yog tsim nyog rau kev tsim cov ntawv nyeem xws li kev txhais lus, sau cov ntawv sau, thiab kev sib tham ntau lawm.
Transformer tes hauj lwm yog qhov muaj txiaj ntsig zoo dua li cov qauv ntawm RNN, ua rau lawv xaiv qhov kev xaiv rau cov ntawv loj loj.
Koj tuaj yeem nrhiav Transformer Networks nyob qhov twg?
Transformer tes hauj lwm tau dav ua hauj lwm nyob rau hauv ib tug ntau yam ntawm daim ntawv thov, tshwj xeeb tshaj yog cov natural language processing.
GPT (Generative Pre-trained Transformer) series yog ib qho tseem ceeb ntawm kev hloov pauv raws li tus qauv uas tau siv los ua haujlwm xws li kev txhais lus, sau ntawv sau, thiab tiam chatbot.
BERT (Bidirectional Encoder Cov Neeg Sawv Cev los ntawm Transformers) yog lwm hom kev hloov pauv raws li tus qauv uas tau siv rau kev nkag siab cov lus zoo xws li cov lus nug teb thiab kev tshuaj xyuas kev xav.
Ob leeg GPT thiab BERT tau tsim nrog PyTorch, ib qho kev qhib-qhov kev kawm sib sib zog nqus uas tau nrov rau kev tsim cov qauv kev hloov pauv.
10. Txwv Boltzmann Machine (RBMs)
Txwv Boltzmann Machines (RBMs) yog ib hom kev tsis saib xyuas neural network uas kawm nyob rau hauv generative yam. Vim lawv muaj peev xwm kawm thiab rho tawm cov yam ntxwv tseem ceeb los ntawm cov ntaub ntawv siab, lawv tau siv dav hauv kev kawm tshuab thiab kev kawm tob.
RBMs yog tsim los ntawm ob txheej, pom thiab zais, nrog txhua txheej uas muaj ib pawg ntawm cov neurons txuas los ntawm qhov hnyav. RBMs yog tsim los kawm txog qhov muaj feem cuam tshuam uas piav qhia cov ntaub ntawv nkag.
Kev txwv Boltzmann Tshuab yog dab tsi?
RBMs siv lub tswv yim kev kawm zoo. Hauv RBMs, cov txheej pom pom muaj kev cuam tshuam cov ntaub ntawv nkag, thaum lub faus txheej encodes cov ntaub ntawv tawm tswv yim tus yam ntxwv. Qhov hnyav ntawm qhov pom thiab zais cov khaubncaws sab nraud povtseg qhia lub zog ntawm lawv qhov txuas.
RBMs kho qhov hnyav thiab kev tsis sib haum xeeb ntawm cov khaubncaws sab nraud povtseg thaum kev cob qhia siv cov txheej txheem hu ua contrastive divergence. Contrastive divergence yog ib txoj kev kawm uas tsis muaj kev saib xyuas uas ua rau tus qauv qhov kev twv yuav tshwm sim.
Dab tsi yog qhov tseem ceeb ntawm Kev Txwv Boltzmann Machine?
RBMs tseem ceeb hauv tshuab kev kawm thiab kev kawm tob vim lawv tuaj yeem kawm thiab rho tawm cov yam ntxwv cuam tshuam los ntawm ntau cov ntaub ntawv.
Lawv ua tau zoo heev rau cov duab thiab kev paub txog kev hais lus, thiab lawv tau ua haujlwm rau ntau yam kev siv xws li cov lus pom zoo, kev kuaj pom tsis zoo, thiab txo qhov loj me. RBMs tuaj yeem pom cov qauv hauv cov ntaub ntawv loj, ua rau muaj kev kwv yees zoo dua thiab kev pom.
Qhov twg yuav txwv Boltzmann Machine siv?
Cov ntawv thov rau RBMs suav nrog kev txo qhov loj me, kev kuaj pom tsis zoo, thiab cov lus pom zoo. RBMs tshwj xeeb yog pab rau kev soj ntsuam kev xav thiab lub ntsiab lus qauv nyob rau hauv cov ntsiab lus ntawm natural language processing.
Kev ntseeg sib sib zog nqus, ib hom neural network siv rau lub suab thiab daim duab lees paub, kuj ntiav RBMs. Lub Deep Belief Network Toolbox, TensorFlow, Thiab Theano yog qee qhov piv txwv tshwj xeeb ntawm software lossis thev naus laus zis uas siv RBMs.
Qhwv Sau
Cov qauv kev kawm tob tau dhau los ua qhov tseem ceeb hauv ntau yam kev lag luam, suav nrog kev paub txog kev hais lus, kev ua cov lus zoo, thiab kev pom hauv computer.
Convolutional Neural Networks (CNNs) thiab Recurrent Neural Networks (RNNs) tau qhia txog cov lus cog tseg thiab tau siv dav hauv ntau daim ntawv thov, txawm li cas los xij, txhua tus qauv Deep Learning muaj lawv qhov zoo thiab qhov tsis zoo.
Txawm li cas los xij, cov kws tshawb fawb tseem tab tom saib rau hauv Kev Txwv Boltzmann Machines (RBMs) thiab lwm yam ntawm Deep Learning qauv vim lawv kuj muaj qhov zoo tshwj xeeb.
Cov qauv tshiab thiab muaj tswv yim xav tias yuav tsim tau raws li thaj tsam ntawm kev kawm tob ntxiv mus ntxiv txhawm rau txhawm rau daws teeb meem nyuaj.
Sau ntawv cia Ncua