- Linear Regression. Implement a linear regression using TFLearn.
- Logical Operators. Implement logical operators with TFLearn (also includes a usage of 'merge').
- Weights Persistence. Save and Restore a model.
- Fine-Tuning. Fine-Tune a pre-trained model on a new task.
- Using HDF5. Use HDF5 to handle large datasets.
- Using DASK. Use DASK to handle large datasets.
- Layers. Use TFLearn layers along with Tensorflow.
- Trainer. Use TFLearn trainer class to train any Tensorflow graph.
- Built-in Ops. Use TFLearn built-in operations along with Tensorflow.
- Summaries. Use TFLearn summarizers along with Tensorflow.
- Variables. Use TFLearn variables along with Tensorflow.
- Multi-layer perceptron. A multi-layer perceptron implementation for MNIST classification task.
- Convolutional Network (MNIST). A Convolutional neural network implementation for classifying MNIST dataset.
- Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset.
- Network in Network. 'Network in Network' implementation for classifying CIFAR-10 dataset.
- Alexnet. Apply Alexnet to Oxford Flowers 17 classification task.
- VGGNet. Apply VGG Network to Oxford Flowers 17 classification task.
- RNN Pixels. Use RNN (over sequence of pixels) to classify images.
- Residual Network (MNIST). A residual network with shallow bottlenecks applied to MNIST classification task.
- Residual Network (CIFAR-10). A residual network with deep bottlenecks applied to CIFAR-10 classification task.
- Auto Encoder. An auto encoder applied to MNIST handwritten digits.
- Reccurent Network (LSTM). Apply an LSTM to IMDB sentiment dataset classification task.
- Bi-Directional LSTM. Apply a bi-directional LSTM to IMDB sentiment dataset classification task.
- City Name Generation. Generates new US-cities name, using LSTM network.
- Shakespeare Scripts Generation. Generates new Shakespeare scripts, using LSTM network.
- Spiral Classification Problem. TFLearn implementation of spiral classification problem from Stanford CS231n.