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.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  1min
• Preprocessing Images to Use in Machine Learning Models
  47mins
• Understanding the Drawbacks of Using Deep Neural Networks with Images
  23mins
• Introducing Convolutional Neural Networks
  30mins
• Building Convolutional Neural Networks for Image Classification
  34mins
• Optimizing Image Classification with Hyperparameter Tuning
  25mins
• Performing Image Classification with Pre-trained Models
  21mins