Deep Learning Image Classification in PyTorch 2.0

About this course

Welcome to this Deep Learning Image Classification course with PyTorch2.0 in Python3. Do you want to learn how to create powerful image classification recognition systems that can identify objects with immense accuracy? if so, then this course is for you what you need! In this course, you will embark on an exciting journey into the world of deep learning and image classification. This hands-on course is designed to equip you with the knowledge and skills necessary to build and train deep neural networks for the purpose of classifying images using the PyTorch framework.

We have divided this course into Chapters. In each chapter, you will be learning a new concept for training an image classification model. These are some of the topics that we will be covering in this course:

• Training all the models with torch.compile which was introduced recently in Pytroch2.0 as a new feature.
• Install Cuda and Cudnn libraires for PyTorch2.0 to use GPU. How to use Google Colab Notebook to write Python codes and execute code cell by cell.
• Connecting Google Colab with Google Drive to access the drive data.

Master the art of data preparation as per industry standards. Data processing with torchvision library. data augmentation to generate new image classification data by using:- Resize, Cropping, RandomHorizontalFlip, RandomVerticalFlip, RandomRotation, and ColorJitter.

Implementing data pipeline with data loader to efficiently handle large datasets.

Deep dive into various model architectures such as LeNet, VGG16, Inception v3, and ResNet50.

Each model is explained through a nice block diagram through layer by layer for deeper understanding.

Implementing the training and Inferencing pipeline.

Understanding transfer learning to train models on less data.

Display the model inferencing result

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