Deep Learning with Python & Pytorch for Image Classification

In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques.

• You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models.
• You will learn how to connect Google Colab with Google Drive and how to access data.
• You will perform data preprocessing using different transformations such as image resize and center crop etc.
• You will perform two types of Image Classification, single-label Classification, and multi-label Classification using deep learning models with Python.

In single-label Cassification, when you feed input image to the network it predicts single label. In multi-label Classification, when you feed input image to the network it predicts multiple labels.  You will Learn Deep Learning architectures such as ResNet and AlexNet. The ResNet is a deep convolution neural network proposed for image classification and recognition. ResNet network architecture designed for classification task, trained on the imageNet dataset of natural scenes that consists of 1000 classes. Deep residual nets won the 1st place on the ILSVRC 2015 Classification challenge. Alexnet is a deep convolution neural network trained on ImageNet dataset to classify the images into 1000 classes. It has five convolution layers followed by max-pooling layers, and 3 fully connected layers. AlexNet won the ILSVRC 2012 Classification challenge. You will perform image classification using ResNet and AlexNet deep learning models. The Deep Learning community has greatly benefitted from these open-source models where pre-trained models are a major reason for rapid advancements in the Computer Vision and deep learning research.