Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners
Abstract
Use this comprehensive guide to learn how to build learning models by leveraging the computational resources that Google Cloud Platform makes available. All the essential principles and tools for building learning models from the ground up are covered.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
RNNs and LSTMs are valuable when analyzing sequences of frames in video data to track the flow of passengers.
“They are valuable when analyzing sequences of frames in video data to track the flow of passengers.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [55]
RNNs and LSTMs are useful in video-based passenger counting where frame sequences are analyzed to monitor passenger movements, such as entry and exit patterns, rather than isolated frame counting.
“RNNs and LSTMs are useful in video-based passenger counting where frame sequences are analyzed to monitor passenger movements, such as entry and exit patterns, rather than isolated frame counting.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [55]
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Neupane, B., Horanont, T., & Aryal, J. (2021) · Remote Sensing · doi:10.3390/rs13040808 · Cited by 247 · see citing works
Widening Access to Applied Machine Learning with TinyML
Janapa Reddi, V., Plancher, B., Kennedy, S., Moroney, L., Warden, P., Suzuki, L., Agarwal, A., Banbury, C., Banzi, M., Bennett, M., et al. (2022) · Harvard Data Science Review · doi:10.1162/99608f92.762d171a · Cited by 62 · see citing works
Jin, F., Peng, X., Sun, L., Song, Z., Zhou, K., & Lin, C. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70004 · Cited by 40 · see citing works
Galli, C., Gavrilova, A. V., & Calciolari, E. (2025) · Information · doi:10.3390/info16050378 · Cited by 36 · see citing works
Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24 · see citing works
Jarmakovica, A. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1621514 · Cited by 10 · see citing works
- Authors
- Bisong, E.
- Corresponding author
- Bisong, Ekaba
- ORCID
- Bisong, Ekaba
- Venue
- Apress eBooks
- Publisher
- Apress
- Published
- 2019
- OpenAlex
- W4234971943
- Cited by
- 895
- References
- 0
- Citations by year
- 2026: 62 · 2025: 169 · 2024: 174 · 2023: 205 · 2022: 157 · 2021: 99 · 2020: 28 · 2019: 1
- FWCI
- 86.92
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Apress eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- machine learning, deep learning, Google Cloud Platform, cloud computing, machine learning model building
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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