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| 编号 | 课程名 | 时间轴 | 翻译 | 审核 |
|---|---|---|---|---|
| 01.01 | Welcome | AcceptedDoge | AcceptedDoge / Fei Li | AcceptedDoge |
| 01.02 | What is a neural network | AcceptedDoge | AcceptedDoge / Fei Li | AcceptedDoge |
| 01.03 | Supervised Learning with Neural Networks | AcceptedDoge | AcceptedDoge | AcceptedDoge |
| 01.04 | Why is Deep Learning taking off | AcceptedDoge | AcceptedDoge | AcceptedDoge |
| 01.05 | About this Course | AcceptedDoge | AcceptedDoge | AcceptedDoge |
| 01.06 | Course Resources | AcceptedDoge | AcceptedDoge | AcceptedDoge |
| 02.01 | Binary Classification | Coursera | 谢小彬 | AcceptedDoge |
| 02.02 | Logistic Regression | AcceptedDoge | 宋泽翰 | AcceptedDoge |
| 02.03 | Logistic Regression Cost Function | Coursera | 陈明 | AcceptedDoge |
| 02.04 | Gradient Descent | Coursera | 曹越 | AcceptedDoge |
| 02.05 | Derivatives | AcceptedDoge / 陈倩倩 | 陈倩倩 | AcceptedDoge |
| 02.06 | More Derivative Examples | AcceptedDoge | 谢小彬 | AcceptedDoge |
| 02.07 | Computation graph | AcceptedDoge | 刘振卫 | AcceptedDoge |
| 02.08 | Derivatives with a Computation Graph | AcceptedDoge | 史红光 | 李智锋 |
| 02.09 | Logistic Regression Gradient Descent | AcceptedDoge | 彭世锦 | AcceptedDoge |
| 02.10 | Gradient Descent on m Examples | 庞伟 | 庞伟 | AcceptedDoge |
| 02.11 | Vectorization | 杨先圣 | 杨先圣 | 李智锋 |
| 02.12 | More Examples of Vectorization | 舒正英 | 舒正英 | AcceptedDoge |
| 02.13 | Vectorizing Logistic Regression | 张乐亨 | 张乐亨 | AcceptedDoge |
| 02.14 | Vectorizing Logistic Regression's Gradient Output | 李晶 | 李晶 | AcceptedDoge |
| 02.15 | Broadcasting in Python | 李晶 | 李晶 | AcceptedDoge |
| 02.16 | A note on python/numpy vectors | 张雲飞 | 张雲飞 | AcceptedDoge |
| 02.17 | Quick tour of Jupyter/iPython Notebooks | 庞伟 | 舒正英 | AcceptedDoge |
| 02.18 | Explanation of logistic regression cost function (optional) | 彭世锦 | 彭世锦 | AcceptedDoge |
- @wetstreet fixed some grammatical and translation errors #1
- @lzwhard 2.14向量化逻辑回归梯度下降 #2