Neural Networks and Deep Learning（簡體中文），比較經典的深度學習入門教程。
標簽： Networks Learning Neural Deep and 簡體中文
上傳時間： 2016-11-09
上傳用戶：zhousui
Holtek燒錄器Q&A_V1.01.PDF
上傳時間： 2017-02-20
上傳用戶：ieedo
GSFM、Costas、BPSK、CW調制信號的抗混響性能比較 Q-Function
標簽： Q-Function Costas GSFM BPSK 調制信號 性能比較
上傳時間： 2017-05-04
上傳用戶：kanra
Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
上傳時間： 2017-10-27
上傳用戶：shawnleaves
無線電通信Q簡語
標簽： 無線電通信
上傳時間： 2017-12-15
上傳用戶：cyrs
詳細介紹三菱Q系列MODBUS通信實例。
上傳時間： 2019-04-08
上傳用戶：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.
標簽： Learning Machine Course in
上傳時間： 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
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
