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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep Learning Zero to All - Tensorflow
| Date | Stars |
|---|---|
| 2026-07-24 | 285 |
| 2026-07-25 | 285 |
| 2026-07-28 | 285 |
| 2026-07-30 | 285 |
| 2026-07-31 | 285 |
| 2026-08-06 | 285 |
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# 모두를 위한 딥러닝 시즌 2 : 모두가 만드는 모두를 위한 딥러닝 모두가 만드는 모두를 위한 딥러닝 시즌 2에 오신 여러분들 환영합니다. ## Getting Started 아래 링크에서 슬라이드와 영상을 통해 학습을 시작할 수 있습니다. * Slide: http://bit.ly/2LQMKvk * YouTube: http://bit.ly/2HHrybT ### Docker 사용자를 위한 안내 동일한 실습 환경을 위해 docker를 사용하실 분은 [docker_user_guide.md](docker_user_guide.md) 파일을 참고하세요! :) ### Install Requirements ```bash pip install -r requirements.txt ``` --- ## TensorFlow Deep Learning Zero to All - TensorFlow 모든 코드는 Tensorflow 1.12(stable)를 기반으로 작성했으며 Tensorflow 2.0이 출시되는 대로 추후 반영할 예정입니다. ## Standarad of Code 코드는 Tensorflow 공식 홈페이지 권장에 따라 Keras + Eager로 작성했으며 Session 버전은 code_session_version / Keras 버전은 other에서 확인하실 수 있습니다. ## Contributions/Comments 언제나 여러분들의 참여를 환영합니다. Comments나 Pull requests를 남겨주세요. We always welcome your comments and pull requests. ## 목차 ### PART 1: Basic Machine Learning * Lec 01: 기본적인 Machine Learning의 용어와 개념 설명 * Lab 01: (추가 예정) * Lec 02: Simple Linear Regression * Lab 02: Simple Linear Regression를 TensorFlow로 구현하기 * Lec 03: Linear Regression and How to minimize cost * Lab 03: Linear Regression and How to minimize cost를 TensorFlow로 구현하기 * Lec 04: Multi-variable Linear Regression * Lab 04: Multi-variable Linear Regression를 TensorFlow로 구현하기 * Lec 05-1: Logistic Regression/Classification의 소개 * Lec 05-2: Logistic Regression/Classification의 cost 함수, 최소화 * Lab 05-3: Logistic Regression/Classification를 TensorFlow로 구현하기 * Lec 06-1: Softmax Regression: 기본 개념 소개 * Lec 06-2: Softmax Classifier의 cost 함수 * Lab 06-1: Softmax classifier를 TensorFlow로 구현하기 * Lab 06-2: Fancy Softmax classifier를 TensorFlow로 구현하기 * Lab 07-1: Application & Tips: 학습률(Learning Rate)과 데이터 전처리(Data Preprocessing) * Lab 07-2-1: Application & Tips: 오버피팅(Overfitting) & Solutions * Lab 07-2-2: Application & Tips: 학습률, 전처리, 오버피팅을 TensorFlow로 실습 * Lab 07-3-1: Application & Tips: Data & Learning * Lab 07-3-2: Application & Tips: 다양한 Dataset으로 실습 ### PART 2: Basic Deep Learning * Lec 08-1: 딥러닝의 기본 개념: 시작과 XOR 문제 * Lec 08-2: 딥러닝의 기본 개념 2: Back-propagation 과 2006/2007 '딥'의 출현 * Lec 09-1: XOR 문제 딥러닝으로 풀기 * Lec 09-2: 딥넷트웍 학습 시키기 (backpropagation) * Lab 09-1: Neural Net for XOR * Lab 09-2: Tensorboard (Neural Net for XOR) * Lab 10-1: Sigmoid 보다 ReLU가 더 좋아 * Lab 10-2: Weight 초기화 잘해보자 * Lab 10-3: Dropout * Lab 10-4: Batch Normalization ### PART 3: Convolutional Neural Network * Lec 11-1: ConvNet의 Conv 레이어 만들기 * Lec 11-2: ConvNet Max pooling 과 Full Network * Lec 11-3: ConvNet의 활용 예 * Lab 11-0-1: CNN Basic: Convolution * Lab 11-0-2: CNN Basic: Pooling * Lab 11-1: mnist cnn keras sequential eager * Lab 11-2: mnist cnn keras functional eager * Lab-11-3: mnist cnn keras subclassing eager * Lab-11-4: mnist cnn keras ensemble eager * Lab-11-5: mnist cnn best keras eager ### PART 4: Recurrent Neural Network * Lec 12: NN의 꽃 RNN 이야기 * Lab 12-0: rnn basics * Lab 12-1: many to one (word sentiment classification) * Lab 12-2: many to one stacked (sentence classification, stacked) * Lab 12-3: many to many (simple pos-tagger training) * Lab 12-4: many to many bidirectional (simpled pos-tagger training, bidirectional) * Lab 12-5: seq to seq (simple neural machine translation) * Lab 12-6: seq to seq with attention (simple neural machine translation, attention) -------------------------- ### 함께 만든 이들 Main Instructor * Prof. Kim (https://github.com/hunkim) Main Creator * 김보섭 (https://github.com/aisolab) * 김수상 (https://github.com/healess) * 김준호 (https://github.com/taki0112) * 신성진 (https://github.com/aiscientist) * 이승준 (https://github.com/FinanceData) * 이진원 (https://github.com/jwlee-ml) Docker Developer * 오상준 (https://github.com/juneoh) Support * 네이버 커넥트재단 : 이효은, 장지수, 임우담
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a4a3386083247c51, topic:tensorflow
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