大学堂
上传课程
登录
注册
首页
课程
嵌入式开发
电源技术
电路基础
编程语言
设计工具/EDA
热门应用
测试测量
创客大咖秀
厂商专区
TI培训
NXP技术大讲堂
直播频道
专题
相关活动
您的位置:
EEWORLD大学堂
/
创客秀
/
科学探索
/
机器学习 吴恩达
播放列表
课程目录
课程笔记
课时1:Welcome
课时2:What is Machine Learning
课时3:Supervised Learning
课时4:Unsupervised Learning
课时5:Model Representation
课时6:Cost Function
课时7:Cost Function - Intuition I
课时8:Cost Function - Intuition II
课时9:Gradient Descent
课时10:Gradient Descent Intuition
课时11:Gradient Descent For Linear Regression
课时12:What-'s Next
课时13:Matrices and Vectors
课时14:Addition and Scalar Multiplication
课时15:Matrix Vector Multiplication
课时16:Matrix Matrix Multiplication
课时17:Matrix Multiplication Properties
课时18:Inverse and Transpose
课时19:Multiple Features
课时20:Gradient Descent for Multiple Variables
课时21:Gradient Descent in Practice I - Feature Scaling
课时22:Gradient Descent in Practice II - Learning Rate
课时23:Features and Polynomial Regression
课时24:Normal Equation
课时25:Normal Equation Noninvertibility (Optional)
课时26:Basic Operations
课时27:Moving Data Around
课时28:Computing on Data
课时29:Plotting Data
课时30:Control Statements- for, while, if statements
课时31:Vectorization
课时32:Working on and Submitting Programming Exercises
课时33:Classification
课时34:Hypothesis Representation
课时35:Decision Boundary
课时36:Cost Function
课时37:Simplified Cost Function and Gradient Descent
课时38:Advanced Optimization
课时39:Multiclass Classification- One-vs-all
课时40:The Problem of Overfitting
课时41:Cost Function
课时42:Regularized Linear Regression
课时43:Regularized Logistic Regression
课时44:Non-linear Hypotheses
课时45:Neurons and the Brain
课时46:Model Representation I
课时47:Model Representation II
课时48:Examples and Intuitions I
课时49:Examples and Intuitions II
课时50:Multiclass Classification
课时51:Cost Function
课时52:Backpropagation Algorithm
课时53:Backpropagation Intuition
课时54:Implementation Note- Unrolling Parameters
课时55:Gradient Checking
课时56:Random Initialization
课时57:Putting It Together
课时58:Autonomous Driving
课时59:Deciding What to Try Next
课时60:Evaluating a Hypothesis
课时61:Model Selection and Train-Validation-Test Sets
课时62:Diagnosing Bias vs. Variance
课时63:Regularization and Bias-Variance
课时64:Learning Curves
课时65:Deciding What to Do Next Revisited
课时66:Prioritizing What to Work On
课时67:Error Analysis
课时68:Error Metrics for Skewed Classes
课时69:Trading Off Precision and Recall
课时70:Data For Machine Learning
课时71:Optimization Objective
课时72:Large Margin Intuition
课时73:Mathematics Behind Large Margin Classification (Optional)
课时74:Kernels I
课时75:Kernels II
课时76:Using An SVM
课时77:Unsupervised Learning- Introduction
课时78:K-Means Algorithm
课时79:Optimization Objective
课时80:Random Initialization
课时81:Choosing the Number of Clusters
课时82:Motivation I- Data Compression
课时83:Motivation II- Visualization
课时84:Principal Component Analysis Problem Formulation
课时85:Principal Component Analysis Algorithm
课时86:Choosing the Number of Principal Components
课时87:Reconstruction from Compressed Representation
课时88:Advice for Applying PCA
课时89:Problem Motivation
课时90:Gaussian Distribution
课时91:Algorithm
课时92:Developing and Evaluating an Anomaly Detection System
课时93:Anomaly Detection vs. Supervised Learning
课时94:Choosing What Features to Use
课时95:Multivariate Gaussian Distribution (Optional)
课时96:Anomaly Detection using the Multivariate Gaussian Distribution (Optional)
课时97:Problem Formulation
课时98:Content Based Recommendations
课时99:Collaborative Filtering
课时100:Collaborative Filtering Algorithm
课时101:Vectorization- Low Rank Matrix Factorization
课时102:Implementational Detail- Mean Normalization
课时103:Learning With Large Datasets
课时104:Stochastic Gradient Descent
课时105:Mini-Batch Gradient Descent
课时106:Stochastic Gradient Descent Convergence
课时107:Online Learning
课时108:Map Reduce and Data Parallelism
课时109:Problem Description and Pipeline
课时110:Sliding Windows
课时111:Getting Lots of Data and Artificial Data
课时112:Ceiling Analysis- What Part of the Pipeline to Work on Next
课时113:Summary and Thank You
时长:8分8秒
日期:2018/05/05
收藏视频
分享
上传者:
老白菜
课程介绍
此课程将广泛介绍机器学习、数据挖掘与统计模式识别的知识。主题包括:(i) 监督学习(参数/非参数
算法
、支持向量机、内核、神经网络)。(ii) 非监督学习(聚类、降维、推荐
系统
、深度学习)。(iii) 机器学习的优秀案例(偏差/方差理论;机器学习和人工
智能
的创新过程)课程将拮取案例研究与应用,学习如何将学习算法应用到智能机器人(观感,
控制
)、文字理解(网页搜索,防垃圾邮件)、计算机视觉、医学信息学、
音频
、数据挖掘及其他领域上。
主讲人简介
吴恩达 Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
相关标签:
机器学习
ML
machine learning
换一批
猜你喜欢
2016 TI 工业研讨会:TI工业信号链方案攻略
Linux下vivado安装教程
如何使用Coocox 开发 STM32F0
Microchip多媒体开发板
英飞凌新能源汽车方案
3 kW工业变频方案
TI 智能音频功放介绍
Microchip Microstick II开发工具
简化 USB3.1 设计的验证和调试
采用 LT8697 为汽车中的USB VBUS供电
论坛相关
更多
想学机器学习,需要了解哪些语言?有什么资料推荐?
机器学习精品电子书
新手提问,编译LINK的时候出现了下面的错误,请赐教。
武汉中证通谈可靠机器学习很多地方被高估
VS 2005编译 tcpmp 0.72中的common出现module machine type 'THUMB' conflicts with target
相关下载
更多
机器学习.汤姆·米切尔].McGrawHill,.Tom.Mitchell.-.Machine.Learning.pdf
Machine Learning with WEKA: An Introduction (讲义)关于数据挖掘和机器学习的.
machine learning
Machine_Learning_Yearning
Machine Learning in Computer Vision - N. SEBE
电子工程世界版权所有
京ICP证060456号
京ICP备10001474号
电信业务审批[2006]字第258号函
京公海网安备110108001534
Copyright © 2005-2018 EEWORLD.com.cn, Inc. All rights reserved