搜索资源列表
MLearningLecture
- 机器学习及其挑战，内容包括：机器学习及其重要性；机器学习角色的转变；五个挑战问题。研究机器学习的兄弟们要看看了。-machine learning and its challenges, including : machine learning and its importance; Machine learning the changing roles; five challenges. The study machine learning to look at the brothers ha
GAPBILExample
- 用VC编写的演示如多智能体的机器学习算法，很好的一个机器学习例子 -VC presentation prepared by the multi-agent such as machine learning algorithm, a very good example of machine learning
SVM-PPT
- 关于数据挖掘中SVM，支持向量机的学习课件，都是PPT，内容详实，可作为讲课与学习的参考资料。-Data mining on the SVM, support vector machine learning courseware are PPT, informative, can be used as reference for lectures and learning.
learning
- 中科院胡占义教授关于计算机视觉中机器学习方法的将以，非常难得的资料。-HuZhanYi sciences professor about computer vision of machine learning method to the material, it is very rare.
semi-supervized-learning
- A PhD thesis on Semi-supervised learning with Graphs by Xiaojin Zhu. Focuses on creating graphs, based on a mixture of labeled and unlabeled data (as per the semi-supervised learning paradigm) and using processes on these graphs to propagate in rigo
Handbook.Of.Research.On.Machine
- 主要讲述机器学习的应用与趋势，包括机器学习的算法，理论算法和新技术，并用实例分析解决现实问题。-Focuses on the application of machine learning and trends, including machine learning algorithms, theory of algorithms and new technologies, and use examples to analyze and solve practical problems.
Willi-Richert--Luis-Pedro-Coelho-Building-Machine
- Willi Richert, Luis Pedro Coelho Building Machine Learning Systems with Python 2013-Willi Richert, Luis Pedro Coelho Building Machine Learning Systems with Python 2013
[Hans_Georg_Schaathun(auth.)]_Machine-learning-in
- Machine Learning Book for Beginner
Tutorial-on-Support-Vector-Machine
- Machine Learning is considered as a subfield of Artificial Intelligence and it is concerned with the development of techniques and methods which enable the computer to learn. In simple terms development of algorithms which enable the machine to learn
Statistical-learning-methods
- 该书籍介绍了很好的统计学习方法，能为机器学习和数据挖掘的爱好者提供很好的理论依据。-The book describes the good statistical learning method that can provide a good theoretical basis for machine learning and data mining enthusiasts.
Deep-learning-and-new-progress-
- 深度学习是机器学习中的一个新的研究领域。通过深度学习的方法构建深度网络来抽取特征是目 前目标和行为识别中得到关注的研究方向。为引起更多计算机视觉领域研究者对深度学习进行探索和讨论，并推 动目标和行为识别的研究，对深度学习及其在目标和行为识别中的新进展给予概述。方法首先介绍深度学习领 域研究的基本状况、主要概念和原理 然后介绍近期利用深度学习在目标和行为识别应用中的一些新进展。结 果阐述了深度学习与神经网络之间的关系，深度学习的优缺点，以及目前深度学习理论需要解决的主要问题。
Machine-Learning-
- Efficient Learning of Image Super-resolution and Compression Artifact Removal with Semi-Local Gaussian Proce-Efficient Learning of Image Super-resolution and Compression Artifact Removal with Semi-Local Gaussian Process
machine-learning_notes_andrew-NG
- 机器学习笔记由著名的专家Andrew NG编写，适合初学者学习。-machine learning_notes_andrew NG，which is the notes of the machine learning lecture.
2-learning
- Reading text photographs is a challenging problem that has received a significant amount of attention. Two key components of most systems are (i) text detection from images and (ii) character recognition, and many recent methods have been pro
Ch15
- machine learning dddddddddddd
AI Bible(Deep Learning)
- 随后该书分为三部分，第一部分是应用数学和机器学习基础，当初步具有上述理论基础后，才算叩开深度学习的大门。第二部分是深层网络的现代实践。第三部分是深度学习的理论研究，适用于想要执果索因、深入学习神经网络内在原理的研究人员学习。(The book is divided into three parts, the first part is the application of mathematical and machine learning based. When initially with t
Deep Learning
- deep learning 书籍，此书包括机器学习基础，深度前馈网络，卷积网络，蒙特卡洛方法等的详细介绍(Deep learning books, which include a detailed introduction to machine learning, deep feedforward networks, convolution networks, Monte Carlo methods, and so on)
learning python
- This book sets out to introduce people to important machine learning algorithms. Tools and applications using these algorithms are introduced to give the reader an idea of how they are used in practice today. A wide selection of machine learning b
Pro01
- softmax function of machine learning
MATLAB and Machine learning
- MATLAB与机器学习，包含机器学习简介，快速入门，应用监督式学习，应用无监督学习(MATLAB and Machine Learning, including Machine Learning Introduction, Quick Start, Application Supervised Learning, Application Unsupervised Learning)