ʻIkepili ʻIke no nā poʻe hoʻomaka
1. Nānā Manaʻo
E nānā i ka hoʻokō ʻana i ka papahana ʻIkepili ʻIkepili me ka hoʻohana ʻana i ke code kumu − .
ʻO ka nānā ʻana i ka manaʻo ka nānā ʻana i nā huaʻōlelo e hoʻoholo ai i nā manaʻo a me nā manaʻo, hiki ke maikaʻi a maikaʻi ʻole paha. He ʻano hoʻohālikelike kēia e hiki ai i nā papa ke lilo i binary (maikaʻi a maikaʻi ʻole) a i ʻole plural (hauʻoli, huhū, kaumaha, ʻino...). E hoʻokō mākou i kēia papahana ʻEpekema ʻIkepili ma R a e hoʻohana mākou i ka ʻikepili ma ka pūʻolo "janeaustenR". E hoʻohana mākou i nā puke wehewehe ʻōlelo maʻamau e like me AFINN, bing a me loughran, e hana i kahi hui i loko, a i ka hopena e hana mākou i kahi huaʻōlelo e hōʻike i ka hopena.
'Ōlelo: R
ʻIkepili/Poloke: janeaustenR
Ua unuhi ʻia ka ʻatikala me ke kākoʻo o EDISON Software, ʻo ia , a me .
2. ʻIke Nuhou Hoʻopunipuni
E lawe i kāu mau mākau i ka pae aʻe ma o ka hana ʻana i kahi papahana ʻepekema Data no nā poʻe hoʻomaka - .
ʻO ka lono hoʻopunipuni ka ʻike wahaheʻe i hoʻolaha ʻia ma o ka media media a me nā ʻoihana pūnaewele ʻē aʻe e hoʻokō i nā pahuhopu politika. Ma kēia manaʻo papahana ʻEpekema Data, e hoʻohana mākou iā Python e kūkulu i kahi kumu hoʻohālike e hiki ke hoʻoholo pololei inā he moʻolelo maoli a hoʻopunipuni paha. E hana mākou i kahi TfidfVectorizer a hoʻohana i kahi PassiveAggressiveClassifier e hoʻokaʻawale i ka nūhou i ka "ʻoiaʻiʻo" a me ka "hoʻopunipuni". E hoʻohana mākou i kahi ʻikepili o ke ʻano 7796 × 4 a holo i nā mea āpau ma Jupyter Lab.
'Ōlelo: Python
ʻIkepili/Poloke: news.csv
3. ʻIke ʻana i ka maʻi o Parkinson
E neʻe i mua me kāu Manaʻo Papahana ʻEpekema Data - .
Ua hoʻomaka mākou e hoʻohana i ka Data Science e hoʻomaikaʻi i ka mālama olakino a me nā lawelawe - inā hiki iā mākou ke wānana i kahi maʻi i ka wā mua, a laila e loaʻa iā mākou nā pōmaikaʻi he nui. No laila, i kēia manaʻo papahana ʻIke ʻIkepili, e aʻo mākou pehea e ʻike ai i ka maʻi o Parkinson me ka hoʻohana ʻana iā Python. He maʻi maʻi neurodegenerative, holomua o ka ʻōnaehana nūnū waena e pili ana i ka neʻe a hoʻoulu i ka haʻalulu a me ka ʻoʻoleʻa. Hoʻopilikia ia i nā neurons hana dopamine i loko o ka lolo, a i kēlā me kēia makahiki, pili ia ma mua o 1 miliona mau kānaka ma India.
'Ōlelo: Python
ʻIkepili/Poloke: UCI ML Parkinsons waihona
Nā papahana ʻepekema ʻikepili o ka paʻakikī waena
4. ʻIke Manaʻo ʻŌlelo
E nānā i ka hoʻokō piha ʻana o ka papahana laʻana ʻIke ʻIkepili − .
