Thinking Data Science A Data Science Practitioner’s Guide /
This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the clie...
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| Váldodahkki: | |
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| Searvvušdahkki: | |
| Materiálatiipa: | Elektrovnnalaš E-girji |
| Giella: | eaŋgalasgiella |
| Almmustuhtton: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Preanttus: | 1st ed. 2023. |
| Ráidu: | The Springer Series in Applied Machine Learning,
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| Fáttát: | |
| Liŋkkat: | https://doi.org/10.1007/978-3-031-02363-7 |
| Fáddágilkorat: |
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