Image Classification

• Implemented a logistic regression model for image classification of cats vs dogs.
• Published a detailed blog post explaining the bits and piecws of the entire process.
• The dataset used was the cats vs dogs image classification dataset from Kaggle.
• The dataset contains 25000 images in all and I split them as 20,000 in the training set and 5000 in the test set.
• Used Mean Squared Loss for Gradient Descent optimization and backpropagation.
• The model trained for 5000 epochs at a learning rate of 0.003 achieves 61% test set accuracy.

Steps to replicate

1. Run the preprocessing notebook to convert the images into our actual dataset i.e. the one we will feed the model.
2. Go through the logistic_regression_with_neural_networks_mindset notebook for the training process.