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- Data Visualization WorkshopToggle Dropdown
- Introduction to Tableau (Summer 2021)
- Data Visualization using Python (Matplotlib and Seaborn)
- Data Visualization Using Python - Interactive Plots (Bokeh)
- Data Visualization using Microsoft PowerPoint and Excel
- Data Visualization with R Part 1: Intro to R
- Data Visualization with R Part 2: Tidyverse/Tidy Data and dplyr
- Data Visualization with R Part 3 - Web Scraping with OpenRefine API
- Data Visualization with R Part 4: ggplot2
- Data Visualization with R Part 5 - Sentiment Analysis
- Data Visualization using Tableau (Summer 2020)

- Machine Learning WorkshopToggle Dropdown
- Introduction to Python
- Machine Learning Overview (using Python)
- Preparing your data for Machine Learning
- Machine Learning using Matlab
- Supervised Learning 1 - Linear Classifiers
- Supervised Learning 2 - Tree Based Models
- Application 1 - Sentiment Analysis
- Application 2 - Dimensionality Reduction
- Application 3 - Time Series Data
- Unsupervised Learning - Clustering Analysis
- Model Validation and Selection
- Fairness and Bias in Machine Learning
- Explainable AI - An Overview
- Introduction to Reinforcement Learning

- Machine Learning and Deep Learning Workshop - 2021Toggle Dropdown
- Introduction to Neural Networks
- Intro to Automated Machine Learning: Hyper-Parameter Tuning
- Introduction to NLP part1 - text processing
- Hyper-Parameter Tuning: Bayesian Optimization
- Introduction to NLP Part 2 - Neural Networks
- Introduction to Julia
- Introduction to Computer Vision with Neural Networks
- Intro to Python visualization tools: Seaborn and ipywidgets.
- Data Scraping and Analysis with Python
- Intro to Reinforcement Learning on an optimization perspective.

- Machine Learning and Deep Learning Workshop - 2022
- Data Scraping and Analysis with Python
- Introduction to Neural Networks
- Introduction to Computer Vision with Neural Networks
- Intro to Hyperparameter Optimization: Black-Box Optimization Approaches
- Introduction to Generative adversarial networks (GANs)
- Introduction to Recommender Systems
- Intro to Parallel Computing
- Introduction to Python in Data Science
- Intro to Supervised and Unsupervised Machine Learning Algorithms
- Data Scraping and Analysis with Python
- Intro to Java and Algorithms Part 1
- Intro to Java and Algorithms Part 2
- Introduction to Nueral Network
- Introduction to Web API and Database
- Intro to RNN and LSTM
- Introduction to Transformers in Image Processing
- Intro to Hyperparameter Optimization: Bayesian Optimization

- Machine Learning and Deep Learning Workshop - 2023Toggle Dropdown
- Introduction to Python
- Data Scraping and Analysis with Python
- Introduction to Hadoop and Mapreduce
- Introduction to Container and Kubernetes
- Introduction to Federated Learning
- Introduction to Python -2023 fall
- Introduction to Machine Learning
- Data visualization using Python
- Introduction to PyTorch 1
- Introduction to PyTorch 2
- Introduction to Transformer Neural Network
- Recommender Systems
- Time series and forecasting
- Clustering techniques in Machine learning
- Introduction to Deep Learning

This workshop will cover the fundamental topics in neural networks. We will learn about perception, calculation of loss, training and gradient descent, and backpropagation. We will study the benefits of neural networks by comparing it with previous methods on a sample problem. This will build the foundation required to understand deep neural networks and its applications in Natural Language Processing and Computer Vision.

- PresentationPresentation from the workshop

- Last Edited: Nov 27, 2023 11:16 AM
- URL: https://guides.lib.purdue.edu/d-velop
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