Inā he polokalamu ʻenehana nui ʻoe, mea noiʻi ʻepekema data, a i ʻole ʻenehana i ka laulā a laila pono ʻoe e makaʻala i ka sub-field alakaʻi o Artificial Intelligence (AI) i kapa ʻia ʻo Machine Learning.
Pono ʻoe e makaʻala i ka nui o nā noi hoihoi o AI mai ka ʻike maʻalahi a me ka ʻike ʻōlelo ʻana i nā mea kōkua virtual integrated akamai. ʻO kēia mau noi a me nā mea hou aʻe i hiki ke hana ʻia e ka hoʻoikaika ʻana o ka Machine Learning Engineers.
E pili ana kēia ʻatikala ʻo wai kēia mau Engineers, nā mea a lākou e hana ai, a me nā mākau e pono ai e lilo ʻoe i ʻenehana ML mākaukau.
He aha ka hana a nā ʻenehana aʻo mīkini?
Ka Papa Hana (ML) Hoʻohui nā ʻenekinia i nā mākau makemakika analytical a me ka hoʻoponopono pilikia me ka ʻenehana polokalamu polokalamu i mea e hana ai i nā ʻōnaehana AI e hoʻoponopono i nā pilikia o ka honua maoli. Pono ka ML Engineer e hana ma ke ʻano he ʻikepili ʻikepili e hana me ka hoʻololi ʻana i ka ʻikepili e like me ke koi i hāʻawi ʻia a kūkulu, hoʻomaʻamaʻa, hōʻoia a hoʻāʻo i nā algorithms ML ma ke ʻano he kumu hoʻohālike ma ka ʻikepili i hāʻawi ʻia.
Ke hana nei paha ia mau ʻEnekinia me kahi hui ma ka ʻoihana ʻenehana, kūʻokoʻa ma ke ʻano he polokalamu polokalamu a i ʻole he mea noiʻi i nā pilikia ML ʻokiʻoki. ʻO kēlā me kēia ʻaoʻao, aia kekahi mau koi akamai e pono e hoʻokō i mea e hiki ai ke lilo i ML Engineer. Ua kūkākūkā nui ʻia kēia mau mākau ma lalo nei.
5 Pono e loaʻa nā mākau ML
1. Ka Manawa a me ka Heluhelu
ʻO kekahi o nā koi mua o ka ML he ʻike i waena o nā kumuhana e pili ana i ka probability a me nā helu. Pono kēia no ka mea ua hoʻokumu ʻia nā algorithms ML a me nā hiʻohiʻona ma luna o kēia mau loina makemakika a ʻaʻole hiki ke kūkulu ʻia me ka ʻole o lākou.
He mea koʻikoʻi ka probability i ka wā e pili ana i nā mea hoʻokomo, nā mea hoʻopuka, a me ka maopopo ʻole o ka honua maoli. ʻO kekahi mau loina o ka probability i hoʻohana ʻia ma ML e pili ana i ke kūlana kūlana, ka lula Bayes, likelihood, a me ke kūʻokoʻa. Hāʻawi ka Stats iā mākou i nā ana e pono ai no ke kūkulu ʻana i nā hiʻohiʻona ML me ka mean, median, varice, puʻunaue (uniform, maʻamau, binomial, Poisson), a me nā ʻano loiloi me ka hoʻāʻo ʻana i ke kuhiakau.
2. Nā kumu o ka papahana
ʻO kekahi mea ʻē aʻe o ka ML ka loaʻa ʻana o ka ʻike kumu o ka papahana. Loaʻa kēia i ka ʻike kūpono o nā hoʻolālā ʻikepili, me nā pūʻulu, queues, multi-dimensional arrays, kumulāʻau, graphs, etc., a me nā algorithms, me ka huli ʻana, ka hoʻokaʻawale ʻana, ka optimization, dynamic programming, etc.
Koho i kou olelo
E pili ana nā ʻōlelo hoʻonohonoho, ʻo ka mea maikaʻi loa e aʻo ai no ML ʻo Python i ukali ʻia e Java. ʻO kēia no ka mea ʻo Python ke kākoʻo pūnaewele nui loa e pili ana i nā code i loaʻa, frameworks, a me ke kōkua kaiāulu.
