CompBio Skills Seminar

Getting started with Tensorflow 2.0

Tensorflow is Google's public deep learning library, first released in 2015. Many would say that it's competed with PyTorch (released in 2016) for popularity, with PyTorch being the preferred library for most deep learning researchers over the past few years. However, just recently (2019), Tensorflow 2.0 was released which offered a much more user-friendly API and deep learning environment than its predecessor. Specifically, it interfaces with Keras now and offers several plug-and-play estimators as well as the ability to develop your own fancy deep learning architectures. While PyTorch still offers a bit more for the deep learning community like more fine-grained control over model architecture and cool autodifferentiation capabilities, TensorFlow 2.0 is probably more widely used in development and offers a ton of support like APIs from Javascript or Swift and tremendous amount of developer tools.

In this tutorial, I'll cover the following items:

  1. How to identify and frame a machine learning problem

  2. Running your first linear regression with Tensorflow 2.0

  3. Classification with a Neural Network

  4. Saving, loading, and checkpointing models

  5. Creating Tensorboard reports

You can navigate to this Colab for the full tutorial.

Additional Resources