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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:
2020-01
深度学习导论
评分:
0.0
出版日期:
2019-01
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
评分:
0.0
出版日期:
2016-01
Process Mining: Data Science in Action
评分:
0.0
出版日期:
2018-01
C++模板元编程实战: 一个深度学习框架的初步实现
评分:
0.0
出版日期:
0000-01
Building Machine Learning Powered Applications: Going from Idea to Product
评分:
0.0
出版日期:
2019-01
Foundations of Deep Reinforcement Learning: Theory and Practice in Python
评分:
0.0
出版日期:
2020-01
First-order and Stochastic Optimization Methods for Machine Learning
评分:
0.0
出版日期:
2020-01
Machine Learning: A Bayesian and Optimization Perspective, 2nd Edition
评分:
0.0
出版日期:
2010-01
视觉信息认知计算理论
评分:
0.0
出版日期:
2020-01
Build a Career in Data Science
评分:
0.0
出版日期:
2009-01
Algebraic Geometry and Statistical Learning Theory
评分:
0.0
出版日期:
2010-01
Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction
评分:
0.0
出版日期:
2007-01
Concentration Inequalities and Model Selection: Ecole d'Eté de Probabilités de Saint-Flour XXXIII - 2003 (Lecture Notes in Mathematics / Ecole d'Eté Probabilit.Saint-Flour)
评分:
0.0
出版日期:
2007-01
Learning Theory: An Approximation Theory Viewpoint
评分:
0.0
出版日期:
1997-01
Computational Learning Theory
评分:
0.0
出版日期:
0000-01
统计机器学习导论
评分:
0.0
出版日期:
2011-01
Statistics for High-Dimensional Data: Methods, Theory and Applications
评分:
0.0
出版日期:
0000-01
Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems
评分:
0.0
出版日期:
2015-01
复杂数据统计方法: 基于R的应用(第三版)
评分:
0.0
出版日期:
2015-01
Foundations of Linear and Generalized Linear Models
评分:
0.0
出版日期:
2018-01
Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling
评分:
0.0
出版日期:
1992-01
贝叶斯统计学 原理、模型及应用
评分:
0.0
出版日期:
1997-01
bootstrap methods and their application: (Cambridge Series in Statistical and Probabilistic Mathematics , No 1)
评分:
0.0
出版日期:
2020-01
Foundations of Data Science
评分:
0.0
