Tag: Deep

Textual Emotion Classification Using Deep Broad Learning


Free Download Textual Emotion Classification Using Deep Broad Learning
English | 2024 | ISBN: 303167717X | 170 Pages | PDF EPUB (True) | 11 MB
In this book, the authors systematically and comprehensively discuss textual emotion classification by using deep broad learning. Since broad learning possesses certain advantages such as simple network structure, short training time and strong generalization ability, it is a new and promising framework for textual emotion classification in artificial intelligence. As a result, how to combine deep and broad learning has become a new trend of textual emotion classification, a booming topic in both academia and industry.

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Inside Deep Learning


Free Download Inside Deep Learning by Edward Raff
English | 2024 | ISBN: 1617298638 | 15h 35m pages | MP3 64 Kbps | 437 Mb
Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems.
In Inside Deep Learning, you will learn how to
Implement deep learning with PyTorch

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Go Programming Mastery A Deep Dive into Golang Learn the Fundamentals, Dive into Advanced Techniques


Free Download Go Programming Mastery: A Deep Dive into Golang: Learn the Fundamentals, Dive into Advanced Techniques, and Build Powerful Applications with Go (Golang) by R. Parvin
English | August 16, 2024 | ISBN: N/A | ASIN: B0DDHJ51KZ | 561 pages | EPUB | 1.82 Mb
Go Programming Mastery: A Deep Dive into Golang Learn the Fundamentals, Dive into Advanced Techniques, and Build Powerful Applications with Go (Golang)

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Deep Learning at Scale At the Intersection of Hardware, Software, and Data


Free Download Deep Learning at Scale: At the Intersection of Hardware, Software, and Data by Suneeta Mall
English | July 23, 2024 | ISBN: 1098145283 | 448 pages | MOBI | 8.27 Mb
Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required.

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