Screen LibraryDeep learning patterns and practices /
Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real-world deep learning experience. You'll build your skills...
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| Main Authors: | |
|---|---|
| Published: |
Manning Publications Co.,
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| Publisher Address: | Shelter Island, NY : |
| Publication Dates: | [2021] |
| Literature type: | Book |
| Language: | English |
| Subjects: | |
| Summary: |
Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real-world deep learning experience. You'll build your skills and confidence with each interesting example. Deep learning patterns and practices is a deep dive into building successful deep learning applications. You'll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you'll get tips for deploying, testing, and maintaining your projects. |
| Item Description: | Includes index. |
| Carrier Form: | xxii, 447 pages : illustrations ; 24 cm |
| ISBN: |
9781617298264 1617298263 |
| Index Number: | Q325 |
| CLC: |
TP183 TP181 |
| Call Number: | TP181/F357 |
| Contents: | Part 1. Deep learning fundamentals. Designing modern machine learning -- Deep neural networks -- Convolutional and residual neural networks -- Training fundamentals -- Part 2. Basic design pattern. Procedural design pattern -- Wide convolutional neural networks -- Alternative connectivity patterns -- Mobile convolutional neural networks -- Autoencoders -- Part 3. Working with pipelines. Hyperparameter tuning -- Transfer learning -- Data distributions -- Data pipeline -- Training and deployment pipeline. |