Paige Bailey is a senior Cloud Developer Advocate at Microsoft specializing in machine learning and artificial intelligence. Prior to joining Microsoft, Paige was a data scientist and machine learning engineer in the energy industry (drilling and completions optimization, subsurface characterization).
Paige has over a decade of experience doing data analysis with Python, and 5 years of experience with R and distributed data processing using Apache Spark. She is on the core committee for JupyterCon and SciPy; is a Python instructor for EdX; and is currently writing an introductory children’s book on machine learning, as well as a technical cookbook for machine learning at scale.
You can connect with Paige on Twitter and LinkedIn here:
A beginner-friendly introduction to deep learning using TensorFlow and Keras!
We’ll be working through several of the newly-released tf.keras exercises on a wide variety of use cases: computer vision, natural language processing, text generation, and structured data classification. We will review the latest APIs (including eager execution), discuss best practices for operationalizing your models, and point you to several resources where you can learn more about machine learning (in general) and TensorFlow and Keras (specifically).
This workshop will be complementary to the afternoon deep dive on TensorBoard.
Prerequisities: your own laptop, and some familiarity with both Python and linear algebra.
Feel free to reach out to @DynamicWebPaige on Twitter if you have any questions, and look forward to seeing you there!
Exploring ways to visualize and explain deep learning models with TensorBoard. This workshop is suitable for people who have taken Paige's Introduction to Tensorflow in the morning, or people that have some experience with Keras, TensorFlow, PyTorch, or similar.
In this intermediate level workshop, we will be exploring ways to visualize and explain deep learning models with TensorBoard: a suite of web applications for inspecting and understanding TensorFlow runs and graphs.
By the end of this workshop, students should be able to answer the following questions:
- What is TensorBoard?
- Can I use TensorBoard with Keras?
- Can I use TensorBoard with frameworks other than TensorFlow?
- What do I need to include in my model to visualize runs in TensorBoard?
- What dashboards are available out of the box?
- How can I build custom extensions with the API?
The speaker canceled and we'll be hosting a lightning round of lightning talks!
Our planet is facing the greatest challenges it's ever faced: in the next two decades, demand for fresh water is anticipated to outpace supply; climate change threatens human health, infrastructure, and natural systems; farmers must produce more food, on arable land, with less impact; and species are going extinct at alarming rates. Climate change is the problem of our time - and it's everyone's problem.
In this talk, we will discuss how machine learning and data science is uniquely positioned to shine a light on these issues: to effectively communicate research findings to the public and to government officials; to more accurately model environmental systems; and to suggest novel solutions.