Google I/O is a wrap! Catch up on TensorFlow sessions

## TensorFlow Probability is a library for probabilistic reasoning and statistical analysis.

```import tensorflow as tf
import tensorflow_probability as tfp

# Pretend to load synthetic data set.
features = tfp.distributions.Normal(loc=0., scale=1.).sample(int(100e3))
labels = tfp.distributions.Bernoulli(logits=1.618 * features).sample()

# Specify model.
model = tfp.glm.Bernoulli()

# Fit model given data.
coeffs, linear_response, is_converged, num_iter = tfp.glm.fit(
model_matrix=features[:, tf.newaxis],
response=tf.cast(labels, dtype=tf.float32),
model=model)
# ==> coeffs is approximately [1.618] (We're golden!)
```
TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. TFP includes:
• A wide selection of probability distributions and bijectors.
• Tools to build deep probabilistic models, including probabilistic layers and a `JointDistribution` abstraction.
• Variational inference and Markov chain Monte Carlo.
• Optimizers such as Nelder-Mead, BFGS, and SGLD.
Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model using a single language throughout the lifecycle of model exploration and production. TFP is open source and available on GitHub. To get started, see the TensorFlow Probability Guide.
Bayesian Methods for Hackers, an introductory, hands-on tutorial, is now available with examples in TensorFlow Probability.
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