R Introduction to Deep Learning: Parts 1-2

March 29, 2022, 10:00am to March 31, 2022, 1:00pm

Trying to register, but not affiliated with the UCB campus? If you are from Berkeley Lab (LBL), UCSF, or CZ Biohub, please register via our partner portals here.

If you are from the UCB campus there's no more waitlist! But after registering above, please do fill out the affiliations form if you have not done so at least once before: https://dlab.berkeley.edu/affiliations

Location: Remote via Zoom. Link will be sent on the morning of the event.

Recordings: This D-Lab workshop will be recorded and made available to UC Berkeley participants for a limited time. Your registration for the event indicates your consent to having any images, comments and chat messages included as part of the video recording materials that are made available.

Date & Time: This workshop is a 2-part series that runs from 10am-1pm.

  • Tuesday, March 29
  • Thursday, March 31

Start Time: D-Lab workshops start 10 minutes after the scheduled start time (“Berkeley Time”). We will admit all participants from the waiting room at that time.


This workshop introduces the basic concepts of Deep Learning — the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels. 

You will use the R interface to Keras to become familiar with basic concepts like input and output layers, batch sizes and output dimensions, dropout rates, weight parametrization and bias, backpropagation, and loss, activation, and optimization functions. You will also gain confidence exploring more complex approaches that utilize pretrained and fine-tuned models.

Prior knowledge requirements: D-Lab's Intro to Machine Learning in R workshop series or equivalent introductory machine learning knowledge.

Prerequisites: D-Lab's R Introduction to Machine Learning with tidymodels: Parts 1-2 series or equivalent introductory machine learning knowledge.

Workshop Materials:https://github.com/dlab-berkeley/Deep-Learning-in-R

Feedback: After completing the workshop, please provide us feedback using this form

Questions? Email: dlab-frontdesk@berkeley.edu