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Title | The Mathematical Engineering of Deep |
Description | The Mathematical Engineeringof Deep The Mathematical Engineering of Deep Learning A book by Benoit Liquet , Sarat Moka and Yoni Nazarathy . To appear in this Chapman & Hall/CRC Data Science |
Keywords | N/A |
WebSite | deeplearningmath.org |
Host IP | 34.194.149.67 |
Location | United States |
Site | Rank |
US$3,153,748
Last updated: 2023-05-15 06:40:52
deeplearningmath.org has Semrush global rank of 3,356,104. deeplearningmath.org has an estimated worth of US$ 3,153,748, based on its estimated Ads revenue. deeplearningmath.org receives approximately 363,894 unique visitors each day. Its web server is located in United States, with IP address 34.194.149.67. According to SiteAdvisor, deeplearningmath.org is safe to visit. |
Purchase/Sale Value | US$3,153,748 |
Daily Ads Revenue | US$2,912 |
Monthly Ads Revenue | US$87,335 |
Yearly Ads Revenue | US$1,048,015 |
Daily Unique Visitors | 24,260 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
deeplearningmath.org. | A | 300 | IP: 34.194.149.67 |
deeplearningmath.org. | A | 300 | IP: 67.207.80.24 |
deeplearningmath.org. | NS | 3600 | NS Record: dns1.p03.nsone.net. |
deeplearningmath.org. | NS | 3600 | NS Record: dns2.p03.nsone.net. |
deeplearningmath.org. | NS | 3600 | NS Record: dns3.p03.nsone.net. |
deeplearningmath.org. | NS | 3600 | NS Record: dns4.p03.nsone.net. |
The Mathematical Engineering of Deep Learning A book by Benoit Liquet , Sarat Moka and Yoni Nazarathy . To appear in this Chapman & Hall/CRC Data Science Series . Synopsis: This book provides a complete and concise overview of the mathematical engineering of deep learning. In addition to overviewing deep learning foundations, the treatment includes convolutional neural networks, recurrent neural networks, transformers, generative adversarial networks, reinforcement learning, and multiple tricks of the trade. The focus is on the basic mathematical description of deep learning models, algorithms and methods. The presentation is mostly agnostic to computer code, neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such an approach is that a mathematically equipped reader can quickly grasp the essence of modern deep learning algorithms, models, and techniques without having to look at computer code, neuroscience, or the historical progression. |
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