Coursera Deep Learning Specialization(by Andrew Ng) 강의 정리

Week2: Neural Networks Basics

• Logistic Regression(로지스틱 회귀)
• Logistic Regression Cost Function(로지스틱 회귀 비용함수)
• Gradient Descent(경사하강법)
• Computation Graph & 미분
• Logistic Regression에서의 Gradient Descent

Week3: Shallow Neural Networks

• Neural network Representation and Vectorization
• Activation Function(활성화 함수)
• non-linear Activation Function를 사용하는 이유
• Activation Function(활성화 함수) 미분
• Weight Random Initialization(가중치 초기화)

Week4: Deep Neural Networks

• Forward Propagation
• Getting your Matrix dimension right
• Why deep Representations? (깊은 신경망이 더 많은 특징을 잡아내는 이유, 직관적으로)
• Parameters vs HyperParameters
• Forward and Backward propagation

Week1: Practical aspects of Deep Learning

• Train/Dev/Test sets
• Bias/Variance
• Basic Recipe for Machine Learning
• Regularization(L1,L2)
• Why Regularization reduces overfitting?
• Dropout Regularization
• Understanding Dropout
• Other regularization methods
• Normalizing Inputs
• Vanishing/Exploding Gradients
• Weight Initialize for Deep Networks
• Numerical Approximations of Gradients
• Gradient Checking

Week2: Optimization algorithms

• Mini Batch Gradient Descent
• Understanding Mini Batch Gradient Descent
• Exponentially Weight Average
• Bias Correction of Exponentially Weight Average
• Gradient Descent with Momentum
• RMSprop
• Adam Optimization Algorithm
• Learning Rate Decay
• The problem of Local Optima

Week3: Hyperparameter tuning, Batch Normalization

• Tuning Process
• Using an Appropriate Scale
• Normalizing Activations in a Network
• Fitting Batch Norm into Neural Networks
• Why Does Batch Norm Work?
• Batch Norm at Test Time
• Softmax Regression
• Training Softmax Classifier

Week1: ML Strategy(1)

• Orthonalization
• Single Number Evaluation Metric
• Satisficing and Optimizing Metrics
• Train/Dev/Test Set Distribusions
• Size of Dev/Test Sets
• When to change Dev/Test sets and Metrics?
• Why Human-Level Performance?
• Avoidable Bias(회피 가능 편향)
• Surpassing Human-Level Performance
• Improving Model Performance

Week2: ML Strategy(2)

• Carrying Out Error Analysis
• Cleaning Up Incorrectly Labelled Data
• Training and Testing on Different Distribusions
• Bias and Variance with Mismatched Data Distribusions
• Addressing Data Mismatch
• Transfer Learning
• Multi-task Learning
• What is end-to-end Deep Learning
• Whether to use end-to-end Deep Learning

Week1: Foundations of Convolutional Neural Networks

• Computer Vision
• Edge Detection Examples
• More Edge Detection
• Padding
• Stride
• Convolutions Over Volume
• One layer of a Convolutional Net
• Simple Convolutional Networks Example
• Pooling Layers
• Why Convolutions?

Week2: Deep Convolutional models(Case Studies)

• Why Look at Case Studies?
• Classic Networks (LeNet, AlexNet, VGG)
• ResNets
• Why ResNets Work?
• Networks in Networks and 1x1 Convolutions
• Inception Network Motivation
• Inception Network
• Transfer Learning
• Data Augmentation
• State of Computer Vision

Week3: Object Detection

• Object Localization
• Landmark Detection
• Object Detection
• Convolutional Implementation of Sliding Windows
• Bounding Box Predictions
• Intersection Over Union
• Non-max Suppression
• Anchor Boxes
• YOLO Algorithm
• Region Proposals