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Dr. Serendipity

Dr. Serendipity

  • 노트(Notes)
    • 도움이 될 수 있는 정보(Potentially Useful Info.)
    • 맛집(Good Restaurants)
    • 문화생활
    • 여행(Trip, Travel)
    • 건강(Health)
    • 주제별 노트(Notes by Subjects)
      • 언어(Language)
      • 수학(Mathematics)
      • 과학(Science)
      • 컴퓨터(Computer)
      • 금융 공학(Financial Engineering)
        • R을 활용한 퀀트 전략 구현(Quant Strategy Implementation Using R)
      • 의학(Medical Science)
      • 영양과 건강(Nutrition & Health)
      • 인공지능(Artificial Intelligence, AI)
        • Artificial Intelligence
          • 1. Introduction to Artificial Intelligence
            • 1-1. Welcome to Artificial Intelligence
            • 1-2. Getting Help
            • 1-3. Get Help with Your Account
            • 1-4. Intro to Artificial Intelligence
            • 1-5. Solving Sudoku With AI
            • 1-6. Workspaces
            • 1-7. Setting Up Your Environment with Anaconda
            • 1-8. Build a Sudoku Solver
            • 1-9. Jobs in AI
          • 2. Constraint Satisfaction Problems
            • 2-1. Constraint Satisfaction Problems
            • 2-2. CSP Coding Exercise
            • 2-3. Additional Readings
          • 3. Classical Search
            • 3-1. Introduction
            • 3-2. Uninformed Search
            • 3-3. Informed Search
            • 3-4. Classroom Exercise: Search
            • 3-5. Additional Search Topics
          • 4. Automated Planning
            • 4-1. Symbolic Logic & Reasoning
            • 4-2. Introduction to Planning
            • 4-3. Classical Planning
            • 4-4. PROJECT: Build a Forward-Planning Agent
            • 4-5. Additional Planning Topics
          • 5. (Optional) Optimization Problems
            • 5-1. Introduction
            • 5-2. Hill Climbing
            • 5-3. Simulated Annealing
            • 5-4. Genetic Algorithms
            • 5-5. Optimization Exercise
            • 5-6. Additional Optimization Topics
          • 6. Adversarial Search
            • 6-1. Search in Multiagent Domains
            • 6-2. Optimizing Minimax Search
            • 6-3. PROJECT: Build an Adversarial Game Playing Agent
            • 6-4. Extending Minimax Search
            • 6-5. Additional Adversarial Search Topics
          • 7. Probabilistic Models
            • 7-1. Probability
            • 7-2. Naive Bayes
            • 7-3. Bayes Nets
            • 7-4. Inference in Bayes Nets
            • 7-5. Hidden Markov Models
            • 7-6. PROJECT: Part of Speech Tagging
            • 7-7. Dynamic Time Warping
            • 7-8. Additional Topics in PGMs
          • 8. After the AI Nanodegree Program
            • 8-1. Additional Topics in AI
          • 9. Extracurricular
            • 9-1. Extracurricular
              • 9-1-1. Hidden Markov Models
              • 9-1-2. Advanced HMMs
            • 9-2. Career Services
              • 9-2-1. PROJECT: Take 30 Min to Improve your LinkedIn
              • 9-2-2. PROJECT: Optimize Your GitHub Profile
        • Artificial Intelligence for Trading
          • 1. Quantitative Trading
            • 1-1. Welcome to the Nanodegree Program
            • 1-2. Getting Help
            • 1-3. Get Help with Your Account
            • 1-4. Stock Prices
            • 1-5. Market Mechanics
            • 1-6. Data Processing
            • 1-7. Stock Returns
            • 1-8. Momentum Trading
            • 1-9. Project 1: Trading with Momentum
            • 1-10. Quant Workflow
            • 1-11. Outliers and Filtering
            • 1-12. Regression
            • 1-13. Time Series Modeling
            • 1-14. Volatility
            • 1-15. Pairs Trading and Mean Reversion
