Teburin Abubuwan Ciki[Boye][Nuna]
A cikin al'ummar yau, kimiyyar bayanai na da matukar muhimmanci!
Don haka cewa masanin kimiyyar bayanai ya sami kambin "Ayyukan Jima'i na Karni na Ashirin da Farko," duk da babu wanda ke tsammanin ayyukan geeky su zama sexy!
Koyaya, saboda girman mahimmancin bayanai, Kimiyyar Bayanai ta shahara sosai a yanzu.
Python, tare da ƙididdigar ƙididdiga, ƙirar bayanai, da iya karantawa, yana ɗaya daga cikin mafi kyau yarukan shirye-shirye don fitar da ƙima daga wannan bayanan.
Python ba ya gushewa yana mamakin masu shirye-shiryen sa idan ana maganar shawo kan kalubalen kimiyyar bayanai. Harshen shirye-shirye ne mai girma, mai fa'ida, mai fa'ida, mai fa'ida, mai fa'ida da fa'ida da yawa tare da ƙarin fasali iri-iri.
An tsara Python tare da manyan ɗakunan karatu don kimiyyar bayanai waɗanda masu shirye-shirye ke amfani da su kowace rana don magance matsaloli.
Anan ne mafi kyawun ɗakunan karatu na Python don la'akari:
1. Panda
Pandas kunshin ne da aka ƙera don taimakawa masu haɓakawa a yin aiki tare da bayanan "lakabi" da "dangantaka" a cikin yanayin yanayi. An gina shi a kan manyan bayanai guda biyu: "Series" (mai girma dabam, kama da jerin abubuwa) da "Data Frames" (mai girma biyu, kamar tebur mai ginshiƙai masu yawa).
Pandas yana goyan bayan canza tsarin bayanai zuwa abubuwan DataFrame, ma'amala da bacewar bayanan, ƙara / share ginshiƙai daga DataFrame, ƙididdige fayilolin da suka ɓace, da gani data amfani da histograms ko kwalayen fili.
Hakanan yana ba da kayan aiki da yawa don karantawa da rubuta bayanai tsakanin tsarin bayanan ƙwaƙwalwar ajiya da tsarin fayil da yawa.
A taƙaice, yana da kyau don sarrafa bayanai cikin sauri da sauƙi, tara bayanai, karantawa da rubuta bayanai, da hangen nesa na bayanai. Lokacin ƙirƙirar aikin kimiyyar bayanai, koyaushe za ku yi amfani da ɗakin karatu na dabba Pandas don sarrafa da bincika bayanan ku.
2. Lambu
NumPy (Python lambobi) kayan aiki ne mai ban sha'awa don yin lissafin kimiyya da na asali da nagartaccen ayyukan tsararru.
Laburaren yana ba da wasu fasaloli masu taimako don aiki tare da n-arrays da matrices a Python.
Yana sauƙaƙa aiwatar da jeri-jeri waɗanda ke ƙunshe da ƙimar nau'in bayanai iri ɗaya da aiwatar da ayyukan ƙididdiga akan tsararraki (ciki har da vectorization). A zahiri, yin amfani da nau'in tsararrun NumPy don sarrafa ayyukan lissafin yana inganta aiki kuma yana rage lokacin aiwatarwa.
Taimako don tsararraki masu yawa don ayyukan lissafi da ma'ana shine ainihin fasalin ɗakin karatu. Ana iya amfani da ayyukan NumPy don fiddawa, tsarawa, sake fasalin, da kuma sadar da abubuwan gani da raƙuman sauti azaman tsararrun lambobi na gaske.
3. matplotlib
A cikin duniyar Python, Matplotlib yana ɗaya daga cikin ɗakunan karatu da aka fi amfani da su. Ana amfani da shi don samar da a tsaye, raye-raye, da abubuwan gani na bayanai. Matplotlib yana da zaɓuɓɓukan tsarawa da gyare-gyare da yawa.
Yin amfani da histograms, masu shirye-shirye na iya warwatsawa, tweak, da shirya hotuna. Laburaren buɗe tushen yana ba da API mai dogaro da abu don ƙara filaye cikin shirye-shirye.
Lokacin amfani da wannan ɗakin karatu don samar da hadaddun abubuwan gani, duk da haka, masu haɓakawa dole ne su rubuta lamba fiye da na al'ada.
Yana da kyau a lura cewa shahararrun ɗakunan karatu na charting suna rayuwa tare da Matplotlib ba tare da wata matsala ba.
Daga cikin wasu abubuwa, ana amfani da shi a cikin rubutun Python, Python da harsashi na IPython, littattafan rubutu na Jupyter, da web aikace-aikace sabobin.
Makirci, ginshiƙi mashaya, kek Charts, histograms, watsa shirye-shirye, ginshiƙi kurakurai, ikon spectra, stemplots, da kowane irin ginshiƙi ginshiƙi duk za a iya ƙirƙira da shi.
4. Jirgin ruwa
An gina ɗakin karatu na Seaborn akan Matplotlib. Ana iya amfani da Seaborn don yin hotuna masu ban sha'awa da ƙididdiga fiye da Matplotlib.
