https://hackerpaper.kaipfister.de/p/2407.18384/Collected HN comments and submissions linking this paper.enWed, 04 Nov 2026 05:35:47 GMThttps://news.ycombinator.com/item?id=50360451urn:hackerpapers:mention:2407.18384:50360451Wed, 04 Nov 2026 01:20:54 GMT<p>Some that comes to mind: - Understanding deep learning by Prince[1]. Has both theoretical and programming exercises. - Deep learning - Foundations and concepts by Bishop[2]. - Deep learning architectures - mathematical approach - Calin[3]. - Mathematical theory of deep learning by Petersen et al[4]. Only handful of exercises each chapter. - Dive into deep learning[5]. This one is more practical programming hands-on. The exercises is a mix between programming exercises and theoretical. - EECS 182/282A | Deep Neural Networks 2025 (not a book but a course by Berkley)[6]. I only skimmed the…</p><p><a href="https://hackerpaper.kaipfister.de/p/2407.18384/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LGhttps://news.ycombinator.com/item?id=41974769urn:hackerpapers:submission:2407.18384:41974769Mon, 28 Oct 2024 18:58:27 GMT<p>Mathematical Theory of Deep Learning</p><p></p><p>3 points; 0 discussion replies on HN. <a href="https://hackerpaper.kaipfister.de/p/2407.18384/">Paper and source history</a>. A submission is not an endorsement.</p>cs.LG