Code examples for some popular machine learning algorithms, using TensorFlow library. This tutorial is designed to easily dive into TensorFlow, through examples. It includes both notebook and code with explanations.
- Nearest Neighbor (notebook) (code)
- Linear Regression (notebook) (code)
- Logistic Regression (notebook) (code)
- Multilayer Perceptron (notebook) (code)
- Convolutional Neural Network (notebook) (code)
- AlexNet (notebook) (code)
- Reccurent Neural Network (LSTM) (notebook) (code)
- Bidirectional Reccurent Neural Network (code)
tensorflow
numpy
matplotlib
cuda (to run examples on GPU)
For more details about TensorFlow installation, you can check Setup_TensorFlow.md
Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, with 60,000 examples for training and 10,000 examples for testing. (Website: http://yann.lecun.com/exdb/mnist/)
Other tutorials are coming soon....