Machine Learning NotebooksAssorted exercises and proof-of-concepts to understand and study machine learning and statistical learning theory
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Python Note《Python 学习手册》(第四版 + 第五版)笔记
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Research Paper NotesNotes and Summaries on ML-related Research Papers (with optional implementations)
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Course julia dayNotes for getting to know the Julia programming language in one day.
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Game Programmer Study Notes⚓ 我的游戏程序员生涯的读书笔记合辑。你可以把它看作一个加强版的Blog。涉及图形学、实时渲染、编程实践、GPU编程、设计模式、软件工程等内容。Keep Reading , Keep Writing , Keep Coding.
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Pigeon💬 一个轻量化的留言板 / 记事本 / 社交系统 / 博客。人类的本质是……咕咕咕?
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JupytergraffitiCreate interactive screencasts inside Jupyter Notebook that anybody can play back
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Pelican JupyterPelican plugin for blogging with Jupyter/IPython Notebooks
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Cheat Sheets🌟 All the cheat-sheets mentioned on my blog in pdf format
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Csinva.github.ioSlides, paper notes, class notes, blog posts, and research on ML 📉, statistics 📊, and AI 🤖.
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BlogSource code for my personal blog
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Blogfupengfei058's blog
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Iblog🖋 (issues blog) 欢迎订阅(watch👀) 收藏(star)
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BlogToo young, too simple. Sometimes, naive & stupid 🐌
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Stock Market Prediction Using Natural Language ProcessingWe used Machine learning techniques to evaluate past data pertaining to the stock market and world affairs of the corresponding time period, in order to make predictions in stock trends. We built a model that will be able to buy and sell stock based on profitable prediction, without any human interactions. The model uses Natural Language Processing (NLP) to make smart “decisions” based on current affairs, article, etc. With NLP and the basic rule of probability, our goal is to increases the accuracy of the stock predictions.
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Bts PytorchPyTorch implementation of BTS Depth Estimator
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Facenet Face RecognitionThis is the research product of the thesis manifold Learning of Latent Space Vectors in GAN for Image Synthesis. This has an application to the research, name a facial recognition system. The application was developed by consulting the FaceNet model.
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Keras2kubernetesOpen source project to deploy Keras Deep Learning models packaged as Docker containers on Kubernetes.
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Midu.devmidu.dev blog 📝
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CommitgenCode and data for the paper "A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes"
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Handwritten Character RecognitionThis a Deep learning AI system which recognize handwritten characters, Here I use chars74k data-set for training the model
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MishOfficial Repsoitory for "Mish: A Self Regularized Non-Monotonic Neural Activation Function" [BMVC 2020]
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NotesThe notes for Math, Machine Learning, Deep Learning and Research papers.
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Tianchi ship 2019天池智慧海洋 2019 https://tianchi.aliyun.com/competition/entrance/231768/introduction?spm=5176.12281949.1003.1.493e5cfde2Jbke
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Ds Python Data AnalysisData manipulation, analysis and visualisation in Python - specialist course Doctoral schools of Ghent University
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TrdesigntrRosetta for protein design
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25daysinmachinelearningI will update this repository to learn Machine learning with python with statistics content and materials
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Policy Gradient MethodsImplementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC
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BrihaspatiCollection of various implementations and Codes in Machine Learning, Deep Learning and Computer Vision ✨💥
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Ganpython notebooks accompanying the book Make Your Own GAN
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Transformer TtsImplementation of "FastSpeech: Fast, Robust and Controllable Text to Speech"
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TelepythTelegram notification with IPython magics.
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ComettsComet Time Series Toolset for working with a time-series of remote sensing imagery and user defined polygons
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Pyspark Setup GuideA guide for setting up Spark + PySpark under Ubuntu linux
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365datascience This Repo Contains all the exercise files for Data Science Course of 365 Datascience . The repo is split into the relevant folders & there is one exercise folder which contains all the files of that course. Don't forget to star it :D
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Neural Process FamilyCode for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
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Figure GenA Python package to effortlessly assemble images in comparison figures. Supports LaTeX, PPTX, and HTML.
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Info490 Fa16INFO 490: Foundations of Data Science, offered in the Fall 2016 Semester at the University of Illinois
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