Coursera 笔记

DeepLearning.AI

Neural Networks and Deep Learning

神经网络与深度学习

4 notes · 33 images · 0/61 条官方条目有笔记 · certified

Coursera ↗

课程笔记

1 篇
  1. 01Course Notes课程笔记
Module 01

逻辑回归

1 / 10 篇
  1. 01Welcome
  2. 02What is a Neural Network?
  3. 03Supervised Learning with Neural Networks
  4. 04Why is Deep Learning taking off?
  5. 05About this Course
  6. 06Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!
  7. 07Frequently Asked Questions
  8. 08Lecture Notes W1
  9. 09Quiz Review: Introduction to Deep Learning错题本：深度学习简介
  10. 10Geoffrey Hinton Interview
Module 02

Neural Networks Basics

0 / 24 篇
  1. 01Binary Classification
  2. 02Logistic Regression
  3. 03Logistic Regression Cost Function
  4. 04Gradient Descent
  5. 05Derivatives
  6. 06More Derivative Examples
  7. 07Computation Graph
  8. 08Derivatives with a Computation Graph
  9. 09Logistic Regression Gradient Descent
  10. 10Gradient Descent on m Examples
  11. 11Derivation of DL/dz (Optional)
  12. 12Vectorization
  13. 13More Vectorization Examples
  14. 14Vectorizing Logistic Regression
  15. 15Vectorizing Logistic Regression's Gradient Output
  16. 16Broadcasting in Python
  17. 17A Note on Python/Numpy Vectors
  18. 18Quick tour of Jupyter/iPython Notebooks
  19. 19Explanation of Logistic Regression Cost Function (Optional)
  20. 20Lecture Notes W2
  21. 21Deep Learning Honor Code
  22. 22Programming Assignment FAQ
  23. 23(Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace
  24. 24Pieter Abbeel Interview
Module 03

Shallow Neural Networks

1 / 14 篇
  1. 01Neural Networks Overview
  2. 02Neural Network Representation
  3. 03Computing a Neural Network's Output
  4. 04Vectorizing Across Multiple Examples
  5. 05Explanation for Vectorized Implementation
  6. 06Activation Functions
  7. 07Why do you need Non-Linear Activation Functions?
  8. 08Derivatives of Activation Functions
  9. 09Gradient Descent for Neural Networks
  10. 10Backpropagation Intuition (Optional)
  11. 11Random Initialization
  12. 12Lecture Notes W3
  13. 13Quiz Review: Shallow Neural Networks错题本：浅层神经网络
  14. 14Ian Goodfellow Interview
Module 04

深度神经网络

1 / 16 篇
  1. 01Deep L-layer Neural Network
  2. 02Forward Propagation in a Deep Network
  3. 03Getting your Matrix Dimensions Right
  4. 04Why Deep Representations?
  5. 05Building Blocks of Deep Neural Networks
  6. 06Forward and Backward Propagation
  7. 07Optional Reading: Feedforward Neural Networks in Depth
  8. 08Parameters vs Hyperparameters
  9. 09Clarification For: What does this have to do with the brain?
  10. 10What does this have to do with the brain?
  11. 11Lecture Notes W4
  12. 12Quiz Review: Key Concepts on Deep Neural Networks错题本：深度神经网络
  13. 13[IMPORTANT] Reminder about end of access to Lab Notebooks
  14. 14Confusing Output from the AutoGrader
  15. 15References
  16. 16Acknowledgments