Video: .MP4, AVC, 1024x768, 29 fps | Audio: English, AAC, 48 KHz, 2 Ch | Duration: 3h 4m | 683 MB
Instructor: Janani Ravi
This course covers the parts of building enterprise-grade image classification systems like image pre-processing, picking between CNNs and DNNs, calculating output dimensions of CNNs, and leveraging pre-trained models using PyTorch transfer learning.
Perhaps the most ground-breaking advances in machine learnings have come from applying machine learning to classification problems. In this course, Image Classification with PyTorch, you will gain the ability to design and implement image classifications using PyTorch, which is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs. First, you will learn how images can be represented as 4-D tensors and then pre-processed to get the best out of ML algorithms. Next, you will discover how to implement image classification using Dense Neural Networks; you will then understand and overcome the associated pitfalls using Convolutional Neural Networks (CNNs). Finally, you will round out the course by understanding and using the most powerful and popular CNN architectures such as VGG, AlexNet, DenseNet and so on, and leveraging PyTorch’s support for transfer learning. When you’re finished with this course, you will have the skills and knowledge to design and implement efficient and powerful image classification solutions using a range of neural network architectures in PyTorch.