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인덱스 번역, 환경 파일 추가

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Haesun Park
2018-04-24 16:13:15 +09:00
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environment.yml
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@@ -6,6 +6,9 @@ dependencies:
- jupyter - jupyter
- pandas - pandas
- pillow - pillow
- nltk
- pip: - pip:
- tensorflow-gpu - tensorflow-gpu
- graphviz
- watermark - watermark
- urlextract

93
index.ipynb
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@@ -4,44 +4,44 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# Machine Learning Notebooks\n", "# 핸즈온 머신러닝 노트북\n",
"\n", "\n",
"*Welcome to the Machine Learning Notebooks!*\n", "*핸즈온 머신러닝 레파지토리에 오신걸 환영합니다!*\n",
"\n", "\n",
"[Prerequisites](#Prerequisites) (see below)\n", "[필요한 기술](#Prerequisites) (아래를 참고하세요)\n",
"\n", "\n",
"## Notebooks\n", "## 노트북\n",
"1. [The Machine Learning landscape](01_the_machine_learning_landscape.ipynb)\n", "1. [한눈에 보는 머신러닝](01_the_machine_learning_landscape.ipynb)\n",
"2. [End-to-end Machine Learning project](02_end_to_end_machine_learning_project.ipynb)\n", "2. [머신러닝 프로젝트 처음부터 끝까지](02_end_to_end_machine_learning_project.ipynb)\n",
"3. [Classification](03_classification.ipynb)\n", "3. [분류](03_classification.ipynb)\n",
"4. [Training Linear Models](04_training_linear_models.ipynb)\n", "4. [모델 훈련](04_training_linear_models.ipynb)\n",
"5. [Support Vector Machines](05_support_vector_machines.ipynb)\n", "5. [서포트 벡터 머신](05_support_vector_machines.ipynb)\n",
"6. [Decision Trees](06_decision_trees.ipynb)\n", "6. [결정 트리](06_decision_trees.ipynb)\n",
"7. [Ensemble Learning and Random Forests](07_ensemble_learning_and_random_forests.ipynb)\n", "7. [앙상블 학습과 랜덤 포레스트](07_ensemble_learning_and_random_forests.ipynb)\n",
"8. [Dimensionality Reduction](08_dimensionality_reduction.ipynb)\n", "8. [차원 축소](08_dimensionality_reduction.ipynb)\n",
"9. [Up and running with TensorFlow](09_up_and_running_with_tensorflow.ipynb)\n", "9. [텐서플로 시작하기](09_up_and_running_with_tensorflow.ipynb)\n",
"10. [Introduction to Artificial Neural Networks](10_introduction_to_artificial_neural_networks.ipynb)\n", "10. [인공 신경망 소개](10_introduction_to_artificial_neural_networks.ipynb)\n",
"11. [Deep Learning](11_deep_learning.ipynb)\n", "11. [심층 신경망 훈련](11_deep_learning.ipynb)\n",
"12. [Distributed TensorFlow](12_distributed_tensorflow.ipynb)\n", "12. [다중 머신과 장치를 위한 분산 텐서플로](12_distributed_tensorflow.ipynb)\n",
"13. [Convolutional Neural Networks](13_convolutional_neural_networks.ipynb)\n", "13. [합성곱 신경망](13_convolutional_neural_networks.ipynb)\n",
"14. [Recurrent Neural Networks](14_recurrent_neural_networks.ipynb)\n", "14. [순환 신경망](14_recurrent_neural_networks.ipynb)\n",
"15. [Autoencoders](15_autoencoders.ipynb)\n", "15. [오토인코더](15_autoencoders.ipynb)\n",
"16. [Reinforcement Learning](16_reinforcement_learning.ipynb)\n", "16. [강화 학습](16_reinforcement_learning.ipynb)\n",
"\n", "\n",
"## Scientific Python tutorials\n", "## 파이썬 과학 라이브러리 튜토리얼\n",
"* [NumPy](tools_numpy.ipynb)\n", "* [넘파이(NumPy)](tools_numpy.ipynb)\n",
"* [Matplotlib](tools_matplotlib.ipynb)\n", "* [맷플롯립(Matplotlib)](tools_matplotlib.ipynb)\n",
"* [Pandas](tools_pandas.ipynb)\n", "* [판다스(Pandas)](tools_pandas.ipynb)\n",
"\n", "\n",
"## Math Tutorials\n", "## 수학 튜토리얼\n",
