工程制圖B教學(xué)課件，內(nèi)容還不錯(cuò)，有需要的下載，看看
標(biāo)簽： 教學(xué)資料
上傳時(shí)間： 2018-03-01
上傳用戶：lgdxy
剛剛上傳的是工程B習(xí)題答案，有需要的可下載看看
標(biāo)簽： 工程制圖
上傳時(shí)間： 2018-03-01
上傳用戶：lgdxy
Py3爬取B站視頻彈幕。。。。。。。。。。。。。
上傳時(shí)間： 2019-12-17
上傳用戶：wine
1998年建模B題，內(nèi)含解題方法解題步驟。
標(biāo)簽： 1998 數(shù)學(xué)建模 論文
上傳時(shí)間： 2020-05-02
上傳用戶：isomerrrr
Machine learning is a broad and fascinating field. Even today, machine learning technology runs a substantial part of your life, often without you knowing it. Any plausible approach to artifi- cial intelligence must involve learning, at some level, if for no other reason than it’s hard to call a system intelligent if it cannot learn. Machine learning is also fascinating in its own right for the philo- sophical questions it raises about what it means to learn and succeed at tasks.
標(biāo)簽： Learning Machine Course in
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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(biāo)簽： Auto-Machine-Learning-Methods-Sys tems-Challenges
上傳時(shí)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(biāo)簽： Bishop-Pattern-Recognition-and-Ma chine-Learning
上傳時(shí)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(biāo)簽： Foundations Learning Machine 2nd of
上傳時(shí)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(biāo)簽： interpretable-machine-learning
上傳時(shí)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(biāo)簽： Technologies Healthcare Learning Machine
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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