mercuriuz Goto Github PK

autodiff

awesome

A curated list of awesome lists

awesome-deep-vision

A curated list of deep learning resources for computer vision

bundler_sfm

Bundler Structure from Motion Toolkit

caffe2

Caffe2 is a cross-platform framework made with expression, speed, and modularity in mind.

cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

fzf

:cherry_blossom: A command-line fuzzy finder written in Go

iris

[ACQUIRED] The fastest web framework for Go in (THIS) Earth. HTTP/2 Ready-To-GO.

lectures

Oxford Deep NLP 2017 course

leveldb

LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.

mercuriuz.github.io

GitHub Pages for my personal blogs.

oh-my-zsh

A delightful community-driven framework for managing your zsh configuration. Includes 200+ optional plugins (rails, git, OSX, hub, capistrano, brew, ant, php, python, etc), over 140 themes to spice up your morning, and an auto-update tool so that makes it easy to keep up with the latest updates from the community.

okvis

OKVIS: Open Keyframe-based Visual-Inertial SLAM.

omnianomaly

KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

opencv

Open Source Computer Vision Library

openexr

OpenEXR is a high dynamic-range (HDR) image file format developed by Industrial Light & Magic for use in computer imaging applications

orb_slam2

Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

play_leetcode

pymc3

Probabilistic Programming in Python. Uses Theano as a backend, supports NUTS and ADVI.

pytudes

Python programs to practice or demonstrate skills.

reveal.js

The HTML Presentation Framework

simple_templates

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.