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

代码记录

开源的各大比赛baseline

Here lies all the exercises I implement and share in my website

Various math-related things in Python code

Repo replaced by cogsec-collaborative/AMITT

To make it easy to benchmark AI accelerators

PyTorch Implementation of Realtime Multi-Person Pose Estimation project.

Notebooks for upcoming fastai book (draft / incomplete)

nbconvert as a web service: Render Jupyter Notebooks as static web pages

DEPRECATED 尝试使用中文文本训练RNN来产生中文小说。（No longer under maintanance.）

This repository contains code for the paper Learning by Association - A versatile semi-supervised training method for neural networks (CVPR 2017) and the follow-up work Associative Domain Adaptation (ICCV 2017).

2nd Place Solution 💰🥈

Semi-Supervised Video Object Segmentation (VOS) with Tensorflow. Includes implementation of *MaskRNN: Instance Level Video Object Segmentation (NIPS 2017)* as part of the NIPS Paper Implementation Challenge.

PennAI: AI-Driven Data Science

Reading notes on the excellent "Designing Data-Intensive Applications"

Statistical Rethinking (2nd Ed) with Tensorflow Probability

All the ML algorithms, ML models are coded from scratch by pure Python/Numpy with the Math under the hood. It works well on CPU.

Lecture notebooks and coding assignments for the quantum machine learning MOOC created by Peter Wittek on EdX in the Spring 2019

A New, Interactive Approach to Learning Python

Python tutorials and puzzles to share with the world!

Assignments for Berkeley CS 285: Deep Reinforcement Learning (Fall 2020)

A numpy implementation of forward energy from the paper “Improved Seam Carving for Video Retargeting" (2008)

Stereo convolutional neural network for depth map prediction from stereo images

📈 如何用深度强化学习自动炒股

Introduction to Statistical Modeling with Python (PyCon 2017)

Modification of fast.ai deep learning course notebooks for usage with Keras 2 and Python 3.

MINE: Mutual Information Neural Estimation in pytorch (unofficial)

Pytorch Implementation of Perceptual Losses for Real-Time Style Transfer and Super-Resolution

Jupyter Notebook with Python samples for the Cognitive Services Computer Vision API

Chapterwise code available in the book

Collection of Artificial Intelligence projects.

Repositório do Deep Learning Book - www.deeplearningbook.com.br

Pytorch implementation of a StyleGAN encoder. Images to latent space representation.

I used Machine Learning to make a Logistic Regression model using scikit-learn, pandas, numpy, seaborn and matplotlib to predict the results of FIFA 2018 World Cup.

Tutorials for SimFin - Simple financial data for Python

SphereFace+ Implementation for <Learning towards Minimum Hyperspherical Energy> in NIPS'18.

简明 SSD 目标检测模型 keras version（交通标志识别 训练部分见 dev 分支）

Fundamentals of Spark with Python (using PySpark), code examples

Welcome to Glacier Data Project. A post-wuhan2020 project for data science

Projects done in the Data Engineering Nanodegree by Udacity.com

Mish Deep Learning Activation Function for PyTorch / FastAI

Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron

IPyPlot is a small python package offering fast and efficient plotting of images inside Python Notebooks. It's using IPython with HTML for faster, richer and more interactive way of displaying big numbers of images.

<펭귄브로의 3분 딥러닝, 파이토치맛> 예제 코드

Learn the pyspark API through pictures and simple examples

Main code for Parcels (Probably A Really Computationally Efficient Lagrangian Simulator)

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

This repository contains the Tutorials for the NPTEL MOOC on Machine Learning.

Implementation of the paper "NetGAN: Generating Graphs via Random Walks".

A minimal implementaion (less than 150 lines of code with visualization) of DCGAN/WGAN in PyTorch with jupyter notebooks

python100天学习资料

Performance analysis of predictive (alpha) stock factors

Python solutions to the problem sets of Stanford's graduate course on Machine Learning, taught by Prof. Andrew Ng

ASAP: Prioritizing Attention via Time Series Smoothing