Aiexponent

Flower Image Classification

We will see how to model a deep learning network which can classify flowers into “roses” and “daisies”. This is a toy dataset and its purpose is to introduce you to the key concepts and methodologies.

Objectives:

  1. How to set up an end-to-end pipeline for training deep learning models
  2. Preprocessing techniques: Morphological transformations etc.
  3. Data augmentation using data generators
  4. Building a network: Ablation experiments, hyperparameter tuning, storing the best model in the disk etc.

This post explains the basic concepts used in the image classification model. If you can want to skip and jump directly to the code, here is the Github Repo.

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Flower Image Classification

We will see how to model a deep learning network which can classify flowers into “roses” and “daisies”. This is a toy dataset and its purpose is to introduce you to the key concepts and methodologies.

Multiple Linear Regression

The objective of this notebook is to provide core concepts of Multiple Regression analysis such as correlation and use of RFE for feature reduction

Linear Regression

Regression is a handy tool to find relationships between dependent variables and independent variables. However, we cannot always fit a regression line to any given dataset. The objective of this notebook is to provide core concepts of Linear Regression analysis.