Gives a holistic approach to machine learning and data science
applications, from design to deployment and quality assurance, as
an overarching cyclical process; Bridges machine learning and
software engineering to build a shared set of best practices useful
to both academia and the industry; Discusses deployment options for
different types of models and data to help practitioners reason and
make informed choices. Emphasizes the role of coding standards and
software architecture alongside statistical rigor to implement
reproducible and scalable machine learning models Key Features: A
complete guide to software engineering for machine learning and
data science applications, from choosing the right hardware to
analysing algorithms and designing scalable architectures. Surveys
the state of the art of the software and frameworks used to build
and run machine learning applications, comparing and contrasting
their trade-offs. Comes with a complete case study in natural
language understanding which illustrates the principles and the
tools covered in the book. Code available from GitHub. Provides a
multi-disciplinary view of how traditional software learning
practices can be integrated with the workflows of domain experts
and the unique characteristics of software in which data play a
central role.
General
| Imprint: |
Crc Press
|
| Country of origin: |
United Kingdom |
| Series: |
Chapman & Hall/CRC Machine Learning & Pattern Recognition |
| Release date: |
April 2025 |
| First published: |
2023 |
| Authors: |
Marco Scutari
• Mauro Malvestio
|
| Dimensions: |
234 x 156mm (L x W) |
| Format: |
Paperback
|
| Pages: |
340 |
| ISBN-13: |
978-0-367-25506-0 |
| Categories: |
Books
Promotions
|
| LSN: |
0-367-25506-5 |
| Barcode: |
9780367255060 |
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