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注重体验与质量的电子书资源下载网站
标签名:
MachineLearning
出版日期:
2012-01
Ensemble Methods: Foundations and Algorithms
评分:
10.0
出版日期:
2019-01
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition: Concepts, Tools, and Techniques to Build Intelligent Systems
评分:
9.9
出版日期:
2001-01
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
评分:
9.9
出版日期:
2018-01
Reinforcement Learning: An Introduction (second edition)
评分:
9.8
出版日期:
2007-01
Pattern Recognition and Machine Learning
评分:
9.5
出版日期:
2011-01
Bayesian Reasoning and Machine Learning
评分:
9.5
出版日期:
2012-01
Computer Vision: Models, Learning, and Inference
评分:
9.5
出版日期:
1998-01
Statistical Learning Theory
评分:
9.5
出版日期:
2012-01
Learning From Data: A Short Course
评分:
9.4
出版日期:
2016-01
Deep Learning: Adaptive Computation and Machine Learning series
评分:
9.3
出版日期:
2013-01
Applied Predictive Modeling
评分:
9.3
出版日期:
2012-01
Boosting: Foundations and Algorithms
评分:
9.2
出版日期:
2012-01
Foundations of Machine Learning
评分:
9.1
出版日期:
2006-01
Prediction, Learning, and Games
评分:
9.1
出版日期:
2015-01
Numerical Python: A Practical Techniques Approach for Industry: A Practical Techniques Approach for Industry
评分:
9.1
出版日期:
2012-01
统计学习方法
评分:
9.0
出版日期:
2009-01
Probabilistic Graphical Models: Principles and Techniques
评分:
9.0
出版日期:
2012-01
Machine Learning: A Probabilistic Perspective
评分:
9.0
出版日期:
2016-01
机器学习
评分:
8.7
出版日期:
2008-01
Neural Networks and Learning Machines: Third Edition
评分:
8.7
出版日期:
2005-01
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
评分:
8.7
出版日期:
2017-01
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
评分:
8.6
出版日期:
2012-01
Machine Learning in Action
评分:
8.5
出版日期:
2018-01
Python机器学习基础教程
评分:
8.5
