Deep Learning for Computer Vision from Coursera

You'll begin with the building blocks of deep learning - understanding how multilayer perceptrons (MLPs) work, and exploring normalization techniques that stabilize and accelerate training. You'll then dive into unsupervised learning with autoencoders and discover the magic behind Generative Adversarial Networks (GANs) that can create realistic images from noise. After, you'll master the architecture that revolutionized computer vision by learning how CNNs extract spatial hierarchies and patterns from images for tasks like object detection and recognition. Finally, you'll explore cutting-edge architectures. ResNet introduces residual learning for deeper networks, while U-Net powers precise image segmentation in medical imaging and beyond.

Whether you're a data scientist, engineer, or AI enthusiast, this course equips you with the skills to build and deploy deep learning models for real-world vision tasks. With practical examples and guided learning, you'll gain both theoretical understanding and hands-on experience.

This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:

MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder

MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

What's inside

Syllabus

Neural Network, Multi-Layer Perceptron, and Normalization

Welcome to Deep Learning for Computer Vision, the second course in the Computer Vision specialization. In this first module, you'll be introduced to the principles behind neural networks and their use in visual recognition tasks. You'll begin by learning the basic building blocks—neurons, weights, biases—and progress toward constructing simple multi-layer perceptrons. Then, you'll discover key activation concepts like batch processing and graph-matrix conversions. Finally, you will visualize neural networks with an emphasis on classification tasks.

Career center

Learners who complete Deep Learning for Computer Vision will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

As a Computer Vision Engineer, you develop systems that enable machines to interpret and understand visual data from the world. This course provides comprehensive training in leveraging deep learning to transform visual data into actionable insights, which is central to this role. You will gain expertise in foundational neural networks like multi-layer perceptrons, and then dive into advanced architectures such as convolutional neural networks, essential for tasks like object detection and recognition. The exploration of cutting-edge models like ResNet and U-Net, including U-Net's direct application in precise image segmentation, equips you with the skills to build and deploy sophisticated deep learning models for real-world vision tasks. This hands-on experience, covering everything from understanding core building blocks to mastering advanced techniques, is precisely what is needed to excel in this specialized engineering field.

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Deep Learning Engineer

A Deep Learning Engineer specializes in designing, developing, and deploying deep neural network models for various applications. This course is exceptionally tailored for aspiring Deep Learning Engineers, guiding learners through the foundational and advanced techniques that drive modern deep learning solutions. You will master the building blocks of deep learning, including multi-layer perceptrons and normalization techniques, followed by advanced concepts like autoencoders and generative adversarial networks for synthetic data creation. Crucially, the course delves into convolutional neural networks, demonstrating how they extract features for classification and object recognition. The exploration of ResNet for deeper network training and U-Net for efficient image segmentation directly equips you to build and deploy sophisticated deep learning models, providing the hands-on experience vital for a successful career in this dynamic field.

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Machine Learning Engineer

As a Machine Learning Engineer, you focus on designing, building, and deploying machine learning models into production systems. This course offers highly relevant skills for a Machine Learning Engineer, especially one working with visual data. It provides a robust foundation in deep learning, a critical subdomain of machine learning, emphasizing practical application to computer vision problems. You will learn about core neural network principles, multi-layer perceptrons, and normalization, alongside advanced architectures such as convolutional neural networks for tasks like image classification and object detection. The course's hands-on approach to building and deploying models, coupled with an understanding of generative adversarial networks and architectures like ResNet and U-Net, prepares you to develop impactful, real-world machine learning solutions that require advanced visual data processing.

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Generative Artificial Intelligence Specialist

A Generative Artificial Intelligence Specialist focuses on creating AI models that can produce new, realistic data, often images, text, or audio. This course offers specific and highly valuable training for a Generative Artificial Intelligence Specialist by dedicating a module to autoencoders and generative adversarial networks. You will learn how GANs operate with competing generator and discriminator networks to create realistic synthetic data from noise, understanding adversarial training and loss functions. The emphasis on hands-on experience with these powerful architectures enables you to implement and evaluate models for representation learning and data generation. Mastering these techniques is fundamental for anyone aiming to innovate in the rapidly expanding field of generative AI, providing a distinct advantage in developing cutting-edge creative and analytical tools.

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Image Recognition Specialist

An Image Recognition Specialist designs and implements systems that can automatically identify and classify objects, patterns, and features within images. This course is directly aligned with the core competencies required for an Image Recognition Specialist, offering comprehensive training in deep learning techniques essential for this field. You will explore how multi-layer perceptrons and convolutional neural networks are used to extract spatial hierarchies and patterns from images for tasks like object detection and recognition. The course's hands-on approach to understanding neural networks, combined with learning cutting-edge architectures like ResNet for deeper networks, provides the practical skills necessary to build and deploy advanced models capable of precise image analysis and classification in real-world applications.

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Artificial Intelligence Engineer

As an Artificial Intelligence Engineer, you design, develop, and implement AI systems across various domains. This course offers significant value for an Artificial Intelligence Engineer, especially those focused on visual data processing and perception. It provides a deep dive into deep learning, a cornerstone of modern AI, with a particular emphasis on computer vision applications. You will learn how neural networks, convolutional neural networks, and advanced architectures like ResNet and U-Net are used to transform visual data into actionable insights for tasks such as image classification, object detection, and segmentation. The course's practical orientation, including building and deploying models, equips you with the essential skills to contribute to and lead AI initiatives that rely on sophisticated visual intelligence.