"* [Linear Algebra](math_linear_algebra.ipynb)\n", "* [선형 대수](math_linear_algebra.ipynb)\n",
"* Calculus (coming soon)\n", "* 미적분 (coming soon)\n",
"\n", "\n",
"## Extra Material\n", "## 부가 자료\n",
"* [Capsule Networks](extra_capsnets.ipynb)\n", "* [캡슐 네트워크(Capsule Networks)](extra_capsnets.ipynb)\n",
"\n", "\n",
"## Misc.\n", "## 그외\n",
"* [Equations](book_equations.ipynb) (list of equations in the book)\n" "* [수식](book_equations.ipynb) (책에 나온 수식을 모아 놓았습니다)\n"
] ]
}, },
{ {
@@ -50,29 +50,16 @@
"collapsed": true "collapsed": true
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"source": [ "source": [
"## Prerequisites\n", "## 필요한 기술\n",
"### To understand\n", "### 이해를 위해서는\n",
"* **Python** – you don't need to be an expert python programmer, but you do need to know the basics. If you don't, the official [Python tutorial](https://docs.python.org/3/tutorial/) is a good place to start.\n", "* **파이썬** – 파이썬 전문가일 필요는 없지만 기초적인 내용은 알고 있어야 합니다. 파이썬이 처음이라면 [파이썬 공식 튜토리얼](https://docs.python.org/3/tutorial/)을 참고하세요. 다른 프로그래밍 언어를 조금 알고 있다면 [번갯불에 파이썬 구워먹기](https://github.com/rickiepark/WhirlwindTourOfPython)를 참고하면 금방 배울 수 있습니다.\n",
"* **Scientific Python** – We will be using a few popular python libraries, in particular NumPy, matplotlib and pandas. If you are not familiar with these libraries, you should probably start by going through the tutorials in the Tools section (especially NumPy).\n", "* **파이썬 과학 패키지** – 여기서는 유명한 파이썬 라이브러리를 사용합니다. 특히 넘파이, 맷플롯립, 판다스를 사용합니다. 이런 라이브러리에 익숙하지 않다면 \"파이썬 과학 라이브러리 튜토리얼\"을 먼저 참고하세요(특히 넘파이).\n",
"* **Math** – We will also use some notions of Linear Algebra, Calculus, Statistics and Probability theory. You should be able to follow along if you learned these in the past as it won't be very advanced, but if you don't know about these topics or you need a refresher then go through the appropriate introduction in the Math section.\n", "* **수학** – 이 책은 선형 대수, 미적분, 통계, 확률 이론 개념을 조금 사용합니다. 여기서는 매우 깊게 들어가지 않으므로 이전에 배운적이 있다면 따라갈 수 있을 것입니다. 만약 이 부분을 잘 모른다면 \"수학 튜토리얼\" 섹션에 있는 자료를 참고하세요.\n",
"\n", "\n",
"### To run the examples\n", "### 예제를 실행하기 위해서는\n",
"* **Jupyter** – These notebooks are based on Jupyter. If you just plan to read without running any code, there's really nothing more to know, just keep reading! But if you want to experiment with the code examples you need to:\n", "* **주피터** – 이 노트북들은 주피터를 기반으로 합니다. 코드를 그냥 읽기만 한다면 더이상 무언가를 할 필요는 없습니다. 하지만 이 코드를 테스트해 보려면: * [설치](https://github.com/rickiepark/handson-ml/#%EC%84%A4%EC%B9%98)을 따라 필요한 패키지를 설치하세요.\n",
" * follow the [installation instructions](https://github.com/ageron/handson-ml/#installation),\n", " * 주피터를 사용하는 법을 배워야합니다. Help 메뉴에서 User Interface Tour를 선택해 보세요."
" * learn how to use Jupyter. Start the User Interface Tour from the Help menu.\n",
"\n",
"### To activate extensions\n",
"* If this is an interactive session (see above), you may want to turn on a few Jupyter extensions by going to the [Extension Configuration](../nbextensions/) page. In particular the \"*Table of Contents (2)*\" extension is quite useful.\n"
] ]
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"metadata": { "metadata": {
@@ -91,7 +78,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.5.2" "version": "3.5.5"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {