Ua pau wau i ka Hāʻawi ʻia kahi papa aʻo mīkini e ke Kulanui ʻo Stanford ma Coursera, a ʻoiai ua nui nā mea i nīnau mai iaʻu ma kahi ākea a pilikino e pili ana iā ia, makemake wau e kikoʻī i kahi mea hou aʻe e pili ana iaʻu a ʻo ka mea e hoʻoholo e hana e ʻike ia i kā lākou e ʻike ai.
He papa manuahi ma ka aʻo ʻana i ka Mīkini, i aʻo ʻia e Andrew Ng. pau kahi inā makemake ʻoe hiki iā ʻoe ke loaʻa kahi palapala hōʻoia e kākoʻo i nā mākau i loaʻa no € 68. Māhele ʻia i ʻekolu pou, wikiō, Exams a i ʻole Quizz a me nā hoʻolālā papahana. Aia ma ka ʻōlelo Pelekania. Loaʻa iā ʻoe nā subtitles i nā ʻōlelo he nui, akā ʻaʻole maikaʻi loa ka Paniolo a i kekahi manawa ua hala ka manawa, ʻoi aku ka maikaʻi inā kau ʻoe iā lākou i ka ʻōlelo Pelekania.
He manaʻo nui ʻole ia. Akā ʻo ia nō paha ke kumu he ala maikaʻi e hoʻomaka ai no ka mea ʻaʻole ʻoe e aʻo i ka mea e hana ai akā no ke aha ʻoe e hana ai.
- I ka manawa e koho ai i kahi algorithm a i ʻole kekahi.
- Pehea e koho ai a wehewehe ai i nā palena ʻokoʻa.
- He aha nā pilikia e hiki ke ala aʻe me nā algorithms a ʻo nā mea nui hoʻi e ana.
Nui kāna algebra a me kekahi calculus, a ʻike, i kaʻu wehewehe ʻana iā ʻoe, ʻaʻole pono ʻoe e hana, ʻaʻole pono ʻoe e hōʻea i kēlā mau hoʻohālikelike, hōʻoia iā lākou, a hoʻololi paha iā lākou, e hoʻopili pono iā lākou. No laila inā ʻaʻole maikaʻi kāu pae o ka makemakika hiki iā ʻoe ke hana i ka papa, akā ʻoiaʻiʻo, hoʻolilo ʻana i mau hola e nānā ana a hoʻolohe nei i nā wikiō kahi e wehewehe ai i kēlā me kēia huaʻōlelo pehea ka hopena a me ke kumu o laila.
Inā ʻaʻole ʻoe e ʻike he aha ia Ke aʻo ʻana i ka mīkini, e ʻōlelo mākou he ʻāpana ia o Artificial Intelligence i hoʻolaʻa ʻia i nā algorithms hana i kēia mau mea āpau mai ka ʻike ʻana i ka mīkini, hoʻonohonoho spam, a pēlā aku.
Ua loli koʻu hihi iaʻu. Ke noʻonoʻo ʻoe e pili ana i kēia mau ʻano pilikia, ua alo ʻoe iā lākou mai kahi kiko o ka papahana, e noʻonoʻo ana e pili ana i nā puka lou, nā kūlana, a me nā hana ʻē aʻe āpau. Nā wānana e pili ana i ka regressions, etc., etc.
Hōʻuluʻulu papa
No laila ma luna o kēia nā ʻāpana nui o ka papa, i māhele ʻia i ʻelua, ka ʻāpana Hoʻokele a me ka ʻāpana Kākoʻo ʻole
Ke aʻo ʻana i luna ʻia
- Ke Ana Hoʻohālike a me ke kumukūʻai
- ʻO ka iho Gradient no ka hoʻoponopono linear
- Regularization
- Pūnaewele Neural
- Hoʻonohonoho ʻana i ka Mīkini Nui a me nā Kernels
- Ka Ikepili Kaha Nui (PCA)
- Hoʻolālā ʻōnaehana Machine Learning
- Kākoʻo i nā mīkini Vector
Ke Aʻo Kākoʻo ʻole ʻia
- Hoʻoemi Dimensionality
- Kahi Anomaly
- ʻ .naehana Paipai
- Ke aʻo ʻana i ka mīkini unahi nui
Haʻalele wau i nā mea akā hele mai ka mea nui, a laila haʻihaʻi nā mea āpau.
