tensorflow-101

TensorFlow Tutorials

1. Basics of TensorFlow / 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