WHERE FUTURE BEGINS
  • ṢELF ḌEEP ḶEARNING
  • LSE MBA Essentials - The London School of Economics
    • Leading with influence
    • Economics for managers
    • Competitive strategy
    • Corporate strategy
    • Financial accounting
    • Management accounting
    • Analysing financial statements
    • In the mind of the manager
    • Nudging behaviour
    • Organisational culture as a leadership tool
  • 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
      • Experiential Branding
      • 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
      • Customers and Digital Marketing
      • Influence and How Information Spreads
      • Pricing Strategies
      • The 7M
      • BRANDING: Effective Brand Communications Strategies and Repositioning Strategies
      • Brand Messaging & Communication
      • Brand Elements: Choosing a Brand Name
      • 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
      • Compounding
      • Useful Shortcuts
      • Taxes
      • Inflation
      • APR and EAR
      • Term Structure
      • Discounted Cash Flow: Decision Making
      • Discounted Cash Flow Analysis
      • Forecast Drivers
      • Forecasting Free Cash Flow
      • Decision Criteria
      • 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
  • Data Analytics
    • Python for Data Analysis
    • Data Structure and Algorithms
  • Programming Language
    • Python
    • R
    • SQL
    • 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. Neutral Networks and Deep Learning

About this Course

PreviousWhy is Deep Learning taking off?NextBinary Classification

Last updated 5 years ago

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你的学习进度已经快接近 这个专项课程的 第一门课的第一周的末尾了 让我给你快速地介绍一下 下一周我们将要学习什么内容 在第一个视频里我已经说过 这个专项课程一共有五门课 目前我们在它的第一门课 在这门课中将教会你最重要的基础知识 它们真的是深度学习最重要的基石 当你学到第一门课的结尾 你将知道如何建立一个 深度神经网络并且使它奏效 下面是一些有关第一门课的细节 这门课有四个星期的材料 目前你就要完成第一周的学习了 当你看到了深度学习的介绍后 在每一周的结尾 也都会有十个多选题 你可以用来检验自己对材料的理解 所以当你看完这个视频的时候 我希望你能看看这些问题 在第二周你会学习一些有关神经网络的编程知识 了解神经网络的结构 我们把它叫做前向传播和反向传播 逐步的完善你的算法以及 如何高效实现你的神经网络 从第二周开始 你也会开始做一些编程训练 这让你练习你刚刚学到的材料 自己实现算法并看到它为自己工作 对于我来说 当我学习算法的时候 如果我能把它编写出来并且真正有效 我将会获得巨大的满足感 所以我希望你们也喜欢 在第三周学习了神经网络编程的框架后 你将可以编写一个单隐藏层的神经网络 所以你需要学习所有必需的关键概念 来实现神经网络的工作 最后在第四周 你将会建立一个深层的神经网络 它有许多层并且你能看到它行之有效 所以恭喜你完成了这段视频 我希望你现在对深入学习的情况 有一个更高层次的理解 也许你们中的一些人也有一些想法 可能想要有你自己应用深度学习的地方 我希望在这段视频结束之后 你可以把跟在后面的10个选择题做了 它们会被显示在课程网站上 这10个选择题将会检查你的理解 假设在第一次做的时候你没有全对 你完全可以不断地尝试 直到你把它们全都做对 我发现它们能确保我理解所有的概念 我希望你也可以做到 那么再次祝贺你一直看到了这里 我期待在本周的另外两个视频中见到你