            • 1-16. Project 2: Breakout Strategy
            • 1-17. Stocks, Indices, Funds
            • 1-18. ETFs
            • 1-19. Portfolio Risk and Return
            • 1-20. Portfolio Optimization
            • 1-21. Project 3: Smart Beta and Portfolio Optimization
            • 1-22. Factors
            • 1-23. Factor Models and Types of Factors
            • 1-24. Risk Factor Models
            • 1-25. Time Series and Cross Sectional Risk Models
            • 1-26. Risk Factor Models with PCA
            • 1-27. Alpha Factors
            • 1-28. Alpha Factor Research Methods
            • 1-29. Advanced Portfolio Optimization
            • 1-30. Project 4: Alpha Research and Factor Modeling
          • 2. AI Algorithms in Trading
            • 2-1. Welcome To Term II
            • 2-2. Intro to Natural Language Processing
            • 2-3. Text Processing
            • 2-4. Feature Extraction
            • 2-5. Financial Statements
            • 2-6. Basic NLP Analysis
            • 2-7. Project 5: NLP on Financial Statements
            • 2-8. Introduction to Neural Networks
            • 2-9. Training Neural Networks
            • 2-10. Deep Learning with PyTorch
            • 2-11. Recurrent Neural Networks
            • 2-12. Embeddings & Word2Vec
            • 2-13. Sentiment Prediction RNN
            • 2-14. Project 6: Sentiment Analysis with Neural Networks
            • 2-15. Overview
            • 2-16. Decision Trees
            • 2-17. Model Testing and Evaluation
            • 2-18. Random Forests
            • 2-19. Feature Engineering
            • 2-20. Overlapping Labels
            • 2-21. Feature Importance
            • 2-22. Project 7: Combining Signals for Enhanced Alpha
            • 2-23. Intro to Backtesting
            • 2-24. Optimization with Transaction Costs
            • 2-25. Attribution
            • 2-26. Project 8: Backtesting
          • 3. Extracurricular
            • 3-1. Python Refresher
              • 3-1-1. Why Python Programming
              • 3-1-2. Data Types and Operators
              • 3-1-3. Control Flow
              • 3-1-4. Functions
              • 3-1-5. Scripting
            • 3-2. Linear Algebra
              • 3-2-1. Introduction
              • 3-2-2. Vectors
              • 3-2-3. Linear Combination
              • 3-2-4. Linear Transformation and Matrices
            • 3-3. Jupyter Notebook, Numpy, and Pandas
              • 3-3-1. Jupyter Notebooks
              • 3-3-2. NumPy
              • 3-3-3. Pandas
            • 3-4. Statistics
              • 3-4-1. Descriptive Statistics – Part I
              • 3-4-2. Descriptive Statistics – Part II
              • 3-4-3. Admissions Case Study
              • 3-4-4. Probability
              • 3-4-5. Binomial Distribution
              • 3-4-6. Conditional Probability
              • 3-4-7. Bayes Rule
              • 3-4-8. Python Probability Practice
              • 3-4-9. Normal Distribution Theory
              • 3-4-10. Sampling distributions and the Central Limit Theorem
              • 3-4-11. Confidence Intervals
              • 3-4-12. Hypothesis Testing
              • 3-4-13. Case Study: A/B tests
            • 3-5. Machine Learning
              • 3-5-1. Linear Regression
              • 3-5-2. Naive Bayes
              • 3-5-3. Clustering
              • 3-5-4. Decision Trees
              • 3-5-5. Introduction to Kalman Filters
            • 3-6. Deep Learning
              • 3-6-1. Introduction to Neural Networks
            • 3-7. Computer Vision
              • 3-7-1. Intro to Computer Vision
            • 3-8. Natural Language Processing
              • 3-8-1. Intro to NLP
