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Learn about convolutional neural networks with online courses

Unlock the potential of advanced AI and machine learning with convolutional neural networks (CNNs). Expand your knowledge with online courses delivered through edX.

What is a convolutional neural network?

ConvNet, short for convolutional neural network, is a type of deep learning system used for image recognition and classification.Footnote 1 In essence, it gives a computer the ability to see and understand images, making it a valuable resource for projects involving batch images, such as robot vision and self-driving cars. The distinguishing factor of CNN, unlike regular neural networks, is that it is designed to process visual data.

CNNs use a 3D structure with height, width, and depth to analyze images. The data then goes into the input layer and passes through hidden layers before producing the final result. It can be likened to a multi-layered camera filter where each layer understands a specific aspect of the image.

Convolutional neural network | Introduction Image Description

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Convolutional neural network course curriculum

An online course on Convolutional Neural Networks (CNN) can cover a range of concepts and skills related to deep learning and image processing. Key topics you might encounter in online courses include:

  • Introduction to deep learning: Understand the fundamentals of deep learning, neural networks and their relevance in image processing.

  • CNN architecture: Explore the structure and components of CNNs, including convolutional layers, pooling layers, and fully connected layers.

  • Convolution and filters: Learn how convolutional layers use filters and feature maps to extract patterns and features from images.

  • Image classification: Study techniques for image classification, object recognition, and feature extraction using CNNs.

  • Image preprocessing: Discover how to preprocess and clean image data to improve model accuracy.

  • Transfer learning: Explore the concept of transfer learning, where pre-trained CNN models can be fine-tuned for specific tasks.

edX offers a variety of educational opportunities for learners interested in studying these topics, as well as a host of other disciplines. A boot camp can provide flexible hands-on learning for those who want to upskill quickly, while executive education programs are designed for busy professionals. You can also pursue a more comprehensive curriculum in a bachelor’s degree program or, for more advanced learners, a master’s degree program. Find the right learning path for you.

Why learn about convolutional neural networks?

A background in CNNs can be beneficial for careers in fields related to computer vision, image processing, and machine learning. Professional roles in these fields include:

  • Computer vision engineer: Focuses on developing algorithms and systems for interpreting visual data from images and videos for applications like facial recognition, object detection, and autonomous vehicles.

  • Machine learning engineer: Designs and implements machine learning models, including CNNs, for a wide range of applications, such as natural language processing, recommendation systems, and image analysis.

  • Data scientist: Conducts image data analysis, pattern recognition, and predictive modeling in various industries, including healthcare, finance, and e-commerce.

  • Deep learning researcher: Researches new CNN architectures, optimization techniques, and applications.

  • AI/Machine learning consultant: Provides expertise on implementing CNN-based solutions for clients in diverse sectors to optimize their operations and products.

These positions may require different levels of experience and technical knowledge. Before deciding on a specific learning path, research the positions you hope to pursue and align your coursework with your career goals. When you are ready to start your learning journey, find the courses you need with edX.