Deep Learning Applications for Computer Vision from Coursera

In this course, you’ll be learning about Computer Vision as a field of study and research. First we’ll be exploring several Computer Vision tasks and suggested approaches, from the classic Computer Vision perspective. Then we’ll introduce Deep Learning methods and apply them to some of the same problems. We will analyze the results and discuss advantages and drawbacks of both types of methods. We'll use tutorials to let you explore hands-on some of the modern machine learning tools and software libraries. Examples of Computer Vision tasks where Deep Learning can be applied include: image classification, image classification with localization, object detection, object segmentation, facial recognition, and activity or pose estimation.

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

Introduction and Background

In this module, you will learn about the field of Computer Vision. Computer Vision has the goal of extracting information from images. We will go over the major categories of tasks of Computer Vision and we will give examples of applications from each category. With the adoption of Machine Learning and Deep Learning techniques, we will look at how this has impacted the field of Computer Vision.

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Read about what's good

what should give you pause

and possible dealbreakers

Taught by Ioana Fleming, who has industry experience in AI

Starts with classic computer tools and image classification to lay a foundation first

Uses Tensorflow, an industry-leading tool

Recommended for those with some experience in computer science

Reviews summary

Practical deep learning for computer vision

According to students, this course provides a strong foundation in deep learning applications for computer vision, effectively bridging classic computer vision techniques with modern deep learning methods. Learners particularly appreciate the hands-on experience through TensorFlow tutorials and practical assignments, which are often described as well-designed and invaluable for real-world application. While the course is generally well-structured with clear explanations, some learners suggest a prior comfort with Python and TensorFlow might be beneficial to avoid finding certain code examples challenging to debug or some content feeling slightly rushed at times.

Pacing is generally good, but some wish for more depth.

"My only minor critique is that some parts felt a bit rushed towards the end, and I wished for more advanced project ideas."

"I would have preferred more challenging assignments or mini-projects to really consolidate the learning."

"While the content is relevant, I felt some of the explanations for hyperparameters could have gone deeper."

Course shows signs of continuous improvement and relevance.

"This course was incredibly insightful and practical! The focus on applications like object detection makes it useful for my work."

"Very relevant content for today's AI landscape."

"Older reviews mentioned outdated content, but recent feedback suggests improvements, as the course now feels current."

Covers essential concepts from classic CV to DL.

"The transition from classic CV to deep learning was well-paced, and the content is comprehensive."

"It effectively bridges the gap between traditional computer vision and modern deep learning."

"This course provides a strong theoretical foundation before diving into practical deep learning."

Instructors explain complex topics clearly and effectively.

"I appreciated how the instructor explained complex concepts clearly."

"The instructor's explanations were spot on, and the progression from basic neural networks to more complex architectures was seamless."

"This course provided a strong theoretical foundation before diving into practical deep learning, with very clear explanations."

Focuses on real-world applicability with hands-on tools.

"The TensorFlow tutorials were hands-on and extremely helpful. I appreciated how the instructor explained complex concepts clearly."

"The hands-on coding and projects are the strongest part of the course for me, genuinely preparing me for real-world applications."

"I loved the practical assignments that truly tested my understanding, making the learning through doing very effective."

Some find code examples challenging or buggy.

"The code examples provided were sometimes buggy or hard to follow without significant external research."

"I struggled a bit with debugging the code examples. Be prepared to supplement with external resources."

"Debugging the provided code was also a pain at times, making some modules less clear."

May require prior comfort with Python/ML tools.

"The deep learning parts, especially the TensorFlow tutorials, sometimes assumed too much prior knowledge."

"I struggled a bit with debugging the code examples; prerequisites should be stated more clearly."

"The explanations often lacked the depth required for someone without a strong prior background in ML."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deep Learning Applications for Computer Vision with these activities:

Review class materials for a comprehensive overview

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Build a strong foundation by organizing and reviewing key concepts from the course materials before starting the course.

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Gather lecture notes, slides, and assignments
Create a study plan or outline
Review the materials regularly and make notes

Brush up on linear algebra and calculus

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Strengthen your mathematical foundation by refreshing your knowledge of linear algebra and calculus, which are essential for understanding computer vision algorithms.

Browse courses on Linear Algebra

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Review textbooks or online resources on linear algebra and calculus
Solve practice problems and exercises
Take an online course or workshop

Learn about image pre-processing in Python

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Increase your proficiency in image pre-processing techniques to enhance your skills in computer vision.

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Read this tutorial
Follow the steps provided in the tutorial
Experiment with different image pre-processing techniques

Four other activities

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Discuss the challenges of object detection in computer vision

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Enhance your understanding of object detection and engage in critical thinking by exchanging ideas with your peers.

Browse courses on Object Detection

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Join a peer study group or online forum
Prepare talking points on the challenges of object detection
Participate in the discussion and share your perspectives

Explore open-source computer vision libraries

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Expand your knowledge of tools and resources by investigating open-source computer vision libraries that can enhance your projects.

Browse courses on Computer Vision

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Research popular open-source computer vision libraries
Select a library and explore its documentation
Follow tutorials or examples to get started with the library

Solve image classification problems on Kaggle

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Strengthen your problem-solving skills and deepen your understanding of image classification algorithms by working on real-world datasets.

Browse courses on Image Classification

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Create an account on Kaggle
Select an image classification competition
Download the dataset and familiarize yourself with it
Develop and train your image classification model
Submit your results and compare them with others

Build a facial recognition system

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Develop hands-on experience by creating a functional facial recognition system using your understanding of computer vision techniques.

