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! :)
- Basics of TensorFlow / MNIST / Numpy / Image Processing / Generating Custom Dataset
- Machine Learing Basics with TensorFlow: Linear Regression / Logistic Regression with MNIST / Logistic Regression with Custom Dataset
- Multi-Layer Perceptron (MLP): Simple MNIST / Deeper MNIST / Xavier Init MNIST / Custom Dataset
- Convolutional Neural Network (CNN): Simple MNIST / Deeper MNIST / Simple Custom Dataset / Basic Custom Dataset
- Using Pre-trained Model (VGG): Simple Usage / CNN Fine-tuning on Custom Dataset
- Recurrent Neural Network (RNN): Simple MNIST / Char-RNN Train / Char-RNN Sample / Hangul-RNN Train / Hangul-RNN Sample
- Word Embedding (Word2Vec): Simple Version / Complex Version
- Auto-Encoder Model: Simple Auto-Encoder / Denoising Auto-Encoder / Convolutional Auto-Encoder (deconvolution)
- Class Activation Map (CAM): Global Average Pooling on MNIST
- TensorBoard Usage: Linear Regression / MLP / CNN
- Semantic segmentation
- Super resolution (in progress)
- Web crawler
- Gaussian process regression
- Neural Style
- 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.