Zviri Mukati[Viga][Ratidza]
Nenzira, isu tese tinoziva nezvekukurumidza kwemuchina kudzidza tekinoroji yakagadzira mumakore akati wandei apfuura. Kudzidza kwemichina chirango chakakwezva kufarira kwemakambani akati wandei, vadzidzi, uye zvikamu.
Nekuda kweizvi, ini ndichakurukura mamwe emabhuku makuru emuchina kudzidza ayo mainjiniya kana newbie anofanirwa kuverenga nhasi. Mese munofanira kunge mabvumirana kuti kuverenga mabhuku hakuna kufanana nekushandisa njere.
Kuverenga mabhuku kunobatsira pfungwa dzedu kuwana zvakawanda zvezvinhu zvitsva. Kuverenga kudzidza, shure kwezvose. A self-learner tag inonakidza kwazvo kuva nayo. Mabhuku makurusa anowanika mumunda achasimbiswa munyaya ino.
Mabhuku anotevera anopa sumo yakaedzwa-uye-yechokwadi kune yakakura ndima yeAI uye anowanzoshandiswa mumakosi ekuyunivhesiti uye anokurudzirwa nevadzidzi uye mainjiniya zvakafanana.
Kunyangwe iwe uine toni ye machine learning ruzivo, kunhonga rimwe remabhuku aya anogona kunge ari nzira inotyisa yekumhanyisa. Pashure pezvose, kudzidza inzira inopfuurira.
1. Machine Kudzidza Kune Mhedziso Vanotanga
Unoda kudzidza kudzidza muchina asi usingazive kuti unozviita sei. Pane akati wandei akakosha e theoretical uye manhamba pfungwa dzaunofanirwa kunzwisisa usati watanga yako epic rwendo mukudzidza muchina. Uye bhuku iri rinozadzisa chido ichocho!
Inopa ma novices akazara ane yepamusoro-level, inoshanda sumo yekudzidza muchina. Iro bhuku Muchina Kudzidza kune Absolute Beginners ndeimwe yesarudzo dzakanakisa kune chero munhu ari kutsvaga tsananguro yakapfava yekudzidza muchina uye mazano anoenderana.
Iri bhuku rakawanda ml algorithms inoperekedzwa netsananguro pfupi uye mienzaniso yakajeka kubatsira vaverengi kunzwisisa zvese zvinokurukurwa.
Misoro yakafukidzwa mubhuku
- Nheyo dze neural networks
- Kudzokorora kudzokorora
- Feature engineering
- Clustering
- Muchinjikwa-kusimbisa
- Data scrubbing techniques
- Sarudzo Miti
- Ensemble modeling
2. Kudzidza kweMichina yeDummies
Kudzidza kwemichina kungave zano rinovhiringa kuvanhu venguva dzose. Zvisinei, inokosha kune avo vedu vanoziva.
Pasina ML, zvakaoma kugadzirisa nyaya senge mhinduro dzekutsvaga pamhepo, kushambadza chaiko pamapeji ewebhu, otomatiki, kana kusefa spam (Hongu!).
Nekuda kweizvozvo, bhuku rino rinokupa sumo yakatwasuka iyo ichakubatsira iwe kudzidza zvakawanda nezve enigmatic nzvimbo yekudzidza muchina. Nerubatsiro rweMuchina Kudzidza KwemaDummies, iwe uchadzidza "kutaura" mitauro yakaita sePython neR, izvo zvinokugonesa kudzidzisa makomputa kuita kucherechedzwa kwemaitiro uye kuongorora data.
Pamusoro pezvo, iwe uchadzidza kushandisa Python's Anaconda uye R Studio kugadzira muR.
Misoro yakafukidzwa mubhuku
- Kugadzirira kwedata
- nzira dzekudzidza muchina
- The machine kudzidza kutenderera
- Kudzidzira uye kusingatarisirwe kudzidza
- Kudzidzisa muchina kudzidza masisitimu
- Kusunga michina yekudzidza nzira kune mhedzisiro
3. Izana Remapeji Muchina Kudzidza Bhuku
Zvinogoneka here kuvhara ese maficha ekudzidza muchina mukati mepasi pemapeji zana? Andriy Burkov's The Hundred-Peji Machine Learning Book kuyedza kuita zvimwe chetezvo.
