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! :)

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

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

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.

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