6192 Open source projects that are alternatives of or similar to Dragonn

Jupyter notebooks and code examples for my Kaggle Beginners' Guide.

Dense Depth for Autonomous Driving (DDAD) dataset.

"Data Mining in Action Course", Moscow Institute of Physics and Technologies

Stanford

lidc nodule detection with CNN and LSTM network

These are my learning exercices from Coursera

Discover augmentation strategies tailored for your dataset

Learn Python, Easy to learn, Awesome

Code and instructions for making topographic maps of planets and moons

Various examples for Google Earth Engine in Python using Jupyter Notebook

基于Tensorflow2的深度学习开源书籍

scikit-learn wrappers for Python fastText.

One has no future if one couldn't teach themself.

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

[ICCV 2019] Monocular depth estimation from a single image

Machine Learning notebooks for refreshing concepts.

This repository contains the lecture materials for EECS 545, a graduate course in Machine Learning, at the University of Michigan, Ann Arbor.

Respository for Open-Science modular microscope system.

Training Diary

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Train and Deploy Simple Machine Learning Model With Web Interface - Docker, PyTorch & Flask

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Notes and Summaries on ML-related Research Papers (with optional implementations)

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All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

An easy to use VR toolkit for Oculus Quest development using the Godot game engine

Step-by-step Deep Leaning Tutorials on Apache Spark using BigDL

Devenez Data-Scientist sur Le Wagon On Demand

A Simple U-net model for Retinal Blood Vessel Segmentation based on tensorflow2

Deep Learning / NLP tutorial for Chatbot Developers

Tutorial of geospatial data processing using python 用python分析时空数据的教程(in Chinese and English )

Find lane lines on the road using Python and OpenCV, applying Canny edge detectors and Hough line transforms

Text recognition (optical character recognition) with deep learning methods.

Python技术 公众号文章代码实例

Código Python, Jupyter Notebooks, archivos csv con ejemplos para los ejercicios del Blog aprendemachinelearning.com y del libro Aprende Machine Learning en Español

Deploy AutoML as a service using Flask

Protein Graph Library

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

Leverage the flexibility of Jupyterlab through the power of your NVIDIA GPU to run your code from Tensorflow and Pytorch in collaborative notebooks on the GPU.

GeostatsPy Python package for spatial data analytics and geostatistics. Mostly a reimplementation of GSLIB, Geostatistical Library (Deutsch and Journel, 1992) in Python. Geostatistics in a Python package. I hope this resources is helpful, Prof. Michael Pyrcz

DS4C: Data Science for COVID-19 in South Korea

🏆 A ranked list of awesome Jupyter Notebook, Hub and Lab projects (extensions, kernels, tools). Updated weekly.

Chromosome visualization for the web

Coherent Semantic Attention for image inpainting(ICCV 2019)

Machine Learning Tutorials in Python

Visual Question Answering Demo on pretrained model

中文文本错别字检测以及自动纠错 / Autochecker & autocorrecter for chinese

Survival analsyis and time-to-failure predictive modeling using Weibull distributions and Recurrent Neural Networks in Keras

Some practices about deep learning

Python/Keras implementation of integrated gradients presented in "Axiomatic Attribution for Deep Networks" for explaining any model defined in Keras framework.

Wi-Fi Geolocation Spoofing with the ESP8266

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Natural Language Processing with TensorFlow, published by Packt

Tools for using a variational auto-encoder for latent image encoding and generation.

Malware Detection and Classification Using Machine Learning

Keras implementation of the paper "3D MRI brain tumor segmentation using autoencoder regularization" by Myronenko A. (https://arxiv.org/abs/1810.11654).