            • 3-9. Career Services
              • 3-9-1. PROJECT: Take 30 Min to Improve your LinkedIn
              • 3-9-2. PROJECT: Optimize Your GitHub Profile
        • Computer Vision
          • 1. Introduction to Computer Vision
            • 1-1. Welcome to Computer Vision
            • 1-2. Getting Help
            • 1-3. Get Help with Your Account
            • 1-4. Image Representation & Classification
            • 1-5. Convolutional Filters and Edge Detection
            • 1-6. Types of Features & Image Segmentation
            • 1-7. Feature Vectors
            • 1-8. CNN Layers and Feature Visualization
            • 1-9. Project: Facial Keypoint Detection
            • 1-10. Jobs in Computer Vision
          • 2. Optional: Cloud Computing
            • 2-1. Cloud Computing with Google Cloud
            • 2-2. Optional: Cloud Computing with AWS
          • 3. Advanced Computer Vision & Deep Learning
            • 3-1. Advanced CNN Architectures
            • 3-2. YOLO
            • 3-3. RNN’s
            • 3-4. Long Short-Term Memory Networks (LSTMs)
            • 3-5. Hyperparameters
            • 3-6. Optional: Attention Mechanisms
            • 3-7. Image Captioning
            • 3-8. Project: Image Captioning
            • 3-9. Optional: Cloud Computing with AWS
          • 4. Object Tracking and Localization
            • 4-1. Introduction to Motion
            • 4-2. Robot Localization
            • 4-3. Mini-project: 2D Histogram Filter
            • 4-4. Introduction to Kalman Filters
            • 4-5. Representing State and Motion
            • 4-6. Matrices and Transformation of State
            • 4-7. Simultaneous Localization and Mapping
            • 4-8. Optional: Vehicle Motion and Calculus
            • 4-9. Project: Landmark Detection & Tracking (SLAM)
          • 5. Extracurricular
            • 5-1. Applications of Computer Vision and Deep Learning
              • 5-1-1. Applying Deep Learning Models
            • 5-2. Review: Training A Neural Network
              • 5-2-1. Feedforward and Backpropagation
              • 5-2-2. Training Neural Networks
              • 5-2-3. Deep Learning with PyTorch
            • 5-3. Skin Cancer Detection
              • 5-3-1. Deep Learning for Cancer Detection with Sebastian Thrun
            • 5-4. Text Sentiment Analysis
              • 5-4-1. Sentiment Analysis
            • 5-5. More Deep Learning Models
              • 5-5-1. Fully-Convolutional Neural Networks & Semantic Segmentation
            • 5-6. C++ Programming
              • 5-6-1. C++ Getting Started
              • 5-6-2. C++ Vectors
              • 5-6-3. Practical C++
              • 5-6-4. C++ Object Oriented Programming
              • 5-6-5. Python and C++ Speed
              • 5-6-6. C++ Intro to Optimization
              • 5-6-7. C++ Optimization Practice
              • 5-6-8. Project: Optimize Histogram Filter
            • 5-7. Career Services
              • 5-7-1. PROJECT: Take 30 Min to Improve your LinkedIn
              • 5-7-2. PROJECT: Optimize Your GitHub Profile
        • Deep Reinforcement Learning
          • 1. Introduction to Deep Reinforcement Learning
            • 1-1. Welcome to Deep Reinforcement Learning
            • 1-2. Getting Help
            • 1-3. Get Help with Your Account
            • 1-4. Learning Plan
            • 1-5. Introduction to RL
            • 1-6. The RL Framework: The Problem
            • 1-7. The RL Framework: The Solution
            • 1-8. Monte Carlo Methods
            • 1-9. Temporal-Difference Methods
            • 1-10. Solve OpenAI Gym’s Taxi-v2 Task
            • 1-11. RL in Continuous Spaces
            • 1-12. What’s Next?