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Research Scientist Computer Vision

A Research Scientist Computer Vision investigates and develops novel algorithms and models to advance the state of the art in machine perception. This role typically requires an advanced degree, such as a master's or PhD. This course provides an excellent foundational and advanced understanding for a Research Scientist Computer Vision by exploring cutting-edge deep learning architectures. You will delve into the theoretical underpinnings and practical implementation of neural networks, multi-layer perceptrons, convolutional neural networks, and crucial architectures like ResNet and U-Net. Understanding how ResNet addresses challenges in deep networks and how U-Net achieves precise image segmentation provides insights into current research frontiers. The course's blend of theoretical understanding and hands-on experience can help build a strong base for conducting innovative research in the field.

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Medical Imaging Engineer

A Medical Imaging Engineer develops and applies technologies for capturing, processing, and analyzing medical images to aid diagnosis and treatment. This role often benefits from an advanced degree. This course is particularly relevant for a Medical Imaging Engineer due to its focus on advanced deep learning techniques, especially image segmentation. The module on U-Net directly addresses its power for precise image segmentation, a critical application in medical imaging and beyond. By learning encoder-decoder structures, skip connections, and upsampling techniques like transposed convolution, you gain specific skills to develop models that can accurately delineate structures within medical scans. This specialized knowledge, combined with a broader understanding of deep learning, can help drive innovation in healthcare technology.

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Data Scientist Machine Learning Specialist

A Data Scientist Machine Learning Specialist applies advanced analytical techniques and machine learning models to extract insights and build predictive solutions from complex datasets. This course offers valuable expertise for a Data Scientist Machine Learning Specialist by equipping you with deep learning skills essential for visual data. You will learn to transform visual data into actionable insights using foundational neural networks and advanced models like convolutional neural networks for classification and object detection. The course also covers generative adversarial networks for synthetic data and advanced architectures such as ResNet and U-Net. This comprehensive training helps you build and deploy deep learning models for real-world vision tasks, expanding your toolkit for tackling diverse data science challenges, particularly those involving image and video data.

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Embedded Vision Engineer

An Embedded Vision Engineer integrates computer vision capabilities into specialized hardware and resource-constrained devices for real-time applications. This course offers relevant foundational knowledge for an Embedded Vision Engineer by focusing on the underlying mechanisms of deep learning algorithms and efficient architectures. Understanding how multi-layer perceptrons and convolutional neural networks operate, along with the specifics of ResNet and U-Net, is crucial for optimizing models for performance on edge devices. The practical examples and guided learning in building and deploying deep learning models contribute to developing the skills needed to implement intelligent vision systems in embedded environments, where computational efficiency and precise visual insight are paramount for effective real-world operation.

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Autonomous Vehicle Systems Engineer

An Autonomous Vehicle Systems Engineer designs and develops the complex software and hardware that enable self-driving vehicles to perceive their environment, navigate, and make decisions. This course is highly beneficial for an Autonomous Vehicle Systems Engineer, as computer vision is a cornerstone of autonomous navigation. You will gain expertise in using deep learning to transform visual data into actionable insights, crucial for tasks such as object detection, recognition, and precise image segmentation within a vehicle's surroundings. The course's focus on convolutional neural networks, ResNet, and U-Net equips you with the ability to build and deploy robust vision models that perceive roads, pedestrians, and obstacles, directly contributing to the safety and functionality of autonomous systems.

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Robotics Software Engineer

A Robotics Software Engineer develops the software that controls robots, including their perception, navigation, and interaction with the environment. This course provides strong foundational knowledge for a Robotics Software Engineer, particularly in the realm of robot perception. Robots heavily rely on computer vision to understand their surroundings, identify objects, and navigate safely. Through this course, you will learn to leverage deep learning techniques, including convolutional neural networks for object detection and recognition, and U-Net for precise image segmentation, which is critical for a robot's interaction with complex environments. The ability to build and deploy deep learning models for real-world vision tasks can help you create more intelligent and autonomous robotic systems.

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Product Manager (Machine Learning)

A Product Manager Machine Learning defines the strategy, roadmap, and features for products that leverage machine learning technologies. This course is valuable for a Product Manager Machine Learning, providing a deep understanding of the technical capabilities and limitations of deep learning for computer vision. While not directly building models, understanding foundational neural networks, convolutional neural networks, generative adversarial networks, and advanced architectures like ResNet and U-Net allows you to make informed product decisions. You will learn what is feasible in terms of image classification, object detection, and generative modeling. This knowledge helps you effectively communicate with engineering teams, identify innovative product opportunities in vision-based AI, and guide the development of cutting-edge machine learning products.

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Solutions Architect Artificial Intelligence

A Solutions Architect Artificial Intelligence designs and oversees the architectural implementation of AI systems and solutions within an organization. This course provides valuable insights for a Solutions Architect Artificial Intelligence by offering a comprehensive understanding of deep learning for computer vision. Architects need to grasp the foundational and advanced techniques, including multi-layer perceptrons, convolutional neural networks, generative adversarial networks, ResNet, and U-Net, to design robust and scalable AI systems. Understanding how these models extract insights from visual data for tasks like classification, detection, and segmentation enables you to select appropriate technologies, anticipate challenges, and effectively guide technical teams in building and deploying impactful AI solutions that leverage computer vision capabilities.

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Computer Graphics Developer

A Computer Graphics Developer creates software and tools for generating visual content, including animations, simulations, and interactive experiences. This course may be helpful for a Computer Graphics Developer, particularly in areas where deep learning is revolutionizing content creation and visual enhancement. The module on autoencoders and generative adversarial networks (GANs) is especially relevant, as GANs can create realistic images from noise and model complex data distributions. This allows for advancements in synthesizing textures, generating realistic environments, or augmenting existing visual assets. Understanding how deep learning models transform visual data and create new content can help a Computer Graphics Developer explore innovative approaches to procedural generation and artistic design within their field.

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