Iro bhuku rekudzidza muchina rakanyorwa zvakanaka uye rinotsigirwa nevatungamiriri vepfungwa vane mukurumbira vanosanganisira Sujeet Varakhedi, Musoro weUinjiniya paeBay, naPeter Norvig, Director weKutsvagisa kuGoogle.
Iro ibhuku rakakura kwazvo kune anotanga kudzidza muchina. Mushure mekunyatsoverenga bhuku, iwe unozokwanisa kuvaka uye kunzwisisa akaomesesa AI masisitimu, kubudirira mubvunzurudzo yekudzidza muchina, uye kunyange kuvhura yako chaiyo ML-yakavakirwa kambani.
Nekudaro, iro bhuku harina kurongerwa kune vanotanga zvakakwana mukudzidza muchina. Tarisa pane imwe nzvimbo kana iwe uchitsvaga chero chinhu chakakosha.
Misoro yakafukidzwa mubhuku
- Anatomy ye kudzidza algorithm
- Kudzidza kwakatariswa uye kudzidza kusingatarisirwe
- Kusimbisa Kudzidza
- Yakakosha algorithms yeMuchina Kudzidza
- Mhedziso yeNeural network uye kudzidza kwakadzama
4. Kunzwisisa Machine Kudzidza
Sumo yakarongeka yekudzidza kwemichina inopiwa mubhuku rinonzi Kunzwisisa Kudzidza Kwemuchina. Iri bhuku rinoongorora zvakadzama mune ekutanga mazano, computational paradigms, uye masvomhu anotorwa ekudzidza muchina.
Huwandu hwakakura hwezvidzidzo zvemuchina zvinoratidzwa nenzira yakapusa nekudzidza muchina. Iwo theoretical hwaro hwekudzidza muchina anotsanangurwa mubhuku, pamwe chete nemasvomhu anotorwa anoshandura idzi nheyo kuita algorithms anobatsira.
Iri bhuku rinopa zvakakosha risati rabata zvakasiyana-siyana zvakakosha izvo zvisati zvambotaurwa nemabhuku ekutanga.
Inosanganisirwa mune iyi nhaurirano ye convexity uye kugadzikana pfungwa uye computational kuoma kwekudzidza, pamwe neakakosha algorithmic paradigms senge stochastic. gradient descent, neural network, uye yakarongeka yakabuda yekudzidza, pamwe neachangobuda edzidziso pfungwa senge PAC-Bayes maitiro uye compression-yakavakirwa miganhu. yakagadzirirwa kutanga magiredhi kana epamberi undergraduates.
Misoro yakafukidzwa mubhuku
- Iyo computational yakaoma yekudzidza muchina
- ML algorithms
- Neural network
- PAC-Bayes nzira
- Stochastic gradient descent
- Kudzidza kwakagadzirwa kwakagadzirwa
5. Nhanganyaya kune Machine Kudzidza nePython
Iwe uri Python-savvy data sainzi anoda kudzidza muchina kudzidza? Iro bhuku rakanakisa rekutanga muchina wako wekudzidza dhizaini naro Sumo kuMuchina Kudzidza nePython: Gwaro reData Sainzi.
Nerubatsiro rwebhuku reKusuma kuMuchina Kudzidza nePython: Gwaro reData Sainzi, iwe unowana akasiyana maitiro anobatsira ekugadzira echinyakare muchina kudzidza zvirongwa.
Iwe unovhara danho rega rega rakakosha rinobatanidzwa mukushandisa Python uye iyo Scikit-Dzidza pasuru kuvaka yakavimbika muchina kudzidza maapplication.
Kuwana kubata kwakasimba kwematplotlib uye NumPy maraibhurari kuchaita kuti kudzidza kuve nyore.
Misoro yakafukidzwa mubhuku
- Nzira dzemazuva ano dzeparameter tweaking uye modhi yekuongorora
- Zvishandiso uye ekutanga muchina kudzidza mazano
- otomatiki kudzidza maitiro
- Matekiniki ekugadzirisa data remavara
- Model chaining uye workflow encapsulation mapaipi
- Kumiririra data mushure mekugadzirisa
6. Maoko-paMuchina Kudzidza neSci-kit dzidza, Keras & Tensorflow
Pakati pezvinyorwa zvakanyatsojeka pasainzi yedata uye kudzidza muchina, yakazadzwa neruzivo. Zvinokurudzirwa kuti nyanzvi nevatsva vadzidze zvakawanda nezvenyaya iyi.