          • 2. Value-Based Methods
            • 2-1. Study Plan
            • 2-2. Deep Q-Networks
            • 2-3. PROJECT: Navigation
            • 2-4. Opportunities in Deep Reinforcement Learning
          • 3. Policy-Based Methods
            • 3-1. Study Plan
            • 3-2. Introduction to Policy-Based Methods
            • 3-3. Policy Gradient Methods
            • 3-4. Proximal Policy Optimization
            • 3-5. Actor-Critic Methods
            • 3-6. Deep RL for Finance (Optional)
            • 3-7. PROJECT: Continuous Control
          • 4. Multi-Agent Reinforcement Learning
            • 4-1. Study Plan
            • 4-2. Introduction to Multi-Agent RL
            • 4-3. Case Study: AlphaZero
            • 4-4. PROJECT: Collaboration and Competition
          • 5. Extracurricular
            • 5-1. Special Topics in Deep Reinforcement Learning
              • 5-1-1. Dynamic Programming
            • 5-2. Neural Networks in PyTorch
              • 5-2-1. Neural Networks
              • 5-2-2. Convolutional Neural Networks
              • 5-2-3. Deep Learning with PyTorch
            • 5-3. Computing Resources
              • 5-3-1. Udacity Workspaces
            • 5-4. C++ Programming
              • 5-4-1. C++ Getting Started
              • 5-4-2. C++ Vectors
              • 5-4-3. Practical C++
              • 5-4-4. C++ Object Oriented Programming
              • 5-4-5. C++ Intro to Optimization
              • 5-4-6. C++ Optimization Practice
            • 5-5. Career Services
              • 5-5-1. PROJECT: Take 30 Min to Improve your LinkedIn
              • 5-5-2. PROJECT: Optimize Your GitHub Profile
        • Natural Language Processing
          • 1. Introduction to Natural Language Processing
            • 1-1. Welcome to Natural Language Processing
            • 1-2. Getting Help
            • 1-3. Get Help with Your Account
            • 1-4. Intro to NLP
            • 1-5. Text Processing
            • 1-6. Spam Classifier with Naive Bayes
            • 1-7. Part of Speech Tagging with HMMs
            • 1-8. Project: Part of Speech Tagging
            • 1-9. (Optional) IBM Watson Bookworm Lab
            • 1-10. Jobs in NLP
          • 2. Computing with Natural Language
            • 2-1. Feature extraction and embeddings
            • 2-2. Topic Modeling
            • 2-3. Sentiment Analysis
            • 2-4. Sequence to Sequence
            • 2-5. Deep Learning Attention
            • 2-6. RNN Keras Lab
            • 2-7. Project: Machine Translation
          • 3. Communicating with Natural Language
            • 3-1. Intro to Voice User Interfaces
            • 3-2. (Optional) Alexa History Skill
            • 3-3. Speech Recognition
            • 3-4. Project: DNN Speech Recognizer
          • 4. Extracurricular
            • 4-1. Recurrent Neural Networks
              • 4-1-1. Recurrent Neural Networks
              • 4-1-2. Long Short-Term Memory Networks (LSTM)
              • 4-1-3. Hyperparameters
            • 4-2. Keras
              • 4-2-1. Keras
            • 4-3. Sentiment Analysis Extras
              • 4-3-1. Sentiment Analysis with Andrew Trask
              • 4-3-2. Sentiment Prediction RNN
            • 4-4. TensorFlow
              • 4-4-1. TensorFlow
            • 4-5. Embeddings and Word2Vec
              • 4-5-1. Embeddings and Word2Vec
            • 4-6. PyTorch
              • 4-6-1. Introduction to PyTorch
              • 4-6-2. Embeddings & Word2Vec
              • 4-6-3. Implementation of RNN & LSTM
              • 4-6-4. Deploying PyTorch Models
            • 4-7. Additional Text Preprocessing
              • 4-7-1. Python Regular Expression
              • 4-7-2. BeautifulSoup Library
            • 4-8. Career Services
              • 4-8-1. PROJECT: Take 30 Min to Improve your LinkedIn
              • 4-8-2. PROJECT: Optimize Your GitHub Profile
  • 시험 정보(Tests Info.)
