Deep Learning Specialization
Introduction
Course 1 - Neural Networks and Deep Learning
Week 1 - Introduction to 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
Week 2 - Neural Networks Basics
Logistic Regression as a Neural Network
Binary Classification
Logistic Regression
Logistic Regression Cost Function
Gradient Descent
Derivatives
More Derivative Examples
Computation graph
Derivatives with a Computation Graph
Logistic Regression Gradient Descent
Gradient Descent on m Examples
Python and Vectorization
Vectorization
More Vectorization Examples
Vectorizing Logistic Regression
Vectorizing Logistic Regression's Gradient Output
Broadcasting in Python
A note on python/numpy vectors
Quick tour of Jupyter/iPython Notebooks
Explanation of logistic regression cost function (optional)
Week 3 - Shallow neural networks
Shallow Neural Network
Neural Networks Overview
Neural Network Representation
Computing a Neural Network's Output
Vectorizing across multiple examples
Explanation for Vectorized Implementation
Activation functions
Why do you need non-linear activation functions?
Derivatives of activation functions
Gradient descent for Neural Networks
Backpropagation intuition (optional)
Random Initialization
Week 4 - Deep Neural Networks
Deep Neural Network
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?
Course 2 - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Week 1 - Practical aspects of Deep Learning
Setting up your Machine Learning Application
Train / Dev / Test sets
Bias / Variance
Basic Recipe for Machine Learning
Regularizing your neural network
Regularization
Why regularization reduces overfitting?
Dropout Regularization
Understanding Dropout
Other regularization methods
Setting up your optimization problem
Normalizing inputs
Vanishing / Exploding gradients
Weight Initialization for Deep Networks
Numerical approximation of gradients
Gradient checking
Gradient Checking Implementation Notes
Week 2 - Optimization algorithms
Optimization algorithms
Mini-batch gradient descent
Understanding mini-batch gradient descent
Exponentially weighted averages
Understanding exponentially weighted averages
Bias correction in exponentially weighted averages
Gradient descent with momentum
RMSprop
Adam optimization algorithm
Learning rate decay
The problem of local optima
Week 3 - Hyperparameter tuning, Batch Normalization and Programming Frameworks
Hyperparameter tuning
Tuning process
Using an appropriate scale to pick hyperparameters
Hyperparameters tuning in practice: Pandas vs. Caviar
Batch Normalization
Normalizing activations in a network
Fitting Batch Norm into a neural network
Why does Batch Norm work?
Batch Norm at test time
Multi-class classification
Softmax Regression
Training a softmax classifier
Introduction to programming frameworks
Deep learning frameworks
TensorFlow
Course 3 - Structuring Machine Learning Projects
Week 1 - ML Strategy (1)
Introduction to ML Strategy
Why ML Strategy
Orthogonalization
Comparing to human-level performance
Why human-level performance?
Avoidable bias
Understanding human-level performance
Surpassing human-level performance
Improving your model performance
Setting up your goal
Single number evaluation metric
Satisficing and Optimizing metric
Train/dev/test distributions
Size of the dev and test sets
When to change dev/test sets and metrics
Week 2 - ML Strategy (2)
Error Analysis
Carrying out error analysis
Cleaning up incorrectly labeled data
Build your first system quickly, then iterate
Mismatched training and dev/test set
Training and testing on different distributions
Bias and Variance with mismatched data distributions
Addressing data mismatch
Learning from multiple tasks
Transfer learning
Multi-task learning
End-to-end deep learning
What is end-to-end deep learning?
Whether to use end-to-end deep learning
Course 4 - Convolutional Neural Networks
Week 1 - Foundations of Convolutional Neural Networks
Convolutional Neural Networks
Computer Vision
Edge Detection Example
More Edge Detection
Padding
Strided Convolutions
Convolutions Over Volume
One Layer of a Convolutional Network
Simple Convolutional Network Example
Pooling Layers
CNN Example
Why Convolutions?
Week 2 - Deep convolutional models: case studies
Case studies
Why look at case studies?
Classic Networks
ResNets
Why ResNets Work
Networks in Networks and 1x1 Convolutions
Inception Network Motivation
Inception Network
Practical advices for using ConvNets
Using Open-Source Implementation
Transfer Learning
Data Augmentation
State of Computer Vision
Week 3 - Object detection
Detection algorithms
Object Localization
Landmark Detection
Object Detection
Convolutional Implementation of Sliding Windows
Bounding Box Predictions
Intersection Over Union
Non-max Suppression
Anchor Boxes
YOLO Algorithm
(Optional) Region Proposals
Week 4 - Special applications: Face recognition & Neural style transfer
Face Recognition
What is face recognition?
One Shot Learning
Siamese Network
Triplet Loss
Face Verification and Binary Classification
Neural Style Transfer
What is neural style transfer?
What are deep ConvNets learning?
Cost Function
Content Cost Function
Style Cost Function
1D and 3D Generalizations
Course 5 - Sequence Models
Week 1 - Recurrent Neural Networks
Recurrent Neural Networks
Why sequence models
Notation
Recurrent Neural Network Model
Backpropagation through time
Different types of RNNs
Language model and sequence generation
Sampling novel sequences
Vanishing gradients with RNNs
Gated Recurrent Unit (GRU)
Long Short Term Memory (LSTM)
Bidirectional RNN
Deep RNNs
Week 2 - Natural Language Processing & Word Embeddings
Introduction to Word Embeddings
Word Representation
Using word embeddings
Properties of word embeddings
Embedding matrix
Learning Word Embeddings: Word2vec & GloVe
Learning word embeddings
Word2Vec
Negative Sampling
GloVe word vectors
Applications using Word Embeddings
Sentiment Classification
Debiasing word embeddings
Week 3 - Sequence models & Attention mechanism
Various sequence to sequence architectures
Basic Models
Picking the most likely sentence
Beam Search
Refinements to Beam Search
Error analysis in beam search
Bleu Score (optional)
Attention Model Intuition
Attention Model
Speech recognition - Audio data
Speech recognition
Trigger Word Detection
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Why ML Strategy
Why ML Strategy
前言
如何是有效的去改善模型的效能。
內容
Motivating example
可以幫助模型改善效能的方向跟方法很多，該如何去有策略的選擇最有用的方向跟方法，讓模型可以快速改善其效能。
該學到的
為什麼需要 ML Strategy?
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