Image Classification(Cats and Dogs) using 3 layered Convolutional Neural Network

Steps:

1. Import dataset

2. Import Libraries and modules

3. Create Datastructures for Input Dataset and Output

4. Process the dataset for any unwanted files

5. Carry out Exploratory Data Analysis

6. Split dataset into Training and Validation Set

7. Carry out Image Augmentation

8. Create a model using CNN

9. Train the model

10. Visualize the results

Image classification are used everwhere from Facebook's photo tagging to self driving car.

A CNN is ideal for 2-D images.

CNNs have an input layer, and output layer, and hidden layers.

The hidden layers usually consist of convolutional layers, ReLU layers, pooling layers, and fully connected layers.

### when we say convolving filters or kernels with the image means taking dot product of the filter or kernel with image.