Image Classification with PyTorch

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.
Course info
Level
Advanced
Updated
Aug 9, 2019
Duration
3h 5m
Table of contents
Course Overview
Preprocessing Images to Use in Machine Learning Models
Understanding the Drawbacks of Using Deep Neural Networks with Images
Introducing Convolutional Neural Networks
Building Convolutional Neural Networks for Image Classification
Optimizing Image Classification with Hyperparameter Tuning
Performing Image Classification with Pre-trained Models
Description
Course info
Level
Advanced
Updated
Aug 9, 2019
Duration
3h 5m
Description

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.

About the author
About the author

A problem solver at heart, 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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
[Autogenerated] Hi, My name is Johnny Ravi and welcome to the school's on image classifications with by Dodge a little about myself. I have a master's degree in electrical engineering from Stanford and have that companies such as Microsoft, Google and Flip Card at Google Lovers, one of the first engineers working on drill time collaborative editing in Coble, Dogs and I hold four patterns for us on the line technologies. I currently work on my own startup lunatic on a studio for high quality video content. Perhaps the most ground breaking advances in machine learning recently have come from applying machine learning classifications problems. In this course, you begin the ability to design and implement image classifications using, I thought, which is fast emerging as a popular choice for building deep learning. Moderns going to its flexibility, ease off use on built in support for optimized hardware such as cheapies. First, he will learn how images can be represented as four dimensional sensors and then three process to get the best out of Emily. Next, you will discover how to implement image classifications using against newly networks. You will then understand and overcome the associate it with false using convolution neural networks. Finally, you will round out the course by understanding and using the most powerful and popular CNN architectures such as Lee Gi, Alex, Met, Dance Net and so on and leveraging Fight or to support for transfer learning. When you're finished with the schools, you will have the skills and knowledge to design and implement efficient and powerful image classifications solutions using a range of neural network architectures and pytorch.