Q GDW 1575-2014用电信息采集终端自动化检测系统技术规范
标签： 1575 2014 GDW 电信 检测系统 技术规范 采集终端 自动化
上传时间： 2016-08-28
上传用户：jelenecheung
Neural Networks and Deep Learning（简体中文），比较经典的深度学习入门教程。
标签： Networks Learning Neural Deep and 简体中文
上传时间： 2016-11-09
上传用户：zhousui
Holtek燒錄器Q&A_V1.01.PDF
上传时间： 2017-02-20
上传用户：ieedo
GSFM、Costas、BPSK、CW调制信号的抗混响性能比较 Q-Function
标签： Q-Function Costas GSFM BPSK 调制信号 性能比较
上传时间： 2017-05-04
上传用户：kanra
Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
上传时间： 2017-10-27
上传用户：shawnleaves
无线电通信Q简语
标签： 无线电通信
上传时间： 2017-12-15
上传用户：cyrs
详细介绍三菱Q系列MODBUS通信实例。
上传时间： 2019-04-08
上传用户：liwei2015
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.
标签： Learning Machine Course in
上传时间： 2020-06-10
上传用户：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.
标签： Auto-Machine-Learning-Methods-Sys tems-Challenges
上传时间： 2020-06-10
上传用户：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.
标签： Bishop-Pattern-Recognition-and-Ma chine-Learning
上传时间： 2020-06-10
上传用户：shancjb
