R Introduction to Deep Learning: Parts 1-2

R Introduction to Deep Learning is a 2-part series that runs from 10am-1pm each day:

• Tuesday, March 29
• Thursday, March 31

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

Please register on the UCSF Registration for D-Lab Workshop portal here.