It is suitable for beginners who want to find clear and concise examples about TensorFlow. For readability, the tutorial includes both notebook and code with explanations.
1 – Introduction
2 – Basic Models
3 – Neural Networks
4 – Utilities
- Save and Restore a model (notebook) (code)
- Tensorboard – Graph and loss visualization (notebook) (code)
- Tensorboard – Advanced visualization (code)
5 – Multi GPU
- 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.
Natural Language Processing
tensorflow numpy matplotlib cuda tflearn (if using tflearn examples)
For more details about TensorFlow installation, you can check TensorFlow Installation Guide
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/)
Original URL: http://feedproxy.google.com/~r/feedsapi/BwPx/~3/Kx6IaOVIpG0/TensorFlow-Examples