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 / MNIST / Numpy / Image Processing / Generating Custom Dataset
2. Machine Learing Basics with TensorFlow: Linear Regression / Logistic Regression with MNIST / Logistic Regression with Custom Dataset
3. Multi-Layer Perceptron (MLP): Simple MNIST / Deeper MNIST / Xavier Init MNIST / Custom Dataset
4. Convolutional Neural Network (CNN): Simple MNIST / Deeper MNIST / Simple Custom Dataset / Basic Custom Dataset
5. Using Pre-trained Model (VGG): Simple Usage / CNN Fine-tuning on Custom Dataset
6. Recurrent Neural Network (RNN): Simple MNIST / Char-RNN Train / Char-RNN Sample / Hangul-RNN Train / Hangul-RNN Sample
7. Word Embedding (Word2Vec): Simple Version / Complex Version
8. Auto-Encoder Model: Simple Auto-Encoder / Denoising Auto-Encoder / Convolutional Auto-Encoder (deconvolution)
9. Class Activation Map (CAM): Global Average Pooling on MNIST
10. TensorBoard Usage: Linear Regression / MLP / CNN
11. Semantic segmentation
12. Super resolution (in progress)
13. Web crawler
14. Gaussian process regression
15. Neural Style
16. Face detection with OpenCV

Requirements

• TensorFlow
• Numpy
• SciPy
• Pillow
• BeautifulSoup
• Pretrained VGG: inside 'data/' folder

Note

Most of the codes are simple refactorings of Aymeric Damien's Tutorial or Nathan Lintz's Tutorial. There could be missing credits. Please let me know.

Collected and Modifyed by Sungjoon

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