# Tensorflow-101

**Repository Path**: rigarch/Tensorflow-101

## Basic Information

- **Project Name**: Tensorflow-101
- **Description**: TensorFlow Tutorials
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-18
- **Last Updated**: 2022-01-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Tensorflow Tutorials using Jupyter Notebook 

TensorFlow tutorials written in Python (of course) with Jupyter Notebook. Tried to explain as kindly as possible, as these tutorials are intended for TensorFlow beginners. Hope these tutorials to be a useful recipe book for your deep learning projects. Enjoy coding! :) 

## Contents
1. Basics of [TensorFlow](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/basic_tensorflow.ipynb) / [MNIST](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/basic_mnist.ipynb) / [Numpy](https://github.com/sjchoi86/Tensorflow-101/blob/master/notebooks/basic_python.ipynb)  / [Image Processing](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/basic_imgprocess.ipynb) / [Generating Custom Dataset](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/basic_gendataset.ipynb)
2. Machine Learing Basics with TensorFlow: [Linear Regression](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/linear_regression.ipynb) / [Logistic Regression with MNIST](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/logistic_regression_mnist.ipynb) / [Logistic Regression with Custom Dataset](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/logistic_regression_customdata.ipynb)
3. Multi-Layer Perceptron (MLP): [Simple MNIST](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/mlp_mnist_simple.ipynb) / [Deeper MNIST](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/mlp_mnist_deeper.ipynb) / [Xavier Init MNIST](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/mlp_mnist_xavier.ipynb) / [Custom Dataset](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/mlp_customdata_basic.ipynb)
4. Convolutional Neural Network (CNN): [Simple MNIST](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/cnn_mnist_simple.ipynb) / [Deeper MNIST](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/cnn_mnist_basic.ipynb) / [Simple Custom Dataset](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/cnn_customdata_simple.ipynb) / [Basic Custom Dataset](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/cnn_customdata_basic.ipynb)
5. Using Pre-trained Model (VGG): [Simple Usage](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/use_vgg.ipynb) / [CNN Fine-tuning on Custom Dataset](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/cnn_customdata_vgg_finetune.ipynb)
6. Recurrent Neural Network (RNN): [Simple MNIST](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/rnn_mnist_simple.ipynb) / [Char-RNN Train](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/char_rnn_train_tutorial.ipynb) / [Char-RNN Sample](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/char_rnn_sample_tutorial.ipynb) / [Hangul-RNN Train](https://github.com/sjchoi86/Tensorflow-101/blob/master/notebooks/char_rnn_train_hangul.ipynb) / [Hangul-RNN Sample](https://github.com/sjchoi86/Tensorflow-101/blob/master/notebooks/char_rnn_sample_hangul.ipynb)
7. Word Embedding (Word2Vec): [Simple Version](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/word2vec_simple.ipynb) / [Complex Version](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/word2vec_basic.ipynb)
8. Auto-Encoder Model: [Simple Auto-Encoder](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/dae_mnist.ipynb) / [Denoising Auto-Encoder](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/dae_mnist_dropout.ipynb) / [Convolutional Auto-Encoder (deconvolution)](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/cae_mnist.ipynb)
9. Class Activation Map (CAM): [Global Average Pooling on MNIST](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/gap_mnist.ipynb)
10. TensorBoard Usage: [Linear Regression](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/vis_linear_regression.ipynb) / [MLP](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/vis_mlp_mnist.ipynb) / [CNN](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/vis_cnn_mnist.ipynb) 
11. [Semantic segmentation](https://github.com/sjchoi86/Tensorflow-101/blob/master/notebooks/semseg_basic.ipynb)
12. [Super resolution (in progress)](https://github.com/sjchoi86/Tensorflow-101/blob/master/notebooks/superres_cnn.ipynb)
13. [Web crawler](https://github.com/sjchoi86/Tensorflow-101/blob/master/notebooks/webcrawling_naverstock.ipynb)
14. [Gaussian process regression](https://github.com/sjchoi86/Tensorflow-101/blob/master/notebooks/gp_demo.ipynb)
15. [Neural Style](https://github.com/sjchoi86/Tensorflow-101/blob/master/notebooks/neural_style.ipynb)
16. [Face detection with OpenCV](https://github.com/sjchoi86/Tensorflow-101/blob/master/notebooks/basic_opencv2.ipynb)

## Requirements
- TensorFlow
- Numpy
- SciPy
- Pillow
- BeautifulSoup
- [Pretrained VGG](http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat): inside 'data/' folder

## Note
Most of the codes are simple refactorings of [Aymeric Damien's Tutorial](https://github.com/aymericdamien/TensorFlow-Examples/) or [Nathan Lintz's Tutorial](https://github.com/nlintz/TensorFlow-Tutorials). 
There could be missing credits. Please let me know. 

#### Collected and Modifyed by [Sungjoon](http://enginius.tistory.com) 
[info](http://cpslab.snu.ac.kr/people/sungjoon-choi)