Kunyange zvazvo bhuku iri riine dzidziso shomanana chete, rinotsigirwa nemienzaniso yakasimba, ichipa nzvimbo parondedzero.
Bhuku rino rinosanganisira nyaya dzakasiyana siyana, kusanganisira scikit-dzidza yemapurojekiti ekudzidza muchina uye TensorFlow yekugadzira nekudzidzisa neural network.
Mushure mekuverenga bhuku rino, tinofunga kuti unenge wakashongedzerwa zvirinani kuti uwedzere kunzvera kudzidza zvakadzika uye kubata nezvinetso zvinoshanda.
Misoro yakafukidzwa mubhuku
- Ongorora mamiriro emuchina kudzidza, kunyanya neural network
- Tevera muenzaniso wekudzidza muchina kubva pakutanga kusvika kumagumo uchishandisa Scikit-Dzidza.
- Ongorora akati wandei ekudzidzisa modhi, senge ensemble matekiniki, masango asina kujairika, miti yesarudzo, uye kutsigira vector michina.
- Gadzira uye dzidzisa neural network uchishandisa TensorFlow raibhurari.
- Funga nezve convolutional network, mambure anodzokororwa, uye zvakadzama kusimbaradza kudzidza paunenge uchiongorora neural net designs.
- Dzidza maitiro ekuyera uye kudzidzisa zvakadzika neural network.
7. Machine Kudzidza kune Hackers
Kune ane ruzivo programmer anofarira kuongorora data, bhuku Muchina Kudzidza kune Hackers rakanyorwa. Hackers inyanzvi dzemasvomhu mune ino mamiriro.
Kune mumwe munhu ane kunzwisisa kwakasimba kweR, bhuku rino isarudzo huru nokuti ruzhinji rwaro rwakanangana nekuongorora data muR. Uyezve yakafukidzwa mubhuku ndiyo nzira yekushandisa data uchishandisa advanced R.
Iko kusanganisirwa kweakakodzera nyaya nyaya kunosimbisa kukosha kwekushandisa muchina kudzidza algorithms inogona kunge iri bhuku Muchina Kudzidza kune Hackers 'inonyanya kukosha yekutengesa nzvimbo.
Iri bhuku rinopa yakawanda-chaiyo-yepasi mienzaniso kuita kuti kudzidza muchina wekudzidza kuve nyore uye nekukurumidza pane kupinda zvakadzika mudzidziso yayo yemasvomhu.
Misoro yakafukidzwa mubhuku
- Gadzira isina ruzivo rweBayesian classifier inoongorora zviri nyore zveemail kuti uone kana iri spam.
- Kufanotaura huwandu hwemapeji ekutarisa epamusoro 1,000 mawebhusaiti uchishandisa mutsara regression
- Ongorora nzira dzekugadzirisa nekuyedza kutsemura bhii rakatwasuka.
8. Python Machine Kudzidza neMienzaniso
Iri bhuku, iro rinokubatsira kuti unzwisise uye kugadzira akasiyana-siyana Kudzidza kweMichina, Kudzidza Kwakadzika, uye nzira dzeKuongorora Dhata, ndiro chete rinotarisa paPython semutauro wekuronga.
Iyo inovhara akati wandei ane simba maraibhurari ekushandisa akasiyana Machine Kudzidza algorithms, senge Scikit-Dzidza. Iyo Tensor Flow module inozoshandiswa kukudzidzisa nezve kudzidza kwakadzama.
Pakupedzisira, inoratidza mikana yakawanda yekuongorora data inogona kuwanikwa uchishandisa muchina uye kudzidza kwakadzama.
Inokudzidzisa zvakare akawanda matekiniki anogona kushandiswa kuwedzera kushanda kweiyo modhi yaunogadzira.
Misoro yakafukidzwa mubhuku
- Kudzidza Python uye Kudzidza Kwemuchina: Gwaro reVatangi
- Kuongorora iyo 2 nhau dzedata seti uye Naive Bayes spam email yekuona
- Uchishandisa maSVM, isa misoro yenyaya dzenhau Dzvanya-kuburikidza nekufungidzira uchishandisa algorithms yakavakirwa pamiti.
