Machine learning book for beginer!
標(biāo)簽： learning Machine beginer book
上傳時間： 2013-12-10
上傳用戶：youth25
Pattern recognition and machine learning WWW-Exercises solutions
標(biāo)簽： WWW-Exercises recognition solutions learning
上傳時間： 2014-01-23
上傳用戶：sammi
機(jī)器學(xué)習(xí)+Tom+M.+Mitchell《Machine+Learning》之中文版。據(jù)我們導(dǎo)師說這是一本很好的人工智能方面的書，希望學(xué)習(xí)人工智能的可以看看,我剛找到看。
標(biāo)簽： Mitchell Learning Machine Tom
上傳時間： 2014-01-15
上傳用戶：天涯
決策樹,Machine Learning, Tom Mitchell, McGraw Hill,第3章決策樹源碼
標(biāo)簽： Learning Mitchell Machine McGraw
上傳時間： 2017-09-19
上傳用戶：小碼農(nóng)lz
To describe Pattern Recognition using Machine Learning Method. It is good for one who want to learn machine learning.
標(biāo)簽： Pattern recognition ML machine learning
上傳時間： 2016-04-14
上傳用戶：shishi
Pattern Recognition and Machine Learning
上傳時間： 2016-06-01
上傳用戶：who123321
Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
標(biāo)簽： Learning Machine Python
上傳時間： 2017-10-27
上傳用戶：shawnleaves
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
上傳時間： 2020-06-10
上傳用戶：shancjb
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