WHERE FUTURE BEGINS
  • ṢELF ḌEEP ḶEARNING
  • LSE MBA Essentials - The London School of Economics
    • Leading with influence
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    • Corporate strategy
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    • Management accounting
    • Analysing financial statements
    • In the mind of the manager
    • Nudging behaviour
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  • Business Foundations Specialization - Wharton Online
    • Introduction to Marketing
      • BRANDING: Marketing Strategy and Brand Positioning
      • Marketing 101: Building Strong Brands Part I
      • Marketing 101: Building Strong Brands Part II
      • Strategic Marketing
      • Segmentation and Targeting
      • Brand Positioning
      • Brand Mantra: The Elevator Speech
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      • CUSTOMER CENTRICITY: The Limits of Product-Centric Thinking
      • Cracks in the Product-Centric Approach
      • Data-Driven Business Models
      • Three Cheers for Direct Marketing
      • Which Firms Are Customer Centric?
      • What is Customer Centricity?
      • Living in a Customer-Centric World
      • More Reflections on Customer CentricityPrev
      • Questions on Customer Centricity
      • GO TO MARKET STRATEGIES: Online-Offline Interaction
      • Online/Offline Competition
      • Friction
      • The Long Tail Theory
      • Preference Isolation
      • How Internet Retailing Startups Grow
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      • The 7M
      • BRANDING: Effective Brand Communications Strategies and Repositioning Strategies
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      • Brand Elements: Color & Taglines
      • Brand Elements: Packaging
      • Brand Elements: Persuasion
      • Repositioning a Brand
    • Introduction to Financial Accounting
      • 1.1.1: Financial Reporting Overview
      • 1.1.2: Financial Reporting Example
    • Managing Social and Human Capital
      • Professor Cappelli and Professor Useem Introductions
    • Introduction to Corporate Finance
      • Time Value of Money
      • Intuition and Discounting
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      • APR and EAR
      • Term Structure
      • Discounted Cash Flow: Decision Making
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      • Sensitivity Analysis
      • Return on Investment
    • Introduction to Operations Management
    • Wharton Business Foundations Capstone
  • Artificial Intelligence Career Program - deeplearning.ai
    • Machine Learning
      • Introduction to Machine Learning
      • Supervised Learning
      • Unsupervised Learning
      • Model Representation - Linear Regression
      • Cost Function
      • Gradient Descent
      • Gradient Descent For Linear Regression
      • Linear Algebra
    • Deep Learning
    • Neutral Networks and Deep Learning
      • Introduction to Deep Learning
      • What is a neural network?
      • Supervised Learning with Neural Networks
      • Why is Deep Learning taking off?
      • About this Course
      • Binary Classification
      • Logistic Regression
      • Gradient Descent
      • Derivatives
      • Computation graph
      • Derivatives with a Computation Graph
      • Logistic Regression Gradient Descent
      • Vectorization
      • Vectorizing Logistic Regression
      • Vectorizing Logistic Regression's Gradient Output
      • Broadcasting in Python
      • A note on python/numpy vectors
      • Explanation of logistic regression cost function (optional)
      • Neural Networks Overview
      • Neural Network Representation
      • Computing a Neural Network's Output
      • Vectorizing across multiple examples
      • Activation functions
      • Derivatives of activation functions
      • Gradient descent for Neural Networks
      • Backpropagation intuition (optional)
      • Random Initialization
      • Deep L-layer neural network
      • Forward Propagation in a Deep Network
      • Getting your matrix dimensions right
      • Why deep representations?
      • Building blocks of deep neural networks
      • Forward and Backward Propagation
      • Parameters vs Hyperparameters
      • What does this have to do with the brain?
    • Convolutional Neural Networks
      • Computer Vision
      • Edge Detection Example
      • Padding
      • Strided Convolutions
      • Convolutions Over Volume
      • One Layer of a Convolutional Network
      • Simple Convolutional Network Example
      • Pooling Layers
      • CNN Example - Fully Connected Layers
      • Why Convolutions?
    • Neural Network Theory [ETH]
    • Natural Language Processing
    • Computer Vision
  • IBM Data Science Professional Certificate
    • What is Data Science?
    • Open Source tools for Data Science
    • Data Science Methodology
    • Python for Data Science and AI
    • Databases and SQL for Data Science
    • Data Analysis with Python
    • Data Visualization with Python
    • Machine Learning with Python
    • Applied Data Science Capstone
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    • Python for Data Analysis
    • Data Structure and Algorithms
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    • R
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    • C++
    • C
    • Java
    • HTML
  • 机器学习工程师
  • 商业数据分析
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  1. Artificial Intelligence Career Program - deeplearning.ai
  2. Machine Learning

Gradient Descent

梯度下降算法

PreviousCost FunctionNextGradient Descent For Linear Regression

Last updated 5 years ago

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To reach a local minimum.

起始点的位置略有不同 你会得到一个 非常不同的局部最优解 这就是梯度下降算法的一个特点

Gradient descent algorithm

Learn rate: Alpha

右边的方法和左边的区别是 让我们看这里 就是这一步 如果这个时候你已经更新了θ0 那么你会使用 θ0的新的值来计算这个微分项 所以由于你已经在这个公式中使用了新的 θ0的值 那么这会产生一个与左边不同的 temp1的值 所以右边并不是正确地实现梯度下降的做法 我不打算解释为什么你需要同时更新 同时更新是梯度下降中的一种常用方法 我们之后会讲到 实际上同步更新是更自然的实现方法 当人们谈到梯度下降时 他们的意思就是同步更新 如果用非同步更新去实现算法 代码可能也会正确工作 但是右边的方法并不是人们所指的那个梯度下降算法 而是具有不同性质的其他算法 由于各种原因 这其中会表现出微小的差别 你应该做的是 在梯度下降中真正实现同时更新 这些就是梯度下降算法的梗概

Gradient Descent Intuition