Academic
  • Introduction
  • Artificial Intelligence
    • Introduction
    • AI Concepts, Terminology, and Application Areas
    • AI: Issues, Concerns and Ethical Considerations
  • Biology
    • Scientific Method
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    • Water, Acids, Bases
    • Properties of carbon
    • Macromolecules
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    • Cellular respiration
    • Cell Signaling
    • Cell Division
    • Classical and molecular genetics
    • DNA as the genetic material
    • Central dogma
    • Gene regulation
  • Bioinformatics
    • Bioinformatics Overview
  • Deep Learning
    • Neural Networks and Deep Learning
      • Introduction
      • Logistic Regression as a Neural Network
      • Python and Vectorization
      • Shallow Neural Network
      • Deep Neural Network
    • Improving Deep Neural Networks
      • Setting up your Machine Learning Application
      • Regularizing your Neural Network
      • Setting up your Optimization Problem
      • Optimization algorithms
      • Hyperparameter, Batch Normalization, Softmax
    • Structuring Machine Learning Projects
    • Convolutional Neural Networks
      • Introduction
    • Sequence Models
      • Recurrent Neural Networks
      • Natural Language Processing & Word Embeddings
      • Sequence models & Attention mechanism
  • Linear Algebra
    • Vectors and Spaces
      • Vectors
      • Linear combinations and spans
      • Linear dependence and independence
      • Subspaces and the basis for a subspace
      • Vector dot and cross products
      • Matrices for solving systems by elimination
      • Null space and column space
    • Matrix transformations
      • Functions and linear transformations
      • Linear transformation examples
      • Transformations and matrix multiplication
      • Inverse functions and transformations
      • Finding inverses and determinants
      • More Determinant Depth
  • Machine Learning
    • Introduction
    • Linear Regression
      • Model and Cost Function
      • Parameter Learning
      • Multivariate Linear Regression
      • Computing Parameters Analytically
      • Octave
    • Logistic Regression
      • Classification and Representation
      • Logistic Regression Model
    • Regularization
      • Solving the Problem of Overfitting
    • Neural Networks
      • Introduction of Neural Networks
      • Neural Networks - Learning
    • Improve Learning Algorithm
      • Advice for Applying Machine Learning
      • Machine Learning System Design
    • Support Vector Machine
      • Large Margin Classification
      • Kernels
      • SVM in Practice
  • NCKU - Artificial Intelligence
    • Introduction
    • Intelligent Agents
    • Solving Problems by Searching
    • Beyond Classical Search
    • Learning from Examples
  • NCKU - Computer Architecture
    • First Week
  • NCKU - Data Mining
    • Introduction
    • Association Analysis
    • FP-growth
    • Other Association Rules
    • Sequence Pattern
    • Classification
    • Evaluation
    • Clustering
    • Link Analysis
  • NCKU - Machine Learning
    • Probability
    • Inference
    • Bayesian Inference
    • Introduction
  • NCKU - Robotic Navigation and Exploration
    • Kinetic Model & Vehicle Control
    • Motion Planning
    • SLAM Back-end (I)
    • SLAM Back-end (II)
    • Computer Vision / Multi-view Geometry
    • Lie group & Lie algebra
    • SLAM Front-end
  • Python
    • Numpy
    • Pandas
    • Scikit-learn
      • Introduction
      • Statistic Learning
  • Statstics
    • Quantitative Data
    • Modeling Data Distribution
    • Bivariate Numerical Data
    • Probability
    • Random Variables
    • Sampling Distribution
    • Confidence Intervals
    • Significance tests
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  • Introduction
  • Basics
  • Shallow neural networks
  • Deep neural network

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  1. Deep Learning

Neural Networks and Deep Learning

在 Neural Networks and Deep Learning 中

我們將會學習 deep learning 的基礎 :

  • 知道 deep learning 的主流技術

  • 能夠 build, train, apply 一個 fully connected 的 deep neural networks

  • 知道如何 implement 一個 vectorized neural networks

  • 了解 neural networks architecture 的 key parameters

而這也是 Deep learning 必修的第一堂課 !

Introduction

認識一些 NN 的專業名詞，以及他們應該被應用在哪裡

  • what is nn

  • Supervised in nn

  • why dl taking off

Basics

讓我們用 NN 的 mindsets 來解決 ML 問題

  • logistic in nn

  • vectorization

Shallow neural networks

用單層的 hidden layer 來試作 forward, backward propogation

  • nn representation

  • vectorized implementation

  • non-linear activation function

  • gradient descent

  • backpropogation preview

  • random initialization

Deep neural network

知道為何要使用 "deep" 的 nn

  • forward propogation

  • why deep ?

  • forward & backward propogation

  • parameters (weight, bias)

  • hyperparameters (experience, learning rates, iter, layers)

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Last updated 5 years ago

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