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

Overview

logo

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.

Playlist

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.

Owner
AWS Samples
AWS Samples
This repository contains the code for the binaural-detection model used in the publication arXiv:2111.04637

This repository contains the code for the binaural-detection model used in the publication arXiv:2111.04637 Dependencies The model depends on the foll

Jörg Encke 2 Oct 14, 2022
The official codes of our CVPR2022 paper: A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift

TwoStageAlign The official codes of our CVPR2022 paper: A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift Pa

Shi Guo 32 Dec 15, 2022
Self-Supervised Image Denoising via Iterative Data Refinement

Self-Supervised Image Denoising via Iterative Data Refinement Yi Zhang1, Dasong Li1, Ka Lung Law2, Xiaogang Wang1, Hongwei Qin2, Hongsheng Li1 1CUHK-S

Zhang Yi 72 Jan 01, 2023
NeuTex: Neural Texture Mapping for Volumetric Neural Rendering

NeuTex: Neural Texture Mapping for Volumetric Neural Rendering Paper: https://arxiv.org/abs/2103.00762 Running Run on the provided DTU scene cd run ba

Fanbo Xiang 67 Dec 28, 2022
CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation

CDGAN CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation CDGAN Implementation in PyTorch This is the imple

Kancharagunta Kishan Babu 6 Apr 19, 2022
A light weight data augmentation tool for training CNNs and Viola Jones detectors

hey-daug A light weight data augmentation tool for training CNNs and Viola Jones detectors (Haar Cascades). This tool inflates your data by up to six

Jaiyam Sharma 2 Nov 23, 2019
OverFeat is a Convolutional Network-based image classifier and feature extractor.

OverFeat OverFeat is a Convolutional Network-based image classifier and feature extractor. OverFeat was trained on the ImageNet dataset and participat

593 Dec 08, 2022
Progressive Growing of GANs for Improved Quality, Stability, and Variation

Progressive Growing of GANs for Improved Quality, Stability, and Variation — Official TensorFlow implementation of the ICLR 2018 paper Tero Karras (NV

Tero Karras 5.9k Jan 05, 2023
subpixel: A subpixel convnet for super resolution with Tensorflow

subpixel: A subpixel convolutional neural network implementation with Tensorflow Left: input images / Right: output images with 4x super-resolution af

Atrium LTS 2.1k Dec 23, 2022
Implementation detail for paper "Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet"

Multi-level-colonoscopy-malignant-tissue-detection-with-adversarial-CAC-UNet Implementation detail for our paper "Multi-level colonoscopy malignant ti

CVSM Group - email: <a href=[email protected]"> 84 Nov 22, 2022
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance

Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.

Ubiquitous Knowledge Processing Lab 22 Jan 02, 2023
Implementation of "Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency"

Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency (ICCV2021) Paper Link: https://arxiv.org/abs/2107.11355 This implementation bui

32 Nov 17, 2022
xitorch: differentiable scientific computing library

xitorch is a PyTorch-based library of differentiable functions and functionals that can be widely used in scientific computing applications as well as deep learning.

24 Apr 15, 2021
Pose estimation for iOS and android using TensorFlow 2.0

💃 Mobile 2D Single Person (Or Your Own Object) Pose Estimation for TensorFlow 2.0 This repository is forked from edvardHua/PoseEstimationForMobile wh

tucan9389 165 Nov 16, 2022
CONditionals for Ordinal Regression and classification in PyTorch

CONDOR pytorch implementation for ordinal regression with deep neural networks. Documentation: https://GarrettJenkinson.github.io/condor_pytorch About

7 Jul 25, 2022
9th place solution in "Santa 2020 - The Candy Cane Contest"

Santa 2020 - The Candy Cane Contest My solution in this Kaggle competition "Santa 2020 - The Candy Cane Contest", 9th place. Basic Strategy In this co

toshi_k 22 Nov 26, 2021
Pytorch library for fast transformer implementations

Transformers are very successful models that achieve state of the art performance in many natural language tasks

Idiap Research Institute 1.3k Dec 30, 2022
Neural Turing Machines (NTM) - PyTorch Implementation

PyTorch Neural Turing Machine (NTM) PyTorch implementation of Neural Turing Machines (NTM). An NTM is a memory augumented neural network (attached to

Guy Zana 519 Dec 21, 2022
One-line your code easily but still with the fun of doing so!

One-liner-iser One-line your code easily but still with the fun of doing so! Have YOU ever wanted to write one-line Python code, but don't have the sa

5 May 04, 2022
Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022)

Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022) Please cite "Independent SE(3)-Equivar

Octavian Ganea 154 Jan 02, 2023