Course materials for Fall 2021 "CIS6930 Topics in Computing for Data Science" at New College of Florida

Overview

Fall 2021 CIS6930 Topics in Computing for Data Science

This repository hosts course materials used for a 13-week course "CIS6930 Topics in Computing for Data Science" as part of Graduate Program in Applied Data Science at New College of Florida.

This course requires the student to have basic knowledge in Machine Learning and programming skills in Python. For those who are new to Machine Learning and/or Python programming, refer to this repository, which hosts the course materials for a 3-day seminar on Machine Learning and NLP.

Course Description

This course covers a line of Deep Learning techniques that have been applied to a variety of computer science problems, especially in Computer Vision and Natural Language Processing. The course will start from Deep Learning fundamentals such as basic model architecture and optimization techniques before moving onto more sophisticated techniques. This course covers commonly used techniques for Computer Vision such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), and for Natural Language Processing such as Recurrent Neural Networks (RNNs) and Transformer. The aim of this course is to teach both theory and practice while the students are proactively learning to apply the techniques to problems through running their own research projects.

The course also provides self-contained hands-on exercise materials that run on Google Colab, which offer quick introduction to popular "must-know" Python libraries such as Pandas, PyTorch, spaCy, Transformers, and PyTorch Lightning.

Course Schedule & Materials

Term projects

Here is a list of students' term projects that are publicly available at GitHub. They are all great work!

Owner
Yoshi Suhara
Yoshi Suhara
Newt - a Gaussian process library in JAX.

Newt __ \/_ (' \`\ _\, \ \\/ /`\/\ \\ \ \\

AaltoML 0 Nov 02, 2021
MEDS: Enhancing Memory Error Detection for Large-Scale Applications

MEDS: Enhancing Memory Error Detection for Large-Scale Applications Prerequisites cmake and clang Build MEDS supporting compiler $ make Build Using Do

Secomp Lab at Purdue University 34 Dec 14, 2022
[ICCV 2021] Relaxed Transformer Decoders for Direct Action Proposal Generation

RTD-Net (ICCV 2021) This repo holds the codes of paper: "Relaxed Transformer Decoders for Direct Action Proposal Generation", accepted in ICCV 2021. N

Multimedia Computing Group, Nanjing University 80 Nov 30, 2022
PyTorch implementation of SimSiam: Exploring Simple Siamese Representation Learning

SimSiam: Exploring Simple Siamese Representation Learning This is a PyTorch implementation of the SimSiam paper: @Article{chen2020simsiam, author =

Facebook Research 834 Dec 30, 2022
Teaching end to end workflow of deep learning

Deep-Education This repository is now available for public use for teaching end to end workflow of deep learning. This implies that learners/researche

Data Lab at College of William and Mary 2 Sep 26, 2022
On Effective Scheduling of Model-based Reinforcement Learning

On Effective Scheduling of Model-based Reinforcement Learning Code to reproduce the experiments in On Effective Scheduling of Model-based Reinforcemen

laihang 8 Oct 07, 2022
Google-drive-to-sqlite - Create a SQLite database containing metadata from Google Drive

google-drive-to-sqlite Create a SQLite database containing metadata from Google

Simon Willison 140 Dec 04, 2022
Code for "Adversarial Attack Generation Empowered by Min-Max Optimization", NeurIPS 2021

Min-Max Adversarial Attacks [Paper] [arXiv] [Video] [Slide] Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang, Tianyun Zha

Jingkang Wang 12 Nov 23, 2022
MohammadReza Sharifi 27 Dec 13, 2022
A Deep Reinforcement Learning Framework for Stock Market Trading

DQN-Trading This is a framework based on deep reinforcement learning for stock market trading. This project is the implementation code for the two pap

61 Jan 01, 2023
KoRean based ELECTRA pre-trained models (KR-ELECTRA) for Tensorflow and PyTorch

KoRean based ELECTRA (KR-ELECTRA) This is a release of a Korean-specific ELECTRA model with comparable or better performances developed by the Computa

12 Jun 03, 2022
Differentiable Annealed Importance Sampling (DAIS)

Differentiable Annealed Importance Sampling (DAIS) This repository contains the code to reproduce the DAIS results from the paper Differentiable Annea

Guodong Zhang 6 Dec 26, 2021
i3DMM: Deep Implicit 3D Morphable Model of Human Heads

i3DMM: Deep Implicit 3D Morphable Model of Human Heads CVPR 2021 (Oral) Arxiv | Poject Page This project is the official implementation our work, i3DM

Tarun Yenamandra 60 Jan 03, 2023
This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?” Usage To replicate our results in Secti

Albert Webson 64 Dec 11, 2022
Implementation of the ICCV'21 paper Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases

Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases [Papers 1, 2][Project page] [Video] The implementation of the papers Temporal

56 Nov 21, 2022
Deep Q-Learning Network in pytorch (not actively maintained)

pytoch-dqn This project is pytorch implementation of Human-level control through deep reinforcement learning and I also plan to implement the followin

Hung-Tu Chen 342 Jan 01, 2023
Anomaly Detection Based on Hierarchical Clustering of Mobile Robot Data

We proposed a new approach to detect anomalies of mobile robot data. We investigate each data seperately with two clustering method hierarchical and k-means. There are two sub-method that we used for

Zekeriyya Demirci 1 Jan 09, 2022
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

Tensorpack is a neural network training interface based on TensorFlow. Features: It's Yet Another TF high-level API, with speed, and flexibility built

Tensorpack 6.2k Jan 09, 2023
When are Iterative GPs Numerically Accurate?

When are Iterative GPs Numerically Accurate? This is a code repository for the paper "When are Iterative GPs Numerically Accurate?" by Wesley Maddox,

Wesley Maddox 1 Jan 06, 2022
Build tensorflow keras model pipelines in a single line of code. Created by Ram Seshadri. Collaborators welcome. Permission granted upon request.

deep_autoviml Build keras pipelines and models in a single line of code! Table of Contents Motivation How it works Technology Install Usage API Image

AutoViz and Auto_ViML 102 Dec 17, 2022