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Computer Vision with MobileNet
1,423 students
What you'll learn
- Gain the ability to train an image classification model using MobileNet architecture
- To become familiar with the SuperGradients training library and how deep learning practitioners can use it to shorten the model development lifecycle.
- To gain practical skills for developing and training neural networks for image classification tasks.
- Be able to discuss ways to reduce computational complexity of convolutional neural networks
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Course content
5 sections • 12 lectures • 1h 4m total length
Requirements
- The target learners are students with a strong foundation in machine learning and a basic understanding of deep learning. These students need to learn about the history and current state of computer vision, as well as gain practical skills for developing and training deep neural networks for image classification tasks. It has a secondary audience of professionals in machine learning and computer vision who are looking to stay up to date on the latest developments and techniques in the field. These professionals will learn about the SuperGradients training library, which could improve their model development process.
Description
This course provides a comprehensive understanding of MobileNet, a state-of-the-art deep learning architecture for resource-constrained devices such as smartphones and IoT devices. MobileNet is optimized for real-time image and video classification, making it an ideal choice for cutting-edge computer vision applications.
One of the key innovations in MobileNet is the use of depthwise separable convolutions, which allow for efficient computation and reduced memory footprint compared to traditional convolutional neural networks (CNNs). In this course, you'll learn about the computational costs of standard convolutions and how depthwise separable convolutions reduce computational overhead.
You'll also delve into the architecture of MobileNet, including the use of linear bottlenecks and inverted residuals to optimize performance. In addition, you'll explore squeeze and excitation layers, which add a self-attention mechanism to the network, allowing it to focus on the most important features in an input image.
The course includes hands-on demonstrations and practical exercises that allow you to experience the power of MobileNet in action. You'll perform image classification on the Describable Textures Dataset using the SuperGradients training library and see how MobileNet can solve real-world problems in computer vision.
In conclusion, this course is designed for anyone interested in deep learning, computer vision, or edge computing. Whether you're a computer science student, a machine learning engineer, or a researcher, you'll leave this course with a comprehensive understanding of MobileNet, its architecture, and its applications. So, don't miss out on this opportunity to advance your skills in deep learning on edge!
Who this course is for:
- To complete this course, learners should have a strong foundation in machine learning and a basic understanding of computer vision. This includes knowledge of supervised learning, neural networks, and image processing. Regarding skill level, learners should to be advanced beginners to intermediate. They have a solid understanding of the fundamental concepts and techniques of machine learning but may still be learning about more advanced topics such as computer vision. They have experience with Python, Pandas, scikit-learn and PyTorch.