E aʻo kākou i ka hoʻohana ʻana i nā waihona like ʻole. Ke hoʻohana nei kēia papahana ʻIkepili ʻIkepili i ka librosa no ka ʻike ʻōlelo. ʻO SER ke kaʻina hana o ka ʻike ʻana i nā manaʻo kanaka a me nā kūlana pili mai ka ʻōlelo. No ka mea, hoʻohana mākou i ka leo a me ka pitch e hōʻike i ka manaʻo me ko mākou leo, pili ka SER. Akā, no ka mea, pili nā manaʻo, he hana paʻakikī ka hoʻopuka leo. E hoʻohana mākou i nā hana mfcc, chroma a me mel a hoʻohana i ka waihona RAVDESS no ka ʻike ʻana i ka manaʻo. E hana mākou i kahi papa helu MLPC no kēia kumu hoʻohālike.
'Ōlelo: Python
ʻIkepili/Poloke: RAVDESS waihona
5. Ke kāne a me ka ʻike makahiki
E hoʻohauʻoli i nā mea hana me ka papahana ʻepekema Data hou loa - .
He ʻepekema ʻikepili hoihoi kēia me Python. Ke hoʻohana nei i hoʻokahi kiʻi wale nō, e aʻo ʻoe e wānana i ke kāne a me ka makahiki o ke kanaka. Ma kēia e hoʻolauna mākou iā ʻoe i Computer Vision a me kāna mau loina. E kūkulu mākou a e hoʻohana i nā hiʻohiʻona i aʻo ʻia e Tal Hassner a me Gil Levy ma ka papa helu Adience. Ma ke ala e hoʻohana mākou i kekahi mau faila .pb, .pbtxt, .prototxt a me .caffemodel.
'Ōlelo: Python
ʻIkepili/Poloke: Adience
6. Uber Ikepili Ikepili
E nānā i ka hoʻokō ʻana o ka papahana ʻIkepili ʻIkepili me ka code kumu − .
He papahana ʻike ʻikepili kēia me ggplot2 kahi e hoʻohana ai mākou iā R a me kāna mau hale waihona puke a nānā i nā ʻāpana like ʻole. E hoʻohana mākou i ka ʻikepili Uber Pickups New York City a hana i nā hiʻohiʻona no nā papa manawa like ʻole o ka makahiki. Hōʻike kēia iā mākou i ka hopena o ka manawa i ka huakaʻi o nā mea kūʻai aku.
'Ōlelo: R
ʻIkepili/Poloke: Uber Pickups ma New York City dataset
7. Keaukaha Drowsiness ike
E hoʻomaikaʻi i kāu mau mākau ma ka hana ʻana ma ka Top Data Science Project - .
He pōʻino loa ka hoʻokele hiamoe, a kokoke i hoʻokahi kaukani ulia i kēlā me kēia makahiki ma muli o ka hiamoe ʻana o nā mea hoʻokele i ke kaʻa. Ma kēia papahana Python, e hana mākou i kahi ʻōnaehana hiki ke ʻike i nā mea hoʻokele hiamoe a hoʻomaopopo pū iā lākou me kahi hōʻailona leo.
Hoʻokō ʻia kēia papahana me Keras a me OpenCV. E hoʻohana mākou i ka OpenCV no ka ʻike maka a me ka maka a me Keras mākou e hoʻokaʻawale i ke kūlana o ka maka (Open a i ʻole Paʻa) me ka hoʻohana ʻana i nā ʻenehana neural hohonu.
8. ʻO Chatbot
E hana i kahi Chatbot me Python a hele i mua i kāu ʻoihana - .
