georgymh / Ml Fraud Detection
Credit card fraud detection through logistic regression, k-means, and deep learning.
Stars: ✭ 117
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Ml Fraud Detection
Deep Math Machine Learning.ai
A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
Stars: ✭ 173 (+47.86%)
Mutual labels: jupyter-notebook, deep-neural-networks, logistic-regression
Deepfake Detection
DeepFake Detection: Detect the video is fake or not using InceptionResNetV2.
Stars: ✭ 23 (-80.34%)
Mutual labels: kaggle, jupyter-notebook, deep-neural-networks
My Journey In The Data Science World
📢 Ready to learn or review your knowledge!
Stars: ✭ 1,175 (+904.27%)
Mutual labels: kaggle, jupyter-notebook, deep-neural-networks
Pytorchnlpbook
Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://nlproc.info
Stars: ✭ 1,390 (+1088.03%)
Mutual labels: jupyter-notebook, deep-neural-networks
Mxnet Finetuner
An all-in-one Deep Learning toolkit for image classification to fine-tuning pretrained models using MXNet.
Stars: ✭ 100 (-14.53%)
Mutual labels: jupyter-notebook, deep-neural-networks
Dataminingnotesandpractice
记录我学习数据挖掘过程的笔记和见到的奇技，持续更新～
Stars: ✭ 103 (-11.97%)
Mutual labels: kaggle, jupyter-notebook
Deep Learning Python
Intro to Deep Learning, including recurrent, convolution, and feed forward neural networks.
Stars: ✭ 94 (-19.66%)
Mutual labels: jupyter-notebook, deep-neural-networks
Intro To Deep Learning
A collection of materials to help you learn about deep learning
Stars: ✭ 103 (-11.97%)
Mutual labels: jupyter-notebook, deep-neural-networks
Dog Breeds Classification
Set of scripts and data for reproducing dog breed classification model training, analysis, and inference.
Stars: ✭ 105 (-10.26%)
Mutual labels: kaggle, jupyter-notebook
Kaggle Houseprices
Kaggle Kernel for House Prices competition https://www.kaggle.com/massquantity/all-you-need-is-pca-lb-0-11421-top-4
Stars: ✭ 113 (-3.42%)
Mutual labels: kaggle, jupyter-notebook
Deep Image Analogy Pytorch
Visual Attribute Transfer through Deep Image Analogy in PyTorch!
Stars: ✭ 100 (-14.53%)
Mutual labels: jupyter-notebook, deep-neural-networks
Btctrading
Time Series Forecast with Bitcoin value, to detect upward/down trends with Machine Learning Algorithms
Stars: ✭ 99 (-15.38%)
Mutual labels: jupyter-notebook, deep-neural-networks
Pytorch Learners Tutorial
PyTorch tutorial for learners
Stars: ✭ 97 (-17.09%)
Mutual labels: jupyter-notebook, deep-neural-networks
Tensorflow2.0 Examples
🙄 Difficult algorithm, Simple code.
Stars: ✭ 1,397 (+1094.02%)
Mutual labels: jupyter-notebook, deep-neural-networks
Isl Python
Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
Stars: ✭ 108 (-7.69%)
Mutual labels: jupyter-notebook, logistic-regression
Machine Learning Demystified
A weekly workshop series at ITP to teach machine learning with a focus on deep learning
Stars: ✭ 114 (-2.56%)
Mutual labels: jupyter-notebook, deep-neural-networks
Ds bowl 2018
Kaggle Data Science Bowl 2018
Stars: ✭ 116 (-0.85%)
Mutual labels: kaggle, jupyter-notebook
Deep Learning Boot Camp
A community run, 5-day PyTorch Deep Learning Bootcamp
Stars: ✭ 1,270 (+985.47%)
Mutual labels: kaggle, jupyter-notebook
Credit Card Fraud Detection
Three models trained to label anonymized credit card transactions as fraudulent or genuine. Dataset from Kaggle. Project through [email protected].
Project by Makena Schwinn, Sunny Zhang, and Georgy Marrero.
Click here to read about our approach and results.
Important Note: The results presented in the paper are currently inconsistent with our latest experimentations. Please view the notebooks to view our empirical results and read the paper to understand our approach.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].