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A simple custom digit recognizer implementation - Create 4 models for multi‐class classification of the input images : Two Basic Machine Learning Models, One Multi‐layer Perceptron and One Simple Convolutional Network

Jupyter Notebook 99.96% Python 0.04%

digit_recognizer's Introduction

digit_recognizer

A simple custom digit recognizer implementation.

Tasks

Model Creation

Create 4 models for multi-class classification of the input images
- A, B) Two Basic Machine Learning Models
- C) Multi-layer Perceptron
- D) Simple Convolutional Network

Development Tools

Stand-alone Jupyter Notebook
Solutions Implemented with Sklearn, Tensorflow, and/or Pytorch (free choice by each student or mini-group)

Assignment Description – Overview

Tasks

Understanding and EDA (Exploratory Data Analysis) of the dataset provided
Build the Models
Train the Models
Cross-Validation + Basic GridSearch hyper-parameter tuning
Performance report / Comparison of results

Submission Requirements

Code (documented and executed)
Models
Selected model (ready to be scored independently with a “test” set)

Grading

As defined in the attached rubrics file, the grading will have 3 components:

35% CONCEPTUAL
35% CODING
30% BAKE-OFF Ranking of results (performance)

For the scoring and ranking of results, an IID(*) “test” set will be used.

Note: the ranking grade is based on the “discrepancy” of performance with respect to the average standard solution (only really under-performing models would have a significant penalty in this rubric).

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