Image source

This is a 3-part series on end to end case study based on Kaggle problem.

Table of Contents

  1. Why this is a ML problem ?
  2. How to approach this problem ?
  3. Existing Solutions
  4. F1- Score Maximization
  5. Exploratory Data Analysis
  6. References

Introduction

Business Problem

Ordering food supplies online is a new way of restocking groceries and other essential items. Be it early morning or midnight, ordering groceries online is stress-free activity without much hassle. But what happens when you forget few items while adding items to the cart or want to get better suggestions on your items? Will you wait for a couple of…


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This is a 2-part series on end to end case study based TableNet research paper.

In the last post we discussed Pytorch implementation of TableNet, refer Part 1.

Table of Contents

  1. Fixing Image Problems and Re-Training
  2. Improving model predictions using OpenCV2
  3. OCR predictions
  4. Deployment
  5. Future Work
  6. End Notes
  7. References

Post EDA of the solution

Looking at evaluation metrics to judge the model behavior is not enough. We should be able to answer questions like:

→Are we able to explain the outputs of Model w.r.t. Input ? ( Explainability is highly effective in classification and regression models)

→ Can the model performance be improved…


Image source

This is a 2-part series on end to end case study based TableNet research paper.

Table of Contents

  1. Business Objective
  2. Dataset Overview
  3. DL Problem and Performance Metric
  4. Data Pre-processing
  5. Exploratory Data Analysis
  6. Model Architecture
  7. Model Implementation
  8. Training
  9. Model Evaluation
  10. References

Introduction

With the increase use of mobile devices, customers tend to share documents as images rather than scanning them. These images are later processed manually to get important information stored in Tables. These tables can be of different sizes and structures. It is therefore expensive to get information in Tables from images.

With TableNet we will employ an end-to-end Deep learning architecture which…


Image source

This is a 3-part series on end to end case study based on Kaggle problem.

In the last 2 posts, we discussed the business problem , EDA, F1 — Maximization, Feature Engineering and Trained few models.

Please refer Part 1 and Part 2 before moving forward.

Table of Contents

  1. Solutions for Cold Start Problems
  2. Build the pipeline
  3. Build Web-API
  4. Predictions
  5. Future Work
  6. End Notes
  7. References

Cold Start Problems

Before we move on to deployment part , we need to address few cold start issues here.

  1. New user : What products can we recommend to a new user ?

There can be many solutions…


Image source

This is a 3-part series on end to end case study based on Kaggle problem.

In the last post we discussed ML approach for this problem , and drew some conclusions with Exploratory Data Analysis, refer Part 1.

Table of Contents

  1. Feature Engineering
  2. Generate Training and Test Data
  3. Training Models
  4. Generate Submission Files
  5. Improve the model
  6. References

Modelling Strategy

Strategy : 1

Generate Training Data (using prior_orders_data )

  • For every orders in prior_orders_data, take n-1 orders of every user for feature engineering.
  • nth order of every user will used to label the dependent variable i.e. reordered.

example : —

let, user A have…


Image Source: https://github.com/brendenlake/omniglot

Abstract

For a neural network to learn features from images in order to classify them we need data, lots of data. It is difficult for a model to learn from very few samples per class.

MNIST dataset has nearly 60000 training images for numbers 0–9 (10 classes).

We will implement One shot learning to build a model which will correctly make predictions given only a single example of each new class.

Background

As humans , when we are presented with new object , we quickly pickup patterns , shape and other features. When we are presented with same kind of object in…

Arun Sagar

Deep Learning Engineer with particular focus on applications of Computer Vision and Autonomous vehicles

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