- Kufanotaura kwekudzvanya-kuburikidza nechiyero uchishandisa logistic regression
- Iko kushandiswa kweregression algorithms kufembera mitengo yemasheya' yepamusoro-soro
9. Python Machine Kudzidza
Bhuku rePython Machine Kudzidza rinotsanangura zvakakosha zvekudzidza kwemichina pamwe nekukosha kwayo mudhijitari. Ibhuku rekudzidza muchina kune vanotanga.
Kuwedzerwa kwakafukidzwa mubhuku kune muchina kudzidza akawanda subfields uye maapplication. Misimboti yePython programming uye maitiro ekutanga neiyo yemahara uye yakavhurika-sosi programming mutauro zvakare yakafukidzwa mubhuku rePython Machine Kudzidza.
Mushure mekupedza bhuku rekudzidza muchina, iwe unozokwanisa kumisikidza akati wandei emabasa ekudzidza muchina uchishandisa Python coding.
Misoro yakafukidzwa mubhuku
- Artificial intelligence basics
- muti wesarudzo
- Kugadziriswa kwemagetsi
- In-deep neural network
- Python programming mutauro zvakakosha
10. Kudzidza Kwemuchina: A Probabilistic Perspective
Kudzidza Muchina: A Probabilistic Perspective ibhuku rinosekesa rekudzidza muchina rinoratidza nostalgic mavara emifananidzo uye anoshanda, chaiwo-enyika mienzaniso kubva kuzvidzidzo zvakaita sebiology, kuona komputa, marobhoti, uye kugadzirisa zvinyorwa.
Iyo izere neyakajairika prose uye pseudocode yeakakosha algorithms. Kudzidza Muchina: A Probabilistic Perspective, kusiyana nemamwe mabhuku ekudzidza muchina anoratidzwa muchimiro chebhuku rekubika uye anotsanangura maitiro akasiyana-siyana eheuristic, anotarisa pane yakamisikidzwa modhi-yakavakirwa maitiro.
Inodoma ma ml modhi ichishandisa mifananidzo inomiririra nenzira yakajeka uye inonzwisisika. Zvichienderana nemaitiro akabatana, anogoneka, bhuku rino rinopa sumo yakazara uye yakazvimiririra yenzvimbo yekudzidza muchina.
Izvo zvirimo zvese zvakapamhama uye zvakadzika, zvinosanganisira yakakosha kumashure zvinyorwa pamisoro yakadai se mukana, optimization, uye mutsara algebra, pamwe nenhaurirano yekufambira mberi kwechizvino-zvino munzvimbo yakadai semamiriro asina kujairika minda, L1 kugadzika, uye kudzidza kwakadzama.
Iri bhuku rakanyorwa nemutauro wakajairika, unotaurika, une pseudo-code yeakakosha algorithms.
Misoro yakafukidzwa mubhuku
- Probability
- Kudzidza zvakadzika
- L1 kugadzirisa
- Optimization
- Kugadziriswa kwemavara
- Computer Vision application
- Robotics application
11. The Elements of Statistical Learning
Nekuda kwechimiro chayo chepfungwa uye akasiyana ezvidzidzo, ichi muchina kudzidza bhuku rinowanzo kubvumwa mumunda.
Iri bhuku rinogona kushandiswa sereferensi kune chero ani zvake anoda kubhurasha pamusoro pemisoro yakaita seneural network uye matekiniki ekuyedza pamwe chete nekusuma kuri nyore pakudzidza muchina.
Iri bhuku rinosundidzira muverengi kuti aite zviedzo zvake uye kuferefeta nguva dzese, zvichiita kuti ive yakakosha pakuvandudza hunyanzvi uye kuda kuziva kunodiwa kuti ufambire mberi mumuchina wekudzidza kana basa.
Icho chishandiso chakakosha kune vanoverengera uye chero ani zvake anofarira mugodhi wedata mubhizinesi kana sainzi. Ita shuwa kuti wanzwisisa linear algebra zvishoma usati watanga bhuku rino.
Misoro yakafukidzwa mubhuku
- Kudzidza kwakatariswa (kufanotaura) kune kudzidza kusingatarisirwe
- Neural network
- Tsigira mavheti emuchina
- Kuronga miti
- Kuwedzera algorithms
12. Kuzivikanwa kwePateni uye Kudzidza Kwemuchina
Nyika dzekuzivikanwa kwemaitiro uye kudzidza muchina zvinogona kunyatsoongororwa mubhuku rino. Nzira yeBayesian yekuzivikanwa kwepateni yakatanga kuratidzwa mubhuku rino.