    • 수능(Korean CSAT)
      • 국어(Korean)
      • 수학(Math)
      • 영어(English)
      • 한국사(Korean History)
      • 탐구(Inquiry)
        • 사회, 과학(Social Studies and Science)
        • 사회(Social Studies)
          • 국사(Korean History (from prehistory to early modern history))
          • 한국 근·현대사(Korean Modern and Contemporary History)
          • 동아시아사(East Asia History)
          • 세계사(World History)
          • 경제(Economics)
          • 경제 지리(Economic Geography)
          • 한국 지리(Korean Geography)
          • 윤리(Ethics)
          • 윤리와 사상(Ethics and Thoughts)
          • 생활과 윤리(Life and Ethics)
          • 사회·문화(Society and Culture)
          • 세계 지리(World Geography)
          • 정치(Politics)
          • 법과 사회(Law and Society)
          • 정치와 법, 법과 정치(Laws, Government and Politics)
        • 과학(Science)
          • 물리학Ⅰ, 물리Ⅰ(Physics Ⅰ)
          • 물리학Ⅱ, 물리Ⅱ(Physics Ⅱ)
          • 화학Ⅰ(Chemistry Ⅰ)
          • 화학Ⅱ(Chemistry Ⅱ)
          • 생물학Ⅰ, 생명 과학Ⅰ(Biology Ⅰ)
          • 생물학Ⅱ, 생명 과학Ⅱ(Biology Ⅱ)
          • 지구 과학Ⅰ(Earth Science Ⅰ)
          • 지구 과학Ⅱ(Earth Science Ⅱ)
        • 직업(Vocational Education)
          • 인간 발달(Human Development)
          • 식품과 영양(Food and Nutrition)
          • 가사·실업1(Home EconomicsㆍVocational Education Ⅰ)
          • 가사·실업2(Home EconomicsㆍVocational Education Ⅱ)
          • 생활 서비스 산업의 이해(Understanding of Living Service Industry)
          • 회계 원리(Principles of Accounting)
          • 상업 경제(Commercial Economics)
          • 상업 정보1(Commercial Information Ⅰ)
          • 상업 정보2(Commercial Information Ⅱ)
          • 농업 이해(Understanding of Agriculture)
          • 농업 기초 기술(Basic Agriculture)
          • 농생명 산업1(Agriculture and Biotechnology Industry Ⅰ)
          • 농생명 산업2(Agriculture and Biotechnology Industry Ⅱ)
          • 농업 정보 관리(Agricultural Information Management)
          • 디자인 일반(General Design)
          • 기초 제도(Basic Drafting)
          • 공업 입문(Introduction to Industry)
          • 공업 일반(General Industry)
          • 공업 Ⅰ(Industry Ⅰ)
          • 공업 Ⅱ(Industry Ⅱ)
          • 수산 일반(Introduction to Fisheries)
          • 수산·해운 산업 기초(Basic FisheryㆍShipping Industry)
          • 수산·해운1(FisheryㆍMaritime Transformation Ⅰ)
          • 수산·해운2(FisheryㆍMaritime Transformation Ⅱ)
          • 수산·해운 정보 처리(FisheryㆍShipping Information Processing)
          • 해양의 이해(Understanding of Marine)
          • 해양 일반(General Oceanography)
          • 해사 일반(Maritime)
          • 정보 기술 기초(Basic Information Technology)
          • 컴퓨터 일반(General Computers)
          • 프로그래밍(Programming)
      • 제2외국어(Second Foreign Language)
        • 독일어Ⅰ(German Ⅰ)
        • 프랑스어Ⅰ(French Ⅰ)
        • 에스파냐어Ⅰ, 스페인어Ⅰ(Spanish Ⅰ)
        • 중국어Ⅰ(Chinese Ⅰ)
        • 일본어Ⅰ(Japanese Ⅰ)
        • 러시아어Ⅰ(Russian Ⅰ)
        • 아랍어Ⅰ(Arabic Ⅰ)
        • 기초 베트남어(Basic Vietnamese)
        • 베트남어Ⅰ(Vietnamese Ⅰ)
        • 한문(Chinese Characters and Classics)
        • 한문Ⅰ(Chinese Characters and Classics Ⅰ)
  • 와인(Wines)

전경이 예쁜곳

급여행, 제주도 서귀포 칼호텔에서 2박3일을

2022-12-25 by Serendipity Owl

갑자기 떠나게 된 제주도!! 2박3일의 여행에서 서귀포에 있는 칼호텔에서 머물게 되었다. 컨디션이 따라주지 않아 내가 할 수 있는만큼의 사진을 찍어보고 기억나는대로 의식의 흐름대로 후기를 남겨본다

Categories 여행(Trip, Travel) Tags 오래되었지만관리가잘된, 전경이 예쁜곳, 제주도 서귀포 칼호텔 Leave a comment
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