ʻO Chatbots kahi ʻāpana o ka ʻoihana. Nui nā ʻoihana e hāʻawi i nā lawelawe i kā lākou mea kūʻai aku a nui ka manpower, ka manawa a me ka hoʻoikaika e lawelawe iā lākou. Hiki i nā Chatbots ke hoʻokaʻawale i ka hapa nui o kāu mea kūʻai aku ma ka pane ʻana i kekahi mau nīnau maʻamau a nā mea kūʻai aku e nīnau ai. ʻElua mau ʻano chatbots: Domain-specific a Open-domain. Hoʻohana pinepine ʻia kahi chatbot domain-specific e hoʻoponopono i kahi pilikia kūikawā. No laila, pono ʻoe e hoʻopili iā ia e hana maikaʻi i kāu kahua. Hiki ke nīnau ʻia nā chatbots open-domain i kekahi mau nīnau, no laila pono ke aʻo ʻana iā lākou i ka nui o ka ʻikepili.
pūʻulu ʻikepili: Manaʻo i ka faila json
'Ōlelo: Python
Nā papahana ʻepekema ʻikepili kiʻekiʻe
9. Mea Hana Kiʻi Kiʻi
E nānā i ka hoʻokō piha ʻana o ka papahana me ka code kumu − .
He hana maʻalahi ka wehewehe ʻana i nā mea i loko o ke kiʻi, akā no nā kamepiula, ʻo ke kiʻi he pūʻulu helu wale nō e hōʻike ana i ka waiwai kala o kēlā me kēia pika. He hana paʻakikī kēia no nā kamepiula. ʻO ka hoʻomaopopo ʻana i ka mea i loko o kahi kiʻi a laila hana i kahi wehewehe ma ka ʻōlelo kūlohelohe (e like me ka ʻōlelo Pelekania) kekahi hana paʻakikī. Ke hoʻohana nei kēia papahana i nā ʻenehana hoʻonaʻauao hohonu kahi e hoʻokō ai mākou i kahi Convolutional Neural Network (CNN) me kahi Recurrent Neural Network (LSTM) e hana i kahi mea hana wehewehe kiʻi.
pūʻulu ʻikepili: Flickr 8K
'Ōlelo: Python
Papahana: Keras
10. ʻIke Hoʻopunipuni Kāleka ʻaiʻē
E hana maikaʻi i kāu hana ʻana ma kāu manaʻo papahana ʻIkepili − .
I kēia manawa ua hoʻomaka ʻoe e hoʻomaopopo i nā ʻenehana a me nā manaʻo. E neʻe kākou i kekahi mau papahana ʻepekema data holomua. Ma kēia papahana e hoʻohana mākou i ka ʻōlelo R me nā algorithms like , logistic regression, artificial neural networks and gradient boosting classifier. E hoʻohana mākou i kahi papa helu o nā kālepa kāleka e hoʻokaʻawale i nā kālepa kāleka hōʻaiʻē ma ke ʻano he hoʻopunipuni a ʻoiaʻiʻo paha. E koho mākou i nā hiʻohiʻona like ʻole no lākou a kūkulu i nā pihi hana.
'Ōlelo: R
ʻIkepili/Poloke: ʻO ka waihona ʻikepili Kāleka Kūʻai
11. Pūnaehana Manaʻo Kiʻiʻoniʻoni
E aʻo i ka hoʻokō ʻana i ka papahana ʻepekema Data maikaʻi loa me ke code Source -
Ma kēia papahana ʻepekema Data, e hoʻohana mākou iā R e hoʻokō i nā ʻōlelo aʻoaʻo o ke kiʻiʻoniʻoni ma o ke aʻo ʻana i ka mīkini. Hoʻouna ka ʻōnaehana manaʻo i nā manaʻo i nā mea hoʻohana ma o ke kaʻina kānana e pili ana i nā makemake o nā mea hoʻohana ʻē aʻe a me ka mōʻaukala mākaʻikaʻi. Inā makemake ʻo A a me B i ka Home Alone, a makemake ʻo B iā Mean Girls, a laila hiki iā ʻoe ke kuhikuhi iā A - makemake paha lākou. ʻAe kēia i nā mea kūʻai aku e launa pū me ka paepae.
