Keras Crash Cource

• 딥러닝과 관련된 여러가지를 Keras, Tensorflow로 학습하기 위해 제작하였습니다.
• 주로 이미지 분류를 수행하는데 활용되는 여러 기술을 위주로 작성되어 있습니다.

Requirements

• tensorflow==2.6.0 (최소 2.4.0 이상)
• keras==2.6.0

Objectives

• Ch1. Activations, Metrics, Optimizer and Loss Function
• [Ch2. Bayesian Model Space]
• Ch3. Basic Tensorflow
• Ch4. Basic Tensorflow Layers
• Ch5. Sequential Model
• Ch6. Functional API Model
• Ch7. Subclass API Model
• Ch8. Model of Keras Application
• Ch9. Model Training 1 - compile, fit
• Ch10. Callbacks
• Ch11. Data Generator - flow, from_directory
• Ch12. Custom Data Loader 1 - Pre-load Data
• Ch13. Custom Data Loader 2 - Batch-load Data
• Ch14. Model Training 2 - gradientTape
• Ch15. MIL - timedistributed layer
• Ch16. Custom Callbacks
• Ch17. Custom Layers
• Ch18. Custom Loss, Custom Metric
• Ch19. Special (History, Save, etc)

Author (저자)

Junseo Ko (고준서)

깃허브 / 유튜브

• Department of Applied Artificial Intelligence, Sungkyunkwan University
• Data Science & Artificial Intelligence Lab.
• RAONDATA

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Table of contents

• Ch1. Basic Functions
• Ch3. Basic Tensorflow
• Ch4. Basic Keras Layers
• Ch5. Sequential Model of Keras
• Ch6. Funtional Model of Keras
• Ch7. Subclass API Method
• Ch8. Model of Keras Application
• Ch9. Model Training 1 - compile, fit
• Ch10. Callbacks
• Ch11. ImageDataGenerator - flow, from_directory
• Ch12. Custom Data Loader 1 - Pre-load Data
• Ch13. Custom Data Loader 1 - Pre-load Data
• Ch14. Gradient Tape
• Ch15. timedistributed layer
• Ch16. Custom Callbacks
• Ch17. Custom Layer
• Ch18. Custom Loss, Metrics
• Ch19. Special Methods
• Ch20. Special Session