All Projects → dantaki → Deepbake

dantaki / Deepbake

Licence: gpl-2.0
Baking Machine Learning into Great British Bake Off

Projects that are alternatives of or similar to Deepbake

Pybkb v3
Python scripts that help me be a successfull meteorologist. (Python 3)
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Duke Tsinghua Mlss 2017
Duke-Tsinghua Machine Learning Summer School 2017
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Cs231n
cs231n assignments sovled by https://ghli.org
Stars: ✭ 1,139 (+1625.76%)
Mutual labels:  jupyter-notebook
Gtsrb
Convolutional Neural Network for German Traffic Sign Recognition Benchmark
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Python
🌶 day by day
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Radio Hacking Scripts
Scripts to aid in the manipulation of electromagnetic radiation (for use with gnu_radio and SDR).
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Python
Jupyter notebooks and datasets for the interesting pandas/python/data science video series.
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Stock Market Analysis
Analyzing stock market trends using several different indicators in quantum finance. I explore machine learning and standard crossovers to predict future short term stock trends.
Stars: ✭ 66 (+0%)
Mutual labels:  jupyter-notebook
Pydata Pandas Workshop
Material for my PyData Jupyter & Pandas Workshops, I'm also available for personal in-house trainings on request
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Text Analytics With Python
Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.
Stars: ✭ 1,132 (+1615.15%)
Mutual labels:  jupyter-notebook
Etymap
Interactive visualization of Wiktionary words and etymologies.
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Dscourses18
ECON 5970: Data Science for Economists, University of Oklahoma (Spring 2018)
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Learningdataminingwithpython
Updated code for the Learning Data Mining With Python book
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Audio classification
CNN 1D vs 2D audio classification
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Pytransit
Fast and easy exoplanet transit light curve modelling.
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Blogposts
Code collection for published blog posts
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
Daru View
daru-view is for easy and interactive plotting in web application & IRuby notebook. daru-view is a plugin gem to the existing daru gem.
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook
C9 Dev Intro Data Science
Sample code for Channel 9 Python for Beginners course
Stars: ✭ 66 (+0%)
Mutual labels:  jupyter-notebook
Covid 19 Detection Flask App Based On Chest X Rays And Ct Scans
COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. A Flask App was later developed wherein user can upload Chest X-rays or CT Scans and get the output of possibility of COVID infection.
Stars: ✭ 66 (+0%)
Mutual labels:  jupyter-notebook
Exampleplots.jl
Collection of examples and recipes for Plots.jl
Stars: ✭ 65 (-1.52%)
Mutual labels:  jupyter-notebook

DeepBake

Baking Machine Learning into Great British Bake Off

Season 11 Predictions

Website


What is this?

DeepBake is a set of deep learning neural network models to predict the final rankings of GBBO contestants.

DeepBake consists of 10 models for each episode, and was trained on data from seasons 2-9.

Data include 8 variables:

  • Technical Challenge Ranking for that week and running mean from prior weeks
  • Contestant was Star Baker and running mean of times named Star Baker
  • Contestant was a favorite baker that week and running mean from prior weeks
  • Contestant was an unfavored baker that week and the running mean

Data were obtained from Wikipedia. Thanks to those who made those pages.

Data were then quantile scaled to fit a normal distribution.


Does this work?

DeepBake's performance was measured using a Leave One Out method. One season was set aside for evaluation while training the model on using the remaining seasons. A mean receiver operating curve was calculated by iterating through all seasons.

The closer the area under the curve (AUC) is to 1, the more accurate the model.

Random chance of making a correct prediction has an AUC of 0.5 (dotted diagonal line). The Episode 4 model has an AUC of 0.91 (+/- 0.04 95% Confidence Interval), meaning it has a very good chance of predicting the final GBBO winner!

DeepBake makes 5 predictions:

  • 1st Place 🏆 🏆
  • Runner-Up 🏆
  • 3rd-4th Place
  • 5th-7th Place
  • 8th Place and Below

The evaluation was measured using this tiered class system.

Note how the classifier gets better at predicting as the season progresses. This makes sense because the good bakers rise to the top (favored and star bakers) and historical data are recorded as running means.


Does this mean DeepBake can predict the winner for Season 10?

Absolutely! Here are the current standings!

Season 10 : Episode 2 Predictions

Finalist Prediction

DeepBake puts Alice in the lead with a 36.8% probability score for being the finalist. Michael, David, and Rosie are close contenders with around 21% probability.

Finalist + Runner-Up

This score is the addition of the finalist probability and the runner-up probability. It's a measurement of how likely a baker would be in the final episode.

DeepBake thinks Alice (87%), David (62%), and Michael (60%) will vie for the title of best baker.

8th and Below

DeepBake gave Dan and Jamie the highest scores (80% and 78%) for being in the bottom tier. Dan was eliminated in week 1, while Jamie was eliminated at the end of episode 2.

In fact, DeepBake can make a prediction before the judges eliminate a baker. These results suggest DeepBake correctly predicted Jamie would leave the tent!


Stay tuned for Week 3!

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].