If you're new to machine learning, we recommend starting here. You'll learn about a classic problem, handwritten digit classification (MNIST), and get a gentle introduction to multiclass classification.
If you're already familiar with other deep learning software packages, and are already familiar with MNIST, this tutorial with give you a very brief primer on TensorFlow.
This is a technical tutorial, where we walk you through the details of using TensorFlow infrastructure to train models at scale. We use again MNIST as the example.
An introduction to TensorFlow Serving, a flexible, high-performance system for serving machine learning models, designed for production environments.
An introduction to convolutional neural networks using the CIFAR-10 data set. Convolutional neural nets are particularly tailored to images, since they exploit translation invariance to yield more compact and effective representations of visual content.
This tutorial motivates why it is useful to learn to represent words as vectors (called word embeddings). It introduces the word2vec model as an efficient method for learning embeddings. It also covers the high-level details behind noise-contrastive training methods (the biggest recent advance in training embeddings).
An introduction to RNNs, wherein we train an LSTM network to predict the next word in an English sentence. (A task sometimes called language modeling.)
A follow on to the RNN tutorial, where we assemble a sequence-to-sequence model for machine translation. You will learn to build your own English-to-French translator, entirely machine learned, end-to-end.
TensorFlow can be used for computation that has nothing to do with machine learning. Here's a naive implementation of Mandelbrot set visualization.
As another example of non-machine learning computation, we offer an example of a naive PDE simulation of raindrops landing on a pond.
Details about downloading the MNIST handwritten digits data set. Exciting stuff.
How to run object recognition using a convolutional neural network trained on ImageNet Challenge data and label set.
We will soon be releasing code for training a state-of-the-art Inception model.
Building on the Inception recognition model, we will release a TensorFlow version of the Deep Dream neural network visual hallucination software.
COMING SOON