No ka hoʻomaʻamaʻa āu e hoʻohana ai ʻO Matlab a i ʻole Octave hiki iā mākou ke ʻōlelo i ka Matlab OpenSource. Ua hana wau i ka papa me Octave. E like me ka mea i hōʻike ʻia i nā papa mua, ua koho lākou i kēia mau mea hana no ka mea ʻae lākou i ka prototyping wikiwiki o nā algorithms. Me nā pono hana ʻē aʻe e lilo ana ka haumāna i mau hoʻolālā manawa nui loa.
ʻO ka mea maopopo ʻoiai ʻaʻole maʻalahi, haʻalele lākou i nā mea āpau na ʻoe e hoʻopau. Mākaukau ʻoe i ke kaiapuni holoʻokoʻa no nā hoʻolālā, nā hoʻonohonoho ʻikepili, nā hoʻolālā o nā kiʻi, nā hana a me nā loli e hoʻohana a me ka mea a ka haumāna e hoʻopihapiha nei i kekahi mau laina me nā algorithms nui.
Ke haʻi hou aku nei au, ʻaʻole ia he mea nui, ʻoiai ʻo ʻoe e hoʻolōʻihi i ka manawa e nānā ai pehea e hana ai kekahi mea iā Octave.
Nā noi kūpono
Ke ʻike nei i nā laʻana o nā noi a me nā mea hiki ke hana ʻAʻohe oʻu kānalua ʻo kēia ka wā e hiki mai ana o ka ʻoihana. E hoʻopau ana kekahi ʻoihana i nā hopena me ka aʻo ʻana i ka mīkini, ʻike kuʻuna a i ʻole nā mea a mākou e makemake ai e kāhea iā ia e hoʻomaikaʻi i nā wānana, ka mālama ʻana i ka maikaʻi a hoʻomaikaʻi i nā kaʻina hana like ʻole. E nānā pono ʻaʻole wau e kamaʻilio wale ana e pili ana i nā noi, a i ʻole ka honua pūnaewele, akā e pili ana i nā ʻoihana kino, nā lawelawe, nā hana, nā loina, a pēlā aku.
Ma waho aʻe o nā mea i ʻike ʻia, ʻike leo, OCR, ʻike kamepiula, unuhi ʻōlelo,
Paipai i nā ʻōnaehana, nā wānana
A kēia manawa
I kēia makahiki koʻu manaʻo e hoʻāʻo e hoʻokomo i ka mea aʻu i aʻo ai ma ka haku ʻana i kekahi mau pono hana e kōkua nui i ka hana. ʻIke wau ʻaʻole maʻalahi ia a e hoʻomaʻamaʻa wau iaʻu iho me Python a me kekahi ʻano, ʻo Tensor Flow, PyTorch a me kahi waihona e like me Numpy. Pono wau e ʻimi i ka mākeke.
Eia hou, makemake wau e komo i loko o Deep Learning me ka papa manuahi i hāʻawi ʻia ma http://course.fast.ai/ a hoʻomaka pū me Big Data, kekahi o nā māla e pili ana i ka ʻike hana a me ka ʻike ʻana i ka Mīkini a ʻo ia pū kekahi. pono loa iaʻu ma kaʻu hana. Ke nānā nei wau i ka loea i ʻIkepili Nui Coursera aia kekahi ʻoi aku ka maikaʻi akā ʻoi aku ka pipiʻi.
Inā he mau nīnau kāu hiki iā ʻoe ke waiho i kahi manaʻo.
ʻO Nacho maikaʻi,
ʻO ka mea mua mahalo iā ʻoe no ka kaʻana like ʻana i kāu ʻike. Makemake au e hana i kahi papa e pili ana i ka Big Data / Machine Learning no ka manawa lōʻihi mai kaʻu hana ma kahi keʻena me Data Scientist a i ka wā e hiki mai ana e hana wau i kahi kekelē laeoʻo e pili ana i kēia kumuhana.