Seaborn ya haɗa da haɗaɗɗen bayanan saiti-daidaitacce API don bincika hulɗar tsakanin masu canji da yawa, ban da cikakken goyan baya don ganin bayanan.
Seaborn yana ba da ɗimbin zaɓuka masu ban mamaki don ganin bayanan, gami da hangen nesa-jerin lokaci, makircin haɗin gwiwa, zane-zanen violin, da sauran su.
Yana amfani da taswira na ma'ana da tara ƙididdiga don samar da hangen nesa mai ba da labari tare da zurfin fahimta. Ya haɗa da adadin tsare-tsare na yau da kullun na saitin bayanai waɗanda ke aiki tare da firam ɗin bayanai da tsararraki waɗanda suka haɗa da saitin bayanai gabaɗaya.
Hannun bayanan sa na iya haɗawa da taswirar mashaya, ginshiƙi kek, histograms, tarwatsawa, sigogin kuskure, da sauran zane-zane. Wannan ɗakin karatu na gani na bayanan Python kuma ya haɗa da kayan aiki don zaɓar palette mai launi, waɗanda ke taimakawa wajen gano abubuwan da ke faruwa a cikin saitin bayanai.
5. Scikit-koya
Scikit-learn shine mafi girman ɗakin karatu na Python don ƙirar bayanai da ƙimar ƙima. Yana ɗaya daga cikin ɗakunan karatu na Python masu taimako. Yana da ɗimbin iyakoki da aka ƙera don manufar ƙirar ƙira kawai.
Ya haɗa da duk Algorithms na Koyon Injin da ba a Kula da su ba, da cikakkun ƙayyadaddun Ƙirar Koyo da Ƙarfafa ayyukan Koyan Injin.
Masana kimiyyar bayanai suna amfani da shi don yin aikin yau da kullun injin inji da ayyukan hakar ma'adinan bayanai kamar tari, koma baya, zaɓin samfuri, rage girman girma, da rarrabuwa. Hakanan yana zuwa tare da cikakkun takardu kuma yana yin abin sha'awa.
Za a iya amfani da Scikit-learn don ƙirƙirar nau'ikan nau'ikan Koyon Injin da ba a kula da su kamar Rarrabewa, Regression, Injin Tallafawa Vector, Dazuzzukan dazuzzuka, Maƙwabta Mafi kusa, Naive Bayes, Bishiyoyin yanke shawara, Tari, da sauransu.
Laburaren koyon injin Python ya ƙunshi nau'ikan kayan aiki masu sauƙi-duk da haka masu inganci don aiwatar da nazarin bayanai da ayyukan hakar ma'adinai.
Don ƙarin karatu, ga jagoranmu akan Scikit-koyi.
6. XGBoost
XGBoost kayan aiki ne na haɓaka gradient da aka rarraba don saurin gudu, sassauƙa, da ɗaukar nauyi. Don haɓaka algorithms na ML, yana amfani da tsarin haɓakar Gradient. XGBoost fasaha ce mai sauri kuma daidaitaccen tsarin haɓaka itace mai daidaitawa wanda zai iya magance ɗimbin matsalolin kimiyyar bayanai.
Yin amfani da tsarin haɓakawa na Gradient, ana iya amfani da wannan ɗakin karatu don ƙirƙirar algorithms na koyon inji.
Ya haɗa da haɓakar itace mai kama da juna, wanda ke taimaka wa ƙungiyoyi don warware batutuwan kimiyyar bayanai iri-iri. Wata fa'ida ita ce masu haɓakawa na iya amfani da lamba ɗaya don Hadoop, SGE, da MPI.
Hakanan abin dogaro ne a cikin yanayin rarrabawa da ƙaƙƙarfan yanayi.
7. Maganin motsa jiki
TensorFlow kyauta ce ta buɗe tushen tushen AI dandamali tare da manyan kayan aiki, ɗakunan karatu, da albarkatu. TensorFlow dole ne ya saba da duk wanda ke aiki akai ayyukan koyon inji a cikin Python.
Kayan aikin lissafi ne na buɗaɗɗen tushe don ƙididdige ƙididdiga ta amfani da jadawali kwararar bayanai waɗanda Google ya haɓaka. Ƙididdigar jadawali suna nuna tsarin tsarin lissafi a cikin jadawali na gudanawar bayanai na TensorFlow.
Gefen jadawali, a gefe guda, su ne tsararrun bayanai masu yawa, wanda kuma aka sani da tenors, waɗanda ke gudana tsakanin nodes na cibiyar sadarwa. Yana ƙyale masu shirye-shirye su rarraba aiki tsakanin ɗaya ko fiye CPUs ko GPUs akan tebur, na'urar hannu, ko uwar garken ba tare da canza lamba ba.
An haɓaka TensorFlow a cikin C da C++. Tare da TensorFlow, zaku iya ƙira kawai kuma jirgin kasa Machine Learning samfura masu amfani da APIs masu girma kamar Keras.