Uyezve, bhuku rinoongorora zvidzidzo zvinonetsa zvinoda nzwisiso yekushanda yemultivariate, data sainzi, uye yakakosha mutsara algebra.
Pakudzidza kwemichina uye zvingangoitika, bhuku rereferensi rinopa zvitsauko zvine zvishoma nezvishoma kuoma mazinga ekuomarara zvichienderana nemaitiro mumaseti. Mienzaniso yakapfava inopiwa pamberi pesumo yakajairika yekuzivikanwa kwepateni.
Iri bhuku rinopa hunyanzvi hwekufungidzira, izvo zvinobvumira kukurumidza kufungidzira mumamiriro ezvinhu apo mhinduro chaiyo isingaite. Iko hakuna mamwe mabhuku anoshandisa graphical modhi kutsanangura mukana wekugovera, asi zvinodaro.
Misoro yakafukidzwa mubhuku
- Bayesian nzira
- Anenge inference algorithms
- Mitsva mitsva yakavakirwa pamakernels
- Nhanganyaya kune basic probability theory
- Nhanganyaya yekuzivikanwa kwepateni uye kudzidza muchina
13. Zvinokosha zveMuchina Kudzidza kubva kuPredictive Data Analytics
Kana iwe wakagona izvo zvakakosha zvekudzidza muchina uye uchida kuenderera mberi kune yekufungidzira data analytics, rino ibhuku rako !!! Nekutsvaga mapatani kubva kumaseti makuru, Kudzidza kweMuchina kunogona kushandiswa kugadzira mhando dzekufungidzira.
Bhuku rino rinoongorora kushandiswa kweML kushandiswa Predictive Data Analytics hwakadzama, kusanganisira zvese misimboti yedzidziso nemienzaniso chaiyo.
Kunyangwe nenyaya yekuti zita rekuti "Zvakakosha zveMuchina Kudzidza kwePredictive Data Analytics" rine muromo, bhuku rino rinotsanangura rwendo rwePredictive Data Analytics kubva kudata kuenda kunjere kusvika kumagumo.
Inokurukurawo nzira ina dzekudzidza muchina: ruzivo-rwakavakirwa pakudzidza, kufanana-kwakavakirwa kudzidza, mukana-wakavakirwa kudzidza, uye kukanganisa-kwakavakirwa kudzidza, imwe neimwe iine isiri-hunyanzvi yepfungwa tsananguro inoteverwa nemasvomhu modhi uye algorithms ine mienzaniso.
Misoro Yakafukidzwa mubhuku
- Kudzidza kwakavakirwa paruzivo
- Kufanana-kwakavakirwa kudzidza
- Probability-based learning
- Kudzidza kwakavakirwa pazvikanganiso
14. Applied Predictive Modeling
Applied Predictive Modeling inoongorora maitiro ese ekufungidzira ekuenzanisira, kutanga nezvikamu zvakakosha zvekugadzirisa data, kupatsanura data, uye modhi tuning nheyo.
Basa racho rinozopa tsananguro dzakajeka dzeakasiyana akajairwa uye achangoburwa kudzoreredza uye kupatsanura maitiro, aine tarisiro yekuratidza uye kugadzirisa chaiyo-yepasirese data matambudziko.
Nhungamiro inoratidza zvikamu zvose zvemaitiro ekuenzanisira nemaoko akati wandei, mienzaniso yepasirese, uye chitsauko chimwe nechimwe chinosanganisira yakazara R kodhi padanho rega rega rekuita.
Iri vhoriyamu yezvinangwa zvakawanda inogona kushandiswa senge sumo yekufungidzira mamodheru uye maitiro ese ekuenzanisira, segwara rereferenzi kune varapi, kana sechinyorwa chepamberi undergraduate kana vakapedza dhigirii level yekufungidzira modhi.
Misoro yakafukidzwa mubhuku
- Technical regression
- Classification technique
- Complex ML algorithms
15. Kudzidza Kwemuchina: Iyo Art uye Sainzi yeAlgorithms Iyo Inonzwisisa Ye data
Kana iwe uri wepakati kana nyanzvi pakudzidza muchina uye uchida kudzokera “kunezvakakosha,” bhuku iri nderenyu! Inobhadhara chikwereti chakazara kuMuchina Kudzidza kwakakura kuomarara uye kudzika asi usingamborasikirwe nekuona kwayo kubatanidza nheyo (chaizvo zvakaitwa!).
