5921 Open source projects that are alternatives of or similar to Datascience_book

CCKS 2018 开放领域的中文问答任务 1st 解决方案

Pythonic Objects

legend

Exercise Solutions for Reinforcement Learning: An Introduction [2nd Edition]

A DroneBlocks course on drone programming with Tello using Python scripts

PyTorch tutorial for learners

Unofficial PyTorch implementation of "Learning a Neural Solver for Multiple Object Tracking"

Your Advanced Twitter stalking tool

A Leiningen plugin to integrate clojure with jupyter notebook

📈 Interactive comparison of Python plotting libraries for exploratory data analysis. Examples of using Pandas plotting, plotnine, Seaborn, and Matplotlib.

Keras implementation of GradCAM.

百度机器阅读理解竞赛模型代码 ，获得 final 第三名

Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes

[Cancer Research] 3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas

An implementation of the Viterbi Algorithm for training Hidden Markov models. This repo accompanies the video found here: https://www.youtube.com/watch?v=kqSzLo9fenk

Colabs for ML Day Tokyo

Dashboard template for Voilà based on GridStackJS

This project is a loose implementation of paper "Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach"

Tutorial teaching the basics of Keras and some deep learning concepts

IJCAI-17 口碑商家客流量预测

Graph-Based Data Science: an abstraction layer in Python for building knowledge graphs, integrated with popular graph libraries – atop Pandas, RDFlib, pySHACL, RAPIDS, NetworkX, iGraph, PyVis, pslpython, pyarrow, etc.

机器学习相关

Demo for CVPR 2017 paper

Pytorch Implementation of Interaction Networks for Learning about Objects, Relations and Physics

Implementation of different machine learning techniques

A Scala kernel for Jupyter

Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)

NJU Master Course **Big Data Mining and Analysis**

YOLOv2 trained against custom dataset

kmeans using PyTorch

HLS Project of pp4fpgas - https://github.com/xupsh/pp4fpgas-cn

书籍：深度学习框架pytorch入门与实践

Artificial Intelligence app that detects diseases in plants using a deep learning model

Introduction to Chemical Engineering Analysis

ResUNet, a semantic segmentation model inspired by the deep residual learning and UNet. An architecture that take advantages from both(Residual and UNet) models.

Source code about Python Development

collaborative audio module for fast.ai

Hands-on Exploratory Data Analysis with Python, published by Packt

Unofficial implementation of Octave Convolutions (OctConv) in TensorFlow / Keras.

CubiCasa5k floor plan dataset

Created the contents of this repo originally for a workshop I gave at UCLA

Source material for Data Science for Telecom Tutorial at Strata Singapore 2015

Graph Neural Ordinary Differential Equations

Jupyter notebooks with exercises for the No bullshit guide to linear algebra.

Lecture Notes for Linear Algebra Featuring Python

code for paper "Graph Structure of Neural Networks"

Finding similar images in the Holidays dataset

Convolutional Neural Network based regression approach for estimating machinery's remaining useful life

Deep Learning Food Classifier for iOS using Keras and Tensorflow

Enriching BERT with Knowledge Graph Embedding for Document Classification (PyTorch)

A Primer on Gaussian Processes for Regression Analysis (PyData NYC 2019)

The Numenta Anomaly Benchmark

A companion code for my Medium post

Code, slides, and documentation for the talks I have given.