Browse courses on Facial Recognition

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Gather a dataset of facial images
Preprocess the images
Extract features from the images
Train a classifier
Evaluate the performance of the classifier

Career center

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

Computer Vision Scientist

Computer Vision Scientists research and develop new computer vision algorithms and techniques. They use their knowledge of computer vision, machine learning, and artificial intelligence to create new ways to see and understand the world. This course "Deep Learning Applications for Computer Vision" provides a solid foundation in Deep Learning which Computer Vision Scientists can apply to their research in the field.

See salaries and explore the career path for Computer Vision Scientist

Medical Imaging Analyst

Medical Imaging Analysts use their knowledge of computer vision, image processing, and medical imaging to analyze medical images. They work with doctors and other medical professionals to help diagnose and treat diseases. This course "Deep Learning Applications for Computer Vision" provides a solid foundation in Deep Learning which Medical Imaging Analysts can apply to their work in the field.

See salaries and explore the career path for Medical Imaging Analyst

Machine Perception Engineer

Machine Perception Engineers design and develop machine perception systems. They use their knowledge of computer vision, machine learning, and artificial intelligence to create systems that can see and understand the world. This course "Deep Learning Applications for Computer Vision" provides a solid foundation in Deep Learning which Machine Perception Engineers can apply to their work in the field.

See salaries and explore the career path for Machine Perception Engineer

Image Processing Engineer

Image Processing Engineers design and develop image processing algorithms and techniques. They use their knowledge of image processing, computer vision, and signal processing to create new ways to process and analyze images. This course "Deep Learning Applications for Computer Vision" provides a solid foundation in Deep Learning which Image Processing Engineers can apply to their work in the field.

See salaries and explore the career path for Image Processing Engineer

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. They use their knowledge of machine learning algorithms, software, and hardware to build models that can learn from data and make predictions. This course "Deep Learning Applications for Computer Vision" provides a solid foundation in Deep Learning which Machine Learning Engineers can apply to develop models for Computer Vision tasks.

See salaries and explore the career path for Machine Learning Engineer

Software Developer

Software Developers design, develop, and maintain software applications. They use their knowledge of programming languages, software development tools, and software engineering principles to create software that meets the needs of users. This course "Deep Learning Applications for Computer Vision" provides valuable knowledge in Deep Learning which Software Developers can apply to develop software applications that use Computer Vision.

See salaries and explore the career path for Software Developer

Robotics Engineer

Robotics Engineers design, build, and maintain robots. They use their knowledge of mechanical engineering, electrical engineering, and computer science to create robots that can perform a variety of tasks. This course "Deep Learning Applications for Computer Vision" provides valuable knowledge in Deep Learning which Robotics Engineers can apply to develop robots that can see and interact with their environment.

See salaries and explore the career path for Robotics Engineer

Systems Analyst

Systems Analysts design, develop, and implement computer systems. They use their knowledge of systems analysis and design techniques to create systems that meet the needs of users. This course "Deep Learning Applications for Computer Vision" provides valuable knowledge in Deep Learning which Systems Analysts can apply to develop systems that use Computer Vision.

See salaries and explore the career path for Systems Analyst

User Experience Designer

User Experience Designers design and develop user interfaces for software applications and websites. They use their knowledge of human-computer interaction and design principles to create interfaces that are easy to use and visually appealing. This course "Deep Learning Applications for Computer Vision" provides valuable knowledge in Deep Learning which User Experience Designers can apply to develop user interfaces that use Computer Vision.

See salaries and explore the career path for User Experience Designer

Web Developer

Web Developers design and develop websites. They use their knowledge of web development languages, tools, and techniques to create websites that meet the needs of users. This course "Deep Learning Applications for Computer Vision" provides valuable knowledge in Deep Learning which Web Developers can apply to develop websites that use Computer Vision.

See salaries and explore the career path for Web Developer

Computer Scientist

Computer Scientists research and develop theoretical foundations for computing, software applications, algorithms, and hardware. They use their understanding of the principles of computation to solve problems in various fields. This course "Deep Learning Applications for Computer Vision" can provide foundational knowledge in Deep Learning that would be helpful for Computer Scientists interested in furthering their understanding of Computer Vision.

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Research Scientist

Research Scientists conduct research in a variety of fields, including computer science, engineering, and medicine. They use their knowledge of scientific methods and research techniques to investigate new problems and develop new solutions. This course "Deep Learning Applications for Computer Vision" may be useful for a Research Scientist to develop a deeper understanding of the principles of Deep Learning which they can then apply to their research in Computer Vision.

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Computer Vision Engineer

Computer Vision Engineers use their knowledge of computer algorithms and software to solve engineering design problems related to computer vision. Their work can be found in a wide variety of industries and help power applications like medical imaging, facial recognition software, robotics, and more. This course "Deep Learning Applications for Computer Vision" may be useful for a Computer Vision Engineer to develop a deeper understanding of the principles of Deep Learning which they can then apply to their work in Computer Vision.

See salaries and explore the career path for Computer Vision Engineer

Data Scientist

Data Scientists use their knowledge of statistics, data mining, and machine learning to extract insights from data. They work in a variety of industries, helping businesses make better decisions by providing them with data-driven insights. This course "Deep Learning Applications for Computer Vision" may be useful for a Data Scientist to develop a deeper understanding of the principles of Deep Learning which they can then apply to image-related data.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts collect, clean, analyze, and interpret large datasets to help businesses make informed decisions. They use their knowledge of statistics, data mining, and programming to uncover trends and patterns in data. This course "Deep Learning Applications for Computer Vision" provides valuable knowledge in Deep Learning which Data Analysts can apply to data related to images.

See salaries and explore the career path for Data Analyst