# ## 주메뉴

### AI를 위한 딥러닝

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• 곽일엽 • 주제분류
자연과학 >수학ㆍ물리ㆍ천문ㆍ지리 >통계학
• 강의학기
2022년 1학기
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2,480
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강의계획서 딥러닝 기초를 강의합니다. Tensorflow 와 keras 를 학습합니다. 기본적인 딥러닝 개념들 학습 후, 간단한 computer vision, timeseries, text 분석방법에 대해 공부해 봅니다. François Chollet 의 Deep Learning with Python 2판을 사용합니다.
Introduction to github, Markdown, Jupyter notebook #### 차시별 강의      1. Introduction to github, Markdown, Jupyter notebook Introduction to colab and github  Introduction to github, Markdown, Jupyter notebook Intro to Markdown  Ch1 What is deep learning Key factors behind deep learning’s rising popularity and future potential 2. Ch2 The mathematical building blocks of neural networks (1) A first example of a neural network  Ch2 The mathematical building blocks of neural networks (2) Tensors and tensor operations 3. Ch2 The mathematical building blocks of neural networks (3) How neural networks learn via backpropagation and gradient descent  Ch3 Introduction to Keras and TensorFlow A closer look at TensorFlow, Keras, and their relationship 4. Ch4 Getting started with neural networks: Classification and regression first examples of real-world machine learning workflows - classification and regression examples 5. Ch5 Fundamentals of machine learning Understanding the tension between generalization and optimization, the fundamental issue in machine learning  Ch6 The universal workflowof machine learning Steps fframing a machine learning problem, Steps fdeveloping a working model 6. Ch7 Working with Keras: A deep dive Creating Keras models, Using built-in Keras training and evaluation loops, Using Keras callbacks 7. Ch8 Introduction to deep learning for computer vision Understanding convolutional neural networks, Using data augmentation, Using a pretrained convnet, Fine-tuning a pretrained convnet 8. Ch9 Advanced deep learning for computer vision (1) The different branches of computer vision, Modern convnet architecture patterns 9. Ch9 Advanced deep learning for computer vision (2) Techniques for visualizing and interpreting what convnets learn  Ch10 Deep learning for timeseries Examples of machine learning tasks that involve timeseries data, Understanding recurrent neural networks, Advanced RNN 10. Ch11 Deep learning for text (1) Preprocessing text data for machine learning applications, Bag-of-words approaches and sequence-modeling approaches for text processing 11. Ch11 Deep learning for text (2) The Transformer architecture, Sequence-to-sequence learning 12. Ch12 Generative deep learning text generation, deep dream #### 연관 자료 #### 사용자 의견

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