Kudzidza Kwemushini: Iyo Art uye Sainzi yeAlgorithms inosanganisira akati wandei nyaya dzekuwedzera kuomarara, pamwe nemienzaniso yakawanda nemifananidzo (kuchengeta zvinhu zvinonakidza!).
Iri bhuku zvakare rinofukidza huwandu hwakasiyana hwehuroyi, geometric, uye manhamba modhi, pamwe neakaomesesa uye novel zvidzidzo senge matrix factorization uye ROC kuongororwa.
Misoro yakafukidzwa mubhuku
- Inorerutsa muchina kudzidza algorithms
- Muenzaniso une musoro
- Geometric muenzaniso
- Statistical modhi
- ROC kuongororwa
16. Kuchera Dhata: Inoshanda Muchina Kudzidza Zvishandiso & Matekiniki
Tichishandisa nzira kubva pakudzidza kwemasisitimu edatabase, kudzidza kwemichina, uye nhamba, nzira dzekuchera data dzinotigonesa kuwana mapatani muhuwandu hwakawanda hwedata.
Iwe unofanirwa kuwana bhuku reData Mining: Practical Machine Learning Tools uye Techniques kana iwe uchida kudzidza nzira dzekuchera data kunyanya kana kuronga kudzidza kudzidza muchina mune zvese.
Iro bhuku rakanakisa pakudzidza kwemichina rinotarisa zvakanyanya padivi raro rehunyanzvi. Iyo inoenderera mberi mune zvemuchina kudzidza hunyanzvi hunyanzvi, uye marongero ekuunganidza data uye kushandisa akasiyana ekuisa uye zvinobuda kutonga mhedzisiro.
Misoro yakafukidzwa mubhuku
- Linear modhi
- Clustering
- Statistical modeling
- Kufanotaura kuita
- Kuenzanisa nzira dzekuchera data
- Kudzidza kwakavakirwa pamuenzaniso
- Kumiririra ruzivo & masumbu
- Maitiro echinyakare uye emazuva ano ekuchera data
17. Python yeData Analysis
Iko kugona kuongorora iyo data inoshandiswa mukudzidza muchina ndihwo hunyanzvi hwakakosha hunofanirwa kuve nesainzi yedata. Usati wagadzira modhi yeML inoburitsa fungidziro chaiyo, ruzhinji rwebasa rako rinosanganisira kubata, kugadzirisa, kuchenesa, uye kuongorora data.
Iwe unofanirwa kujairana nemitauro yekuronga sePandas, NumPy, Ipython, uye nevamwe kuitira kuti uite ongororo yedata.
Kana iwe uchida kushanda mune data sainzi kana kudzidza muchina, unofanirwa kuve nehunyanzvi hwekushandisa data.
Iwe unofanirwa kuverenga bhuku Python yeData Analysis mune iyi kesi.
Misoro yakafukidzwa mubhuku
- Essential MaLibhurari ePython
- Advanced Pandas
- Data Analysis Mienzaniso
- Data Kuchenesa uye Kugadzirira
- Masvomhu uye Statistical Nzira
- Kupfupikisa uye Computing Descriptive Statistics
18. Kugadzirisa Mutauro Wechisikigo nePython
Hwaro hwemakina ekudzidza masisitimu kugadzirwa kwemutauro wechisikigo.
Bhuku rinonzi Natural Language Processing nePython rinokuraira mashandisiro eNLTK, muunganidzwa unofarirwa zvakanaka wemamodule ePython uye maturusi ekufananidzira uye manhamba echisikigo emutauro weChirungu neNLP zvakazara.
The Natural Language Processing nePython bhuku rinopa anoshanda Python routines anoratidza NLP muchidimbu, pachena nzira.
Vaverengi vanokwanisa kuwana dhatabheti rakanyatsotsanangurwa rekubata nedata isina kurongeka, chimiro chemutauro-mavara, uye zvimwe zvakanangana neNLP.
Misoro yakafukidzwa mubhuku
- Mutauro wevanhu unoshanda sei?
