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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 ofTensorFlow /MNIST /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
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

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

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