All Projects → willkurt → Probandstats Pydatanyc2019

willkurt / Probandstats Pydatanyc2019

Licence: mit
Introduction to Probability and Statistics

Projects that are alternatives of or similar to Probandstats Pydatanyc2019

Accurate Binary Convolution Network
Binary Convolution Network for faster real-time processing in ASICs
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Randomized Svd
demos for PyBay talk: Using Randomness to make code faster
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Winter 2016 Cs231n
Assignments: CNN for Visual Recognition.
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Do Zero Ao Ml
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Spotifyml
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Pure Numpy Feedfowardnn
Simple feedforward neural network class written in pure python+numpy
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Chinesetrafficpolicepose
Detects Chinese traffic police commanding poses 检测中国交警指挥手势
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Wsdm Adhoc Document Retrieval
This is our solution for WSDM - DiggSci 2020. We implemented a simple yet robust search pipeline which ranked 2nd in the validation set and 4th in the test set. We won the gold prize at innovation track and bronze prize at dataset track.
Stars: ✭ 50 (+0%)
Mutual labels:  jupyter-notebook
Salmonte
SalmonTE is an ultra-Fast and Scalable Quantification Pipeline of Transpose Element (TE) Abundances
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Feature Engineering Book
Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018
Stars: ✭ 1,052 (+2004%)
Mutual labels:  jupyter-notebook
Lipreading
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Universodiscreto
Códigos explicados nos vídeos do canal Universo Discreto (YouTube)
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Mlapp Solutions
Solutions in Python for Kevin Murphy's Machine Learning: a Probabilistic Perspective
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Eeg Classification Using Recurrent Neural Network
Used LSTM Network to classify eeg signals based on stimuli the subject recieved (visual or audio)
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
My Projects
It's my projects
Stars: ✭ 50 (+0%)
Mutual labels:  jupyter-notebook
Mckinsey Smartcities Traffic Prediction
Adventure into using multi attention recurrent neural networks for time-series (city traffic) for the 2017-11-18 McKinsey IronMan (24h non-stop) prediction challenge
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Teal deer
Teal deer (from TL;DR) helps you get the gist of all the stuff you need to read, so you don't have to read it all at once.
Stars: ✭ 49 (-2%)
Mutual labels:  jupyter-notebook
Vapoursynthcolab
AI Video Processing/Upscaling With VapourSynth in Google Colab
Stars: ✭ 47 (-6%)
Mutual labels:  jupyter-notebook
Live Video Analytics
A collection of reference applications using live video analytics capabilities in Azure Media Services
Stars: ✭ 50 (+0%)
Mutual labels:  jupyter-notebook
Tensorflow From Zero To One
TensorFlow 最佳学习资源大全(含课程、书籍、博客、公开课等内容)
Stars: ✭ 1,052 (+2004%)
Mutual labels:  jupyter-notebook

PyDataNYC2019 - Introduction to Probability and Statistics

NOTE: as of 10/27/2019 this Tutorial is very much a work in progress. Stayed tune as PyData NYC is Nov. 4-6, as that data approaches this content should be much closer to ready.

This project contains a whirlwind introduction to probability and statistics that will be part of a tutorial session at PyData NYC 2019. The tutorial consists of 3 notebooks that will walk you through the stages of stastical analysis.

Install Instructions

  1. clone this project to your local machine:

git clone [email protected]:willkurt/ProbAndStats-PyDataNYC2019.git

  1. Go into project directory:

cd ProbAndStats-PyDataNYC2019

  1. Create a virtual environment for this project

python3 -m venv /path/to/new/virtual/environment

  1. Activate the virtual environment

source /path/to/new/virtual/environment/bin/activate

  1. Pip install the requirments (make sure your at the project's root directory)

pip install -r requirements.txt

  1. Run the jupyter notebook and you should be all set!

jupyter notebook

About this Tutorial

The aim of this tutorial is to give you a 90 minutes overview covering as much ground as is possible in understanding statics. There is a lot to take in, so if you're going to 90 minutes don't worry about throughly understanding everything. The goal here is just so that you get a sense of what probability and statistics is all about and also understand some ideas about how to solve problems using statistics and what all those fancy tools like pymc3 are doing. If you have more than 90 minutes then spending some extra time playing with the notebooks here should provide you with a pretty strong background in how to think about problems statistically.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].