Skills you'll learn:
Image pre-processingImage segmentation
Convolutional Neural Networks
Course
This course introduces Convolutional Neural Networks, the most widely used type of neural networks specialized in image processing. You will learn the main characteristics of CNNs that make them so useful for image processing, their inner workings, and how to build them from scratch to complete image classification tasks. You will learn what are the most successful CNN architectures, and what are their main characteristics. You will apply these architectures to custom datasets using transfer learning. You will also learn about autoencoders, a very important architecture at the basis of many modern CNNs, and how to use them for anomaly detection as well as image denoising. Finally, you will learn how to use CNNs for object detection and semantic segmentation.
This course introduces Convolutional Neural Networks, the most widely used type of neural networks specialized in image processing. You will learn the main characteristics of CNNs that make them so useful for image processing, their inner workings, and how to build them from scratch to complete image classification tasks. You will learn what are the most successful CNN architectures, and what are their main characteristics. You will apply these architectures to custom datasets using transfer learning. You will also learn about autoencoders, a very important architecture at the basis of many modern CNNs, and how to use them for anomaly detection as well as image denoising. Finally, you will learn how to use CNNs for object detection and semantic segmentation.
Intermediate
4 weeks
Last Updated December 3, 2024
Prerequisites:
Deep learning fluencyNeural network basicsIntermediate PythonBackpropagationGradient descent
Intermediate
4 weeks
Last Updated December 3, 2024
Skills you'll learn:
Image pre-processing • Image segmentation • Neural network initialization • Bounding boxes
Prerequisites:
Deep learning fluency • Neural network basics • Intermediate Python
Course Lessons
Lesson 1
Introduction to CNNs
In this lesson we will look at the main applications of CNNs, understand professional roles involved in the development of a CNN-based application, and learn about the history of CNNs.
Lesson 2
CNN Concepts
In this lesson we will recap how to use a Multi-Layer Perceptron for image classification, understand the limitations of this approach, and learn how CNNs can overcome these limitations.
Lesson 3
CNNs in Depth
In this lesson we will study in depth the basic layers used in CNNs, build a CNN from scratch in PyTorch, use it to classify images, improve its performance, and export it for production.
Lesson 4
Transfer Learning
In this lesson we will learn about key CNN architectures and their innovations, and apply multiple ways of adapting them to our use cases with transfer learning.
Lesson 5
Autoencoders
In this lesson we will design and train linear and CNN-based autoencoders for anomaly detection and for image denoising.
Lesson 6
Object Detection and Segmentation
In this lesson we will study applications of CNNs beyond image classification. We will train and evaluate an object detection model as well as a semantic segmentation model on custom datasets.
Lesson 7 • Project
Landmark Classification & Tagging for Social Media
In this project, you will apply the skills you have acquired in the Convolutional Neural Network (CNN) course to build a landmark classifier.
Taught By The Best
Nathan Klarer
Head of ML & COO of Datyra
Nathan is a data scientist and entrepreneur. He currently leads a Datyra, a 50-person AI consultancy. He was the first AI team member at $CORZ. Prior to that he founded a VC backed data startup that was acquired. Nathan was named “27 CEO's Under 27” by Entrepreneur.com and has been featured in Inc. and Forbes.
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