The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
標簽： Auto-Machine-Learning-Methods-Sys tems-Challenges
上傳時間： 2020-06-10
上傳用戶：shancjb
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
標簽： Bishop-Pattern-Recognition-and-Ma chine-Learning
上傳時間： 2020-06-10
上傳用戶：shancjb
Design for manufacturability and statistical design encompass a number of activities and areas of study spanning the integrated circuit design and manufacturing worlds. In the early days of the planar integrated circuit, it was typical for a handful of practitioners working on a particular design to have a fairly complete understanding of the manufacturing process, the resulting semiconductor active and passive devices, as well as the resulting circuit - often composed of as few as tens of devices. With the success of semiconductor scaling, predicted and - to a certain extent even driven - by Moore’s law, and the vastly increased complexity of modern nano-meter scale processes and the billion-device circuits they allow, there came a necessary separation between the various disciplines.
標簽： Manufacturability Statistical Design for and
上傳時間： 2020-06-10
上傳用戶：shancjb
This book is a general introduction to machine learning that can serve as a reference book for researchers and a textbook for students. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms.
標簽： Foundations Learning Machine 2nd of
上傳時間： 2020-06-10
上傳用戶：shancjb
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
標簽： interpretable-machine-learning
上傳時間： 2020-06-10
上傳用戶：shancjb
Much has been written concerning the manner in which healthcare is changing, with a particular emphasis on how very large quantities of data are now being routinely collected during the routine care of patients. The use of machine learning meth- ods to turn these ever-growing quantities of data into interventions that can improve patient outcomes seems as if it should be an obvious path to take. However, the field of machine learning in healthcare is still in its infancy. This book, kindly supported by the Institution of Engineering andTechnology, aims to provide a “snap- shot” of the state of current research at the interface between machine learning and healthcare.
標簽： Technologies Healthcare Learning Machine
上傳時間： 2020-06-10
上傳用戶：shancjb
Machine learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on “automatic”, i.e., machine learning is concerned about general-purpose methodologies that can be applied to many datasets, while producing something that is mean- ingful. There are three concepts that are at the core of machine learning: data, a model, and learning.
上傳時間： 2020-06-10
上傳用戶：shancjb
Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning一本數學大全書，由Jean Gallier and Jocelyn Quaintance合著。
上傳時間： 2022-05-05
上傳用戶：默默
Electronic Devices and Circuit Theory 電子器件與電路理論英文版，非常好的一本書，原版書籍售價上千的。本文檔共927頁，高清文字版，帶書簽。
上傳時間： 2022-06-29
上傳用戶：默默
Learning Python 第5版電子版書籍，正規版本，不是掃描的哦，關于這本書的內容不解釋了，懂Python的應該知道，很不錯的一本書，不過非?？简炗⑽乃健?/p>
標簽： python
上傳時間： 2022-07-02
上傳用戶：xsr1983