Hakanan yana da digiri na abstraction da yawa, yana ba ku damar zaɓar mafi kyawun bayani don ƙirar ku. TensorFlow kuma yana ba ku damar tura samfuran Learning Machine zuwa gajimare, mai bincike, ko na'urar ku.
Shine kayan aiki mafi inganci don ayyuka kamar tantance abu, fahimtar magana, da sauran su. Yana taimakawa wajen haɓaka na wucin gadi neural networks wanda dole ne yayi mu'amala da tushen bayanai da yawa.
Anan ga jagorarmu mai sauri akan TensorFlow don ƙarin karatu.
8. Keras
Keras kyauta ne kuma bude tushen Cibiyar jijiyoyi ta tushen Python kayan aiki don basirar wucin gadi, zurfin koyo, da ayyukan kimiyyar bayanai. Hakanan ana amfani da hanyoyin sadarwar jijiya a cikin Kimiyyar Bayanai don fassara bayanan lura (hotuna ko sauti).
Tarin kayan aiki ne don ƙirƙirar ƙira, zayyana bayanai, da tantance bayanai. Hakanan ya haɗa da bayanan da aka riga aka yi wa lakabin da za a iya shigo da su cikin sauri da lodawa.
Yana da sauƙi don amfani, mai yawa, kuma manufa don bincike bincike. Bugu da ƙari, yana ba ku damar ƙirƙirar cikakken haɗin kai, juzu'i, haɗawa, maimaitawa, haɗawa, da sauran nau'ikan hanyoyin sadarwa na Jijiya.
Ana iya haɗa waɗannan samfuran don gina Cikakkun Cibiyar Sadarwar Jijiya na Jijiya don manyan bayanai da batutuwa. Babban ɗakin karatu ne don ƙirƙira da ƙirƙirar hanyoyin sadarwa na jijiyoyi.
Yana da sauƙi don amfani kuma yana ba masu haɓaka sassauci da yawa. Keras yana da kasala idan aka kwatanta da sauran fakitin koyon injin Python.
Wannan saboda da farko yana haifar da jadawali mai amfani da kayan aikin baya sannan kuma yayi amfani da shi don gudanar da ayyuka. Keras yana da matuƙar bayyanawa da daidaitawa idan ana batun yin sabon bincike.
9. PyTorch
PyTorch sanannen fakitin Python ne don zurfin ilmantarwa da kuma koyon inji. Software ce ta tushen tushen tushen ilimin kimiyyar kwamfuta don aiwatar da zurfafa koyo da hanyoyin sadarwa na jijiya akan manyan bayanai.
Facebook yana yin amfani da yawa na wannan kayan aikin don ƙirƙirar hanyoyin sadarwa na jijiyoyi waɗanda ke taimakawa a cikin ayyuka kamar tantance fuska da sanya alama ta atomatik.
PyTorch dandamali ne na masana kimiyyar bayanai waɗanda ke son kammala ayyukan koyo mai zurfi cikin sauri. Kayan aikin yana ba da damar lissafin tensor don yin aiki tare da haɓaka GPU.
Hakanan ana amfani da ita don wasu abubuwa, gami da gina hanyoyin sadarwar lissafi masu ƙarfi da ƙididdige matakan gradients ta atomatik.
Abin farin ciki, PyTorch wani fakiti ne mai ban sha'awa wanda ke ba masu haɓakawa damar canzawa cikin sauƙi daga ka'idar da bincike zuwa horo da haɓakawa idan aka zo ga koyon injin da bincike mai zurfi don ba da matsakaicin sassauci da sauri.
10. NLTK
NLTK (Kayan Aikin Harshen Halitta) sanannen kunshin Python ne don masana kimiyyar bayanai. Ana iya yin tambarin rubutu, alamar alama, fahimtar ma'anar fassarar, da sauran ayyuka masu alaƙa da sarrafa harshe na halitta tare da NLTK.
Hakanan ana iya amfani da NLTK don kammala ƙarin hadaddun AI (Artificial Intelligence) ayyuka. An ƙirƙiri NLTK asali don tallafawa nau'ikan koyarwar AI da na'ura daban-daban, kamar ƙirar harshe da ka'idar fahimi.
A halin yanzu yana tuƙi AI algorithm da haɓaka ƙirar ƙira a cikin ainihin duniya. An karɓe shi sosai don amfani da shi azaman kayan aikin koyarwa da kuma kayan aikin binciken mutum ɗaya, baya ga amfani da shi azaman dandamali don samfuri da haɓaka tsarin bincike.
Rarrabawa, fassarawa, fahimtar ma'anar tatsuniyoyi, tsinkaya, sanya alama, da alamar alama duk ana samun goyan baya.
Kammalawa
Wannan ya ƙare manyan ɗakunan karatu na Python guda goma don kimiyyar bayanai. Ana sabunta ɗakunan karatu na kimiyyar bayanai na Python akai-akai yayin da ilimin kimiyyar bayanai da na'ura ke ƙara shahara.
Akwai dakunan karatu na Python da yawa don Kimiyyar Bayanai, kuma zaɓin mai amfani ya fi dacewa da nau'in aikin da suke aiki akai.
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