E ʻike i kāu IDE
ʻO ka hana aʻe e kamaʻāina iā ʻoe iho me kahi Integrated Development Environment (IDE). No ka mea ke lawelawe nei mākou i ka nui o ka ʻikepili ʻaʻole hiki i kāu IDE ke lilo i kahi Command Line Interface (CLI) maʻalahi ma kahi mea hana e like me Visual Studio Code a i ʻole. Puke Memo Jupyter. E like me Python, loaʻa iā Jupyter ke kākoʻo pūnaewele nui loa a hoʻohana ʻia e nā kumu aʻo ML he nui no nā kumu hoʻonaʻauao pū kekahi.
Hoʻomaopopo i nā Hale Waihona Puke
ʻO nā hale waihona puke he hōʻiliʻili o nā kumuwaiwai e pono e hoʻokomo ʻia i loko o kahi papahana ma mua o ka hoʻohana ʻana. Aia kekahi mau hale waihona puke ML e like me TensorFlow, Keras, PyTorch, Pandas, Matplotlib, Numpy, etc. He mea nui i ka ML Engineer ka ʻike maikaʻi ʻana i ka ML a me nā waihona mālama ʻikepili i mea e maʻalahi ai ka hoʻonohonoho ʻana a me ka launa pū.
3. Hoʻohālike a me ka loiloi ʻikepili
ʻO kekahi o nā ʻāpana koʻikoʻi o ML ʻo ia ke kaʻina o ka hoʻohālikelike ʻana i ke ʻano kumu o kahi waihona i hāʻawi ʻia i mea e ʻike ai i nā kumu kūpono, ʻo ia hoʻi nā correlations, clusters, eigenvectors. Pono mākou e wānana i nā waiwai o nā hiʻohiʻona ʻikepili me ka regression, ka helu ʻana, a me ka ʻike anomaly. Pono ka ML Engineer e loiloi i kahi hoʻohālike i hāʻawi ʻia me ka hoʻohana ʻana i ka metric pololei a me ka hoʻolālā.
4. Hoʻohana i nā Algorithms Aʻo Mīkini
ʻO kekahi ʻāpana koʻikoʻi o ML ka hiki ke hoʻopili i nā algorithms ML. He mea pono e hoʻomaopopo i ke kūkulu ʻana i kāu kumu hoʻohālike he mea maʻamau ʻole ka nui o nā hiʻohiʻona ML a me nā hoʻokō i loaʻa i nā hale waihona puke e like me Keras a me scikit-learn. Eia naʻe, ʻo ka hoʻohana ʻana i kēia mau hiʻohiʻona ma ke ʻano kūpono loa a e like me ka ʻikepili e pono ai ka mākaukau a me ka pae maikaʻi o ka hoʻomaopopo ʻana i nā hiʻohiʻona ML ma ka laulā.
Pono kekahi ML Engineer e ʻike i nā pono pili a me nā hemahema o nā ala like ʻole a me nā pilikia e like me ka overfitting, underfitting, bias, a me nā pilikia ʻokoʻa.
5. E kūkulu i nā pūnaewele neural
ʻO nā Neural Networks (NNs) he ʻāpana o kahi sub-field o ML i ʻike ʻia ʻo Hoʻopiha hohonu a he koi ʻoi aku ka lōʻihi e pili ana i nā mākau ML koʻikoʻi. Eia nō naʻe, i nā hoʻohana pono ʻana o ML, pono mākou e ʻike i nā NN e hana i nā hiʻohiʻona ikaika loa no kā mākou ʻōnaehana AI.
Hoʻohana ka NN i nā papa a me nā neurons e hana i nā hiʻohiʻona ML ikaika. Hiki i kekahi ML Engineer ke kūkulu, hoʻomaʻamaʻa, hōʻoia a hoʻāʻo i nā NN.
Panina
Pono ʻoe e hoʻomaopopo maikaʻi i kēia manawa Ka Papa Hana ʻO nā ʻenekinia, nā mea a lākou e hana ai, a me nā mākau e pono ai ʻoe e hoʻomaka i kāu huakaʻi. Pono ʻoe e aʻo maikaʻi me ka probability, ka helu helu helu, ka hoʻonohonoho ʻana, ka hoʻohālikelike ʻikepili, nā noi algorithms, a me ke kūkulu ʻana i nā ʻupena neural e kūkulu i nā hoʻonā AI a me ML ikaika.
E ʻike iā mākou ma nā ʻōlelo inā he mea kōkua ka ʻatikala a he aha kāu e manaʻo ai ka mākaukau koʻikoʻi no ka lilo ʻana i ML Engineer mākaukau.
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