<b>Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production.</b> In <i>Deep Learning Patterns and Practices</i> you will learn: ย ย ย Internal functioning of modern convolutional neural networks ย ย ย Procedural reus
Deep Learning Patterns and Practices
โ Scribed by Andrew Ferlitsch
- Publisher
- Manning Publications
- Year
- 2020
- Tongue
- English
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. Youโll 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, youโll get tips for deploying, testing, and maintaining your projects.
โฆ Subjects
Computers, Data Science, Machine Learning, Neural Networks, Artificial Intelligence, General
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Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: โข Internal functioning of modern convolutional neural networks โข Procedural reuse design pattern for
<b>Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production.</b> In <i>Deep Learning Patterns and Practices</i> you will learn: ย ย ย Internal functioning of modern convolutional neural networks ย ย ย Procedural reus
<b>Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production.</b> In <i>Deep Learning Patterns and Practices</i> you will learn: ย ย ย Internal functioning of modern convolutional neural networks ย ย ย Procedural reus
Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted