Aws-machine-learning-university-accelerated-tab - Machine Learning University: Accelerated Tabular Data Class

Last update: Dec 23, 2022

Overview

Machine Learning University: Accelerated Tabular Data Class

This repository contains slides, notebooks, and datasets for the Machine Learning University (MLU) Accelerated Tabular Data class. Our mission is to make Machine Learning accessible to everyone. We have courses available across many topics of machine learning and believe knowledge of ML can be a key enabler for success. This class is designed to help you get started with tabular data (spreadsheet-like tables), learn about widely used Machine Learning techniques for tabular data, and apply them to real-world problems.

YouTube

Watch all Tabular Data class video recordings in this YouTube playlist from our YouTube channel.

Course Overview

There are three lectures and one final project for this class.

Lecture 1	Lecture 2	Lecture 3
Introduction to ML	Feature Engineering	Optimization
Sample ML Model	Tree-based Models	Regression Models
Model Evaluation	Bagging	Boosting
Exploratory Data Analysis	Hyperparameter Tuning	Neural Networks
K Nearest Neighbors (KNN)	AWS AI/ML Services	AutoML

Final Project: Practice working with a "real-world" tabular dataset for the final project. Final project dataset is in the data/final_project folder. For more details on the final project, check out this notebook.

Contribute

If you would like to contribute to the project, see CONTRIBUTING for more information.

License

The license for this repository depends on the section. Data set for the course is being provided to you by permission of Amazon and is subject to the terms of the Amazon License and Access. You are expressly prohibited from copying, modifying, selling, exporting or using this data set in any way other than for the purpose of completing this course. The lecture slides are released under the CC-BY-SA-4.0 License. The code examples are released under the MIT-0 License. See each section's LICENSE file for details.

Aws-machine-learning-university-accelerated-tab - Machine Learning University: Accelerated Tabular Data Class

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Overview

Machine Learning University: Accelerated Tabular Data Class

YouTube

Course Overview

Contribute

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AWS Samples

Pytorch implementation of the paper: "A Unified Framework for Separating Superimposed Images", in CVPR 2020.

Code for the paper 'A High Performance CRF Model for Clothes Parsing'.

Pytorch library for fast transformer implementations

CVPR2022 (Oral) - Rethinking Semantic Segmentation: A Prototype View

PyTorch Implementation of Vector Quantized Variational AutoEncoders.

Politecnico of Turin Thesis: "Implementation and Evaluation of an Educational Chatbot based on NLP Techniques"

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks."

NasirKhusraw - The TSP solved using genetic algorithm and show TSP path overlaid on a map of the Iran provinces & their capitals.

This repo. is an implementation of ACFFNet, which is accepted for in Image and Vision Computing.

SuperSonic, a new open-source framework to allow compiler developers to integrate RL into compilers easily, regardless of their RL expertise

Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"

Vehicles Counting using YOLOv4 + DeepSORT + Flask + Ngrok

SoGCN: Second-Order Graph Convolutional Networks

Fantasy Points Prediction and Dream Team Formation

Exponential Graph is Provably Efficient for Decentralized Deep Training

Record radiologists' eye gaze when they are labeling images.

Use VITS and Opencpop to develop singing voice synthesis; Maybe it will VISinger.

Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021)

General purpose GPU compute framework for cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends)