http://pythonkim.tistory.com/19

http://neuralix.blogspot.kr/2014/01/blog-post_626.html

https://codeonweb.com/entry/12045839-0aa9-4bad-8c7e-336b89401e10

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# Category: Research

## Tensorflow 관련 사이트

## tensorflow 예제 및 실용 코드

# TensorFlow Examples

## Tutorial index

#### 0 – Prerequisite

#### 1 – Introduction

#### 2 – Basic Models

#### 3 – Neural Networks

#### 4 – Utilities

#### 5 – Multi GPU

## Dataset

## More Examples

### Tutorials

### Basics

### Computer Vision

### Natural Language Processing

### Reinforcement Learning

### Others

### Notebooks

### Extending TensorFlow

## Dependencies

## Tensorflow Study

Documents for Development and Engineer

노력이 배신한다고 하지만, 그 노력이 언젠가 인정 받는 것이라는…

Research ML, Program Technology, ETC…

tensorflow는 모델을 기반으로 학습하고 그 데이터를 예측하는 시스템으로 분석된다.

해당건에 대하여 학습시키려면 실용 예제가 필요한데

좋은 예제는 아래를 보면 될듯..

https://github.com/aymericdamien/TensorFlow-Examples

1개씩 봐야지..

TensorFlow Tutorial with popular machine learning algorithms implementation. This tutorial was designed for easily diving into TensorFlow, through examples.

It is suitable for beginners who want to find clear and concise examples about TensorFlow. For readability, the tutorial includes both notebook and code with explanations.

Note: If you are using older TensorFlow version (before 0.12), please have a look here

- Nearest Neighbor (notebook) (code)
- Linear Regression (notebook) (code)
- Logistic Regression (notebook) (code)

- Multilayer Perceptron (notebook) (code)
- Convolutional Neural Network (notebook) (code)
- Recurrent Neural Network (LSTM) (notebook) (code)
- Bidirectional Recurrent Neural Network (LSTM) (notebook) (code)
- Dynamic Recurrent Neural Network (LSTM) (code)
- AutoEncoder (notebook) (code)

- Save and Restore a model (notebook) (code)
- Tensorboard – Graph and loss visualization (notebook) (code)
- Tensorboard – Advanced visualization (code)

Some examples require MNIST dataset for training and testing. Don’t worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

Official Website: http://yann.lecun.com/exdb/mnist/

The following examples are coming from TFLearn, a library that provides a simplified interface for TensorFlow. You can have a look, there are many examples and pre-built operations and layers.

- TFLearn Quickstart. Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier.

- Linear Regression. Implement a linear regression using TFLearn.
- Logical Operators. Implement logical operators with TFLearn (also includes a usage of ‘merge’).
- Weights Persistence. Save and Restore a model.
- Fine-Tuning. Fine-Tune a pre-trained model on a new task.
- Using HDF5. Use HDF5 to handle large datasets.
- Using DASK. Use DASK to handle large datasets.

- Multi-layer perceptron. A multi-layer perceptron implementation for MNIST classification task.
- Convolutional Network (MNIST). A Convolutional neural network implementation for classifying MNIST dataset.
- Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset.
- Network in Network. ‘Network in Network’ implementation for classifying CIFAR-10 dataset.
- Alexnet. Apply Alexnet to Oxford Flowers 17 classification task.
- VGGNet. Apply VGG Network to Oxford Flowers 17 classification task.
- VGGNet Finetuning (Fast Training). Use a pre-trained VGG Network and retrain it on your own data, for fast training.
- RNN Pixels. Use RNN (over sequence of pixels) to classify images.
- Highway Network. Highway Network implementation for classifying MNIST dataset.
- Highway Convolutional Network. Highway Convolutional Network implementation for classifying MNIST dataset.
- Residual Network (MNIST). A bottleneck residual network applied to MNIST classification task.
- Residual Network (CIFAR-10). A residual network applied to CIFAR-10 classification task.
- Google Inception (v3). Google’s Inception v3 network applied to Oxford Flowers 17 classification task.
- Auto Encoder. An auto encoder applied to MNIST handwritten digits.

- Recurrent Neural Network (LSTM). Apply an LSTM to IMDB sentiment dataset classification task.
- Bi-Directional RNN (LSTM). Apply a bi-directional LSTM to IMDB sentiment dataset classification task.
- Dynamic RNN (LSTM). Apply a dynamic LSTM to classify variable length text from IMDB dataset.
- City Name Generation. Generates new US-cities name, using LSTM network.
- Shakespeare Scripts Generation. Generates new Shakespeare scripts, using LSTM network.
- Seq2seq. Pedagogical example of seq2seq reccurent network. See this repo for full instructions.
- CNN Seq. Apply a 1-D convolutional network to classify sequence of words from IMDB sentiment dataset.

- Atari Pacman 1-step Q-Learning. Teach a machine to play Atari games (Pacman by default) using 1-step Q-learning.

- Recommender – Wide & Deep Network. Pedagogical example of wide & deep networks for recommender systems.

- Spiral Classification Problem. TFLearn implementation of spiral classification problem from Stanford CS231n.

- Layers. Use TFLearn layers along with TensorFlow.
- Trainer. Use TFLearn trainer class to train any TensorFlow graph.
- Built-in Ops. Use TFLearn built-in operations along with TensorFlow.
- Summaries. Use TFLearn summarizers along with TensorFlow.
- Variables. Use TFLearn variables along with TensorFlow.

```
tensorflow 1.0alpha
numpy
matplotlib
cuda
tflearn (if using tflearn examples)
```

For more details about TensorFlow installation, you can check TensorFlow Installation Guide

**TensorFlow는 Apache 2.0 기반의 오픈소스 프레임워크 이다.**

현재 진행상황으로는, tensorflow를 사용하기 위한 환경 조성은 OK인데…

이것을 어떻게 적용할까?? 라는 의문점은 사라지지 않는다.

(왜냐니, How to learn ML using tensorflow.)

그래서 좀 찾아봤더니 이런 것이 있더라.

**1. 텐서 플로우 설치도 했고, 튜토리얼도 봤고, 기초 예제도 짜봤다면..**

**2. 텐서 플로우의background, Tutorial – Logistic regression, CNN, RNN**

** (아 이건 Neural 신경망쪽 인데… 이건 좀 study를 심도있게 고려를 해봐야 겠다.)**

**3. 텐서 플로우의 예제들, 그리고 파이썬 함수를 찾는데 참조한 것들.**

https://github.com/nlintz/TensorFlow-Tutorials/

https://docs.scipy.org/doc/numpy/reference/

텐서플로우 Study를 위한 pdf정보, pptx (다 원서이다..)

**– 원서PDF**

http://www.jorditorres.org/wp-content/uploads/2016/02/LibroTF.english.PART0_RevByRTOUS.pdf

**– 원서PDF를 PPTX로**

https://github.com/jorditorresBCN/FirstContactWithTensorFlow

https://github.com/jorditorresBCN

http://jorditorres.org/?s=FirstContactWithTensorFlow

(한국어로 번역한 자료가 있긴한데, 원서 읽고 나중에 참조해봐야겠다..)

**설치 튜토리얼은 OK, 그런데 머신러닝은 HOW?**

우선 아래꺼 부터 봐야겠다.. 영어로 되어있는데, PPT내용이 훌륭해..

http://www.jorditorres.org/wp-content/uploads/2016/02/FirstContactWithTensorFlow.part1_.pdf