Abdul

Salam

As a part of my portfolio, I have created a project focused on the classification of images of cats and dogs using deep learning techniques. The project involved training a convolutional neural network (CNN) on a dataset consisting of thousands of labeled images of cats and dogs. The CNN architecture used in the project was designed to learn important features from the images, such as the shape of the ears, eyes, nose, and fur patterns. The model was trained using the popular TensorFlow framework and achieved high accuracy rates on the test set. To enhance the performance of the model, several techniques were employed, including data augmentation, which involves artificially generating new training samples by performing random transformations on the existing images. Additionally, transfer learning was utilized by utilizing a pre-trained model to fine-tune the model to the cat and dog classification task. Overall, this project showcases my skills in deep learning and computer vision