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注重体验与质量的电子书资源下载网站
标签名:
机器学习
出版日期:
2012-01
Ensemble Methods: Foundations and Algorithms
评分:
10.0
出版日期:
1996-01
Convex Analysis: Analysis
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:
2016-01
应用预测建模
评分:
9.7
出版日期:
0000-01
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents): Robotics
评分:
9.7
出版日期:
2017-01
Scikit-Learn与TensorFlow机器学习实用指南(影印版)
评分:
9.7
出版日期:
2016-01
Make Your Own Neural Network
评分:
9.6
出版日期:
2002-01
Neural Network Design
评分:
9.6
出版日期:
2010-01
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
评分:
9.6
出版日期:
2013-01
An Introduction to Statistical Learning: with Applications in R
评分:
9.6
出版日期:
2011-01
非线性最优化基础
评分:
9.6
出版日期:
2019-01
High-Dimensional Statistics: A Non-Asymptotic Viewpoint
评分:
9.6
出版日期:
2004-01
Convex Optimization
评分:
9.6
出版日期:
1989-01
Introductory Functional Analysis with Applications
评分:
9.6
出版日期:
2018-01
Python深度学习
评分:
9.5
出版日期:
2017-01
Deep Learning with Python
评分:
9.5
出版日期:
2020-01
神经网络与深度学习
评分:
9.5
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:
2003-01
The Elements of Statistical Learning
评分:
9.5