'Ōlelo: R
ʻIkepili/Poloke: MovieLens waihona
12. Māhele Kūʻai
E hoʻohauʻoli i nā mea hana me kahi papahana ʻepekema Data (me ka code kumu) - .
ʻO ka hoʻokaʻawale ʻana o ka mea kūʻai aku he noi kaulana . Ke hoʻohana nei i ka clustering, ʻike nā ʻoihana i nā ʻāpana mea kūʻai aku e kuhikuhi i kahi waihona mea hoʻohana. Hoʻokaʻawale lākou i nā mea kūʻai aku i nā pūʻulu e like me nā hiʻohiʻona maʻamau e like me ke kāne, ka makahiki, nā makemake a me nā hana hoʻolimalima i hiki iā lākou ke kūʻai pono i kā lākou huahana i kēlā me kēia hui. E hoʻohana mākou , a me ka nānā ʻana i ka māhele ʻana ma ke kāne a me ka makahiki. A laila e kālailai mākou i kā lākou mau loaʻa a me nā pae hoʻolimalima makahiki.
'Ōlelo: R
ʻIkepili/Poloke: Mall_Customers dataset
13. Ka Papa Ma'i Kano'i
E nānā i ka hoʻokō piha ʻana o kahi papahana ʻepekema Data ma Python − .
Ke hoʻi mai nei i ka hāʻawi olakino o ka ʻepekema data, e aʻo kākou pehea e ʻike ai i ka maʻi maʻi umauma me ka hoʻohana ʻana iā Python. E hoʻohana mākou i ka IDC_regular dataset no ka ʻike ʻana i ka invasive ductal carcinoma, ke ʻano maʻamau o ka maʻi maʻi umauma. Hoʻoulu ia i loko o nā ʻauamo waiu, e ʻeli ana i loko o ka ʻiʻo o ka umauma fibrous a momona paha ma waho o ke kahawai. Ma kēia manaʻo papahana ʻepekema hōʻiliʻili ʻikepili mākou e hoʻohana ai a me ka hale waihona puke Keras no ka hoʻokaʻawale ʻana.
'Ōlelo: Python
ʻIkepili/Poloke: IDC_mau
14. ʻIke ʻia nā hōʻailona kaʻa
Loaʻa i ka pololei i ka ʻenehana hoʻokele ponoʻī me ka papahana ʻIke ʻIkepili puna hāmama.
He mea koʻikoʻi nā hōʻailona alanui a me nā lula kaʻa no kēlā me kēia kaʻa e pale aku i nā pōʻino. No ka hahai ʻana i ke kānāwai, pono ʻoe e hoʻomaopopo mua i ke ʻano o kahi hōʻailona alanui. Pono ke kanaka e aʻo i nā hōʻailona alanui a pau ma mua o ka hāʻawi ʻia ʻana o ka laikini e hoʻokele i kekahi kaʻa. Akā i kēia manawa ke ulu nei ka nui o nā kaʻa autonomous, a i ka wā e hiki mai ana ʻaʻole e hoʻokele kaʻa ke kanaka i kahi kaʻa kaʻawale. Ma ka papahana ʻike alanui, e aʻo ʻoe pehea e hiki ai i kahi papahana ke ʻike i ke ʻano o nā hōʻailona alanui ma ka lawe ʻana i ke kiʻi i mea hoʻokomo. Hoʻohana ʻia ka ʻikepili German Traffic Sign Recognition Benchmark (GTSRB) e kūkulu i kahi pūnaewele neural hohonu e ʻike ai i ka papa nona kahi hōʻailona kaʻa. Hana mākou i kahi GUI maʻalahi e launa pū me ka noi.
'Ōlelo: Python
pūʻulu ʻikepili: ʻO GTSRB (German Traffic Sign Recognition Benchmark)
Heluhelu hou aku
Source: www.habr.com