An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics. Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models Short, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models Streamlined presentation separates critical ideas from background context and extraneous detail Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks
This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. • Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry • A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking • More than 70 algorithms are described in sufficient detail to implement • More than 350 full-color illustrations amplify the text • The treatment is self-contained, including all of the background mathematics • Additional resources at www.computervisionmodels.com
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics. Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models Short, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models Streamlined presentation separates critical ideas from background context and extraneous detail Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks
It has been hard to categorise and identify the 'Wisdom psalms' within the Psalter. Interpreters have produced different lists of wisdom psalms of greatly varying lengths, and individual scholars often change their choices over time. Cheung re-examines the issues at stake in identifying this group of psalms in order to better describe the configuration of this psalmic genre. Past scholarship has failed to settle this issue because of the use of unfit criteria and an ill-understood concept of genre. With the aid of the concepts of 'family resemblance' and 'prototypes', this book proposes to define 'wisdom psalms' as a psalm family which is characterised by a wisdom-oriented constellation of its generic features. Three such features are identified after a fresh assessment of the most typical characteristics of 'wisdom literature'. This proposed method is put to test in the extensive study of seven psalms (37, 49, 73, 128, 32, 39, and 19) and the three criteria are verified to be suitable descriptors of the 'wisdom psalm' family. Cheung also explores questions related to the wisdom-cult disparity, Joban parallels as wisdom indicators, and the wisdom-orientation of 'torah psalms'.
Begun by William Hendriksen, Baker's New Testament Commentary has earned the acclaim and respect of Reformed and evangelical scholars and pastors. Since Hendriksen's death in 1982, the series has been continued by Simon J. Kistemaker. Four of the volumes compiled by Kistemaker earned the Gold Medallion Award (Hebrews, James and 1-3 John, Acts, and 1 Corinthians). The series was completed in 2001 with the publication of Revelation. This award-winning series from Baker Academic is currently the only available commentary from a Reformed perspective that covers the entire New Testament.
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