ʻO wau he ʻenehana ʻenehana a he manaʻo nui koʻu no ka hana o Big Data, akā makemake wau e ʻike inā ʻoe e ʻōlelo aʻoaʻo e lawe pololei i kahi papa Big Data a i ʻole hiki ke hana pololei ʻia ka papa aʻo Machine.
Ma ka ʻaoʻao ʻē aʻe, ʻaʻole kiʻekiʻe loa kaʻu pae o ka ʻōlelo Pelekania (ma kahi haʻahaʻa) no laila ʻaʻole wau i ʻike inā loaʻa iaʻu nā pilikia i ka lawe ʻana i ka papa.
Mahalo no kou manawa! I nā mea maikaʻi 'oi loa.
Aloha Javier. He papa hoʻolauna a me nā kumuhana maoli, no laila ʻaʻole pono e ʻike i ka ʻike nui, no ka mea ʻaʻole ʻoe e ʻohiʻohi i nā ʻikepili, hāʻawi ʻia kēia iā ʻoe i nā hana. Nonoi wale lākou iā ʻoe e hoʻokō i ka algorithm nui.
A ʻo ka ʻōlelo Pelekania. Hoʻopili ʻia nā wikiō ma ka ʻōlelo Pelekania a me Paniolo. A laila aia nā transcripts. ʻAʻole pono ʻoe e kamaʻilio, no laila manaʻo wau ʻaʻole ʻoe e pilikia. Maliʻa uku paha iā ʻoe i kekahi mea hou aʻe, akā ʻaʻole au ʻike ia mea he impediment.
Aloha a haʻi iaʻu inā ʻaʻa ʻoe. :)
Pehea ʻoe i lanakila ai i nā pilikia o ka hoʻouna ʻana i nā hana?
Aloha Carlos. He aha nā pilikia āu e manaʻo ai? Me ka paepae e hāʻawi iā ʻoe i kahi kuhi?
Ua hoʻomaka wau i ka papa, maopopo iaʻu ka helu holoʻokoʻa o nā pule 2 mua, akā i ka manawa o ka hoʻokō ʻana i ka hana i hāʻawi mua ʻia ʻaʻole wau i ʻike pehea e hoʻokō ai i ka mea e nalo ana no ka holo pono ʻana o ka papahana, e like me kāu e ʻōlelo nei ua maʻalahi i nā mea āpau, akā ua hana wau i nā mea āpau a lākou e wehewehe ai i nā wikiō a ʻaʻohe mea, a makemake wau inā hiki iā ʻoe ke hāʻawi iaʻu i kahi kōkua no kēlā.
Aloha, e haʻi iaʻu i kahi e ʻike ai inā hiki iaʻu ke kōkua iā ʻoe.
Aloha'oe.
Ke ʻimi nei wau i ka ʻikepili mai ka papa Stanford Machine Learning a hiki i kāu ʻaoʻao. Makemake wau i kēia kumuhana a me ke aʻo ʻana i ka python.
E like me kāu e ʻōlelo nei he keu aku paha kēia a ua ʻimi au i nā mea kūpono aʻe akā ʻaʻole maopopo iaʻu he aha ia. He nui ko IBM, ʻo kekahi o ia palapala ʻo "IBM AI Engineering Professional Professional": https://www.coursera.org/professional-certificates/ai-engineer#courses
Aloha.
ʻAe, he manaʻo nui loa ia, ʻo ia e aʻo maikaʻi pehea e hana ai nā algorithms. Eia hou aʻe nā papa, https://www.ikkaro.com/cursos-machine-learning-deep-learning-ia/ ka hāmeʻa aʻo mīkini google, ua noi nui ʻia. Ke hoʻohana nei iā Tensorflow
Mahalo oe.
E hana wau i kahi āu e kuhikuhi ai mai google a inā hiki iaʻu ke hoʻopau maikaʻi e hana wau i kahi ʻē aʻe āu i loaʻa iā Udacity i ʻoi aku ka piha a manuahi hoʻi.