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Name: Pablo Ríos
Type: User
Name: Pablo Ríos
Type: User
Advanced Scikit-learn training session
A wizard's guide to Adversarial Autoencoders
Replicates Adversarial Autoencoder architecture from [Makhzani, Alireza, et al. "Adversarial autoencoders." arXiv preprint arXiv:1511.05644 (2015)](https://arxiv.org/abs/1511.05644). The code is adapted from Naresh's implementation [here](https://github.com/Naresh1318/Adversarial_Autoencoder).
Implementation of Camelyon'16 grand challenge
The 3rd edition of course.fast.ai
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Classification of Lung cancer slide images using deep-learning
Practical Deep Learning for Genomic Prediction: A Keras based guide to implement deep learning
Extension library for the Varian Eclipse Scripting API
My projects for fast.ai's Practical Deep Learning for Coders course (fast.ai part1v3)
My projects for fast.ai's Deep Learning from the Foundations course (fast.ai part2v3)
Deep learning models to predict the gamma index of treatment plans based on calculated fluence maps for intensity modulated radiation therapy (IMRT).
GPT4 & LangChain Chatbot for large PDF docs
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Our solution for ICIAR 2018 Grand Challenge
JAMA 2016; 316(22) Replication Study
Team o_O solution for the Kaggle Diabetic Retinopathy Detection Challenge
1st place of Kaggle's RSNA Screening Mammography Breast Cancer Detection competition
Open Machine Learning Course
Course demos and handouts for our Modern APIs with FastAPI course.
Implementation of the paper "Predicting gamma passing rates for portal dosimetry based IMRT QA using machine learning"
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
A Deep Learning talk+tutorial for medical image processing
2nd Place Solution of the Kaggle Competition - Santander Product Recommendation
Spatially-sparse convolutional networks. Allows processing of sparse 2, 3 and 4 dimensional data.Build CNNs on the square/cubic/hypercubic or triangular/tetrahedral/hyper-tetrahedral lattices.
This repository contains demos Niels Rogge made with the Transformers library by HuggingFace.
Code samples for ESAPI and other Varian APIs and web services.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.