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## An Introduction to Julia

This workshop aims to introduce both users of scripting languages and advanced programmers to the Julia ecosystem and explore details about the Julia language which can help produce efficient and readable code. The goal of the workshop is for students to understand where Julia can be applied and be well-equipped to start using Julia in their own research. Students will learn about the current state of Julia development (IDEs, documentation, where to get help), how to write efficient code by…

Find out more »## Neural Networks and Learning with Python

This course provides an introduction to NNs and learning with Python. In this workshop, you will learn about Tensorflow programming fundamentals and basic ideas of neural networks through building and training different MNIST models. Moreover, you will be introduced to more advanced applications of deep learning in computer vision and natural language processing with Keras high-level API. Date: May 31, 2018 Time: 9 a.m. to 5 p.m. with lunch provided Location: Donald Bren Hall, Room 4011 Instructor: Lingge Li, Julian Collado…

Find out more »## 10/19/18 Intro to R and Data Visualization in R with ggplot

Intro to R: In this session, students will be familiarized with R: data types, functions and basic data manipulation including some exploratory data analysis and how to perform statistical tests. Data Visualization in R with ggplot: In this part of the workshop, students will learn the basic commands to create statistical plots, understand the grammar of graphics behind ggplot, and master how to create more sophisticated data visualizations through hands-on exercises on real data sets.

Find out more »## 10/26/18 Intro to Linux on the HPC

This course covers how to best exploit the bash shell for both interactive work and batch jobs, moving & simple manipulation of data, as well very short introductions to programming in bash, Perl, and R. This is not computer science; this is a driver’s license. Date: Friday, October 26, 2018 Time: 9am-5pm (coffee and lunch will be provided) Location: Donald Bren Hall, Room 4011 Cost: Register online before 10/26/18: $10 Walk-ins: $15 (cash or check payable to "UC Regents" only)

Find out more »## 11/02/18 Topics in R

This course builds on our Introduction to R by teaching advanced visualization and data tidying. In this course, you will cover advanced visualization including ggplot and R Shiny. In addition to visualizations, principles of tidy data and reporting will be discussed. Pre-requisites: 1) familiarity with basic statistical concepts, and 2) intermediate R programming knowledge. For the tutorial, bring a laptop with R downloaded and installed and WiFi.

Find out more »## 11/09/18 An Introduction to Julia

This workshop aims to introduce both users of scripting languages and advanced programmers to the Julia ecosystem and explore details about the Julia v1.0 language which can help produce efficient and readable code. The goal of the workshop is for students to understand where Julia can be applied and be well-equipped to start using Julia in their own research. Students will learn about the current state of Julia development (IDEs, documentation, where to get help), how to write efficient code…

Find out more »## 02/15/19 Introduction to Deep Learning

In this workshop, you’ll learn basic ideas of neural networks and Tensorflow programming fundamentals through building and training different models. Moreover, you will be introduced to more advanced applications of deep learning in computer vision and natural language processing with Keras high-level API.

Find out more »## 02/22/19 Intro to Linux

This course covers how to best exploit the bash shell for both interactive work and batch jobs, moving & simple manipulation of data, as well very short introductions to programming in bash, Perl, and R. This is not computer science; this is a driver’s license.

Find out more »## 03/01/19 A Review of Graph Convolutional Neural Networks

The first part of this workshop will be a review of neural networks in tensorflow and keras. The second part will go into an exciting specific type of neural network called graph convolutional neural networks. There are numerous real-world data in non-euclidean relations. Finding an optimum representation of these types of data can be useful to investigate their hidden patterns and structures. 2-d manifolds in a 3-d space and graph-embedded relations are two important examples of data points in a…

Find out more »## 03/08/19 Intro to Deep Generative Models

This workshop aims at introducing commonly used deep neural networks and their application as deep generative models. We will cover motivating ideas and theory behind various deep generative models such as variational auto-encoder (VAE), generative adversarial network (GAN) and flow-based model. You will learn to implement these models to generate realistic looking images in Tensorflow.

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