- Linguistic data zvimiro
- Natural Language Toolkit (NLTK)
- Parsing uye semantic kuongorora
- Zvinyorwa zvemitauro zvakakurumbira
- Batanidza maitiro kubva chakagadzirwa njere uye linguistics
19. Programming Collective Intelligence
Iyo Programming Collective Intelligence naToby Segaran, iro rinoonekwa serimwe remabhuku makuru ekutanga kunzwisisa muchina kudzidza, rakanyorwa muna 2007, makore sainzi yedata uye kudzidza kwemichina isati yawana chinzvimbo chavo chazvino sekutungamira nzira dzehunyanzvi.
Bhuku rinoshandisa Python senzira yekuparadzira hunyanzvi hwayo kune vateereri vayo. Iyo Programming Collective Intelligence yakawanda yegwaro rekushandisa ml pane kuri sumo yekudzidza muchina.
Iri bhuku rinopa ruzivo rwekugadzira inoshanda ML algorithms yekuunganidza data kubva kumaapps, hurongwa hwekutora data kubva kumawebhusaiti, uye kuwedzera data rakaunganidzwa.
Chitsauko chega chega chinosanganisira zviitiko zvekuwedzera maalgorithms akakurukurwa uye kuwedzera kukosha kwawo.
Misoro yakafukidzwa mubhuku
- Bayesian kusefa
- Tsigira mavheti emuchina
- Tsvaga injini algorithms
- Nzira dzekufungidzira
- Maitiro ekusefa anodyidzana
- Kwete-negative matrix factorization
- Evolving intelligence yekugadzirisa matambudziko
- Nzira dzekuona mapoka kana mapatani
20. Kudzidza Kwakadzika (Adaptive Computation uye Machine Learning Series)
Sezvatinoziva tese, kudzidza kwakadzama imhando yakavandudzwa yekudzidza kwemichina inoita kuti makomputa adzidze kubva mukuita kwekare uye huwandu hukuru hwe data.
Paunenge uchishandisa michina yekudzidza matekiniki, iwe unofanirwa zvakare kutaurirana nezvakadzika misimboti yekudzidza. Iri bhuku, iro rinoonekwa seBhaibheri rekudzidza kwakadzama, richabatsira zvakanyanya mumamiriro ezvinhu aya.
Nyanzvi nhatu dzekudzidza dzakadzama dzinovhara misoro yakaoma kwazvo izere nemasvomhu uye yakadzika generative modhi mubhuku rino.
Kupa hwaro hwemasvomhu uye hwepfungwa, basa rinokurukura mazano akakodzera mumutsara algebra, mukana wedzidziso, dzidziso yeruzivo, kuverenga kwenhamba, uye kudzidza muchina.
Inoongorora mashandisirwo akaita semagadzirirwo emutauro wechisikigo, kuzivikanwa kwekutaura, kuona komputa, masisitimu ekurudziro epamhepo, bioinformatics, uye videogames uye inotsanangura nzira dzakadzama dzekudzidza dzinoshandiswa nevarapi veindasitiri, senge yakadzika feedforward network, kudzoreredza, uye optimization algorithms, convolutional network, uye inoshanda nzira. .
Misoro yakafukidzwa mubhuku
- Numerical Computation
- Tsvagiridzo Yekudzidza Yakadzama
- Computer Vision matekiniki
- Deep Feedforward Networks
- Optimization for Training Deep Models
- Runoshanda Methodology
- Tsvagiridzo Yekudzidza Yakadzama
mhedziso
Iwo makumi maviri epamusoro ekudzidza mabhuku mabhuku akapfupikiswa mune iyo runyorwa, iyo yaunogona kushandisa kufambira mberi muchina kudzidza munzira yaunoda.
Iwe unozokwanisa kugadzira hwaro hwakasimba muhunyanzvi hwekudzidza muchina uye raibhurari yereferensi yaunogona kushandisa kazhinji uchishanda munzvimbo iyi kana ukaverenga akasiyana emabhuku aya.
Iwe unozofemerwa kuti urambe uchidzidza, kuita zvirinani, uye kuve nemhedzisiro kunyangwe ukangoverenga bhuku rimwe chete.
Kana iwe wagadzirira uye uchikwanisa kugadzira yako wega muchina kudzidza algorithms, ramba uchifunga kuti data rakakosha mukubudirira kweprojekiti yako.
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