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Matt Rich,
Megan Thompson,
Amanda Wang,
Brandon Armstrong,
and
Mehdi Alemi
Starting with zero deep learning knowledge, this foundational course will guide you to effectively train cutting-edge models for image classification purposes. From analyzing medical images to recognizing traffic signs, classification is important for many applications. Classification models also serve as the backbone for more complicated object detection models. Through hands-on projects, you will train and evaluate models to classify street signs and identify the letters of American Sign Language. By completing this course, you will develop a strong foundation in deep learning for image analysis and will be equipped with the skills to tackle real-world computer vision challenges.
Read more
Starting with zero deep learning knowledge, this foundational course will guide you to effectively train cutting-edge models for image classification purposes. From analyzing medical images to recognizing traffic signs, classification is important for many applications. Classification models also serve as the backbone for more complicated object detection models. Through hands-on projects, you will train and evaluate models to classify street signs and identify the letters of American Sign Language. By completing this course, you will develop a strong foundation in deep learning for image analysis and will be equipped with the skills to tackle real-world computer vision challenges.
Read more
Starting with zero deep learning knowledge, this foundational course will guide you to effectively train cutting-edge models for image classification purposes. From analyzing medical images to recognizing traffic signs, classification is important for many applications. Classification models also serve as the backbone for more complicated object detection models. Through hands-on projects, you will train and evaluate models to classify street signs and identify the letters of American Sign Language. By completing this course, you will develop a strong foundation in deep learning for image analysis and will be equipped with the skills to tackle real-world computer vision challenges.
By the end of this course, you will be able to:
• Explain how deep learning networks find image features and make predictions
• Retrain common models like GoogLeNet and ResNet for specific applications
• Investigate model behavior to identify errors and determine potential fixes
• Improve model performance by tuning hyperparameters
• Complete the entire deep learning workflow in a final project
For the duration of the course, you will have free access to MATLAB, software used by top employers worldwide. The courses draw on the applications using MATLAB, so you spend less time coding and more time applying deep learning concepts.
Register for this course and see more details by visiting:
OpenCourser.com/course/c8ppkr/introduction
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What's inside
Syllabus
Introduction to Deep Learning with Images
Learn the key components of convolutional neural networks and train a simple classification model
Transfer Learning
Read more
Syllabus
Introduction to Deep Learning with Images
Learn the key components of convolutional neural networks and train a simple classification model
Transfer Learning
Read more
Retraining networks with new data is the most common way to apply deep learning in industry. In this module, you'll retrain common networks, set appropriate values for training options, and compare results from different models.
Investigating Network Behavior
Explaining how models make predictions is increasingly important. In this module, you'll use confidence scores and visualizations to determine what regions of an image the model is using to make predictions. You'll also identify common errors and adjust training options to improve performance.
Final Project: Classifying the ASL Alphabet
Apply your new skills to a final project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops foundational deep learning skills in image classification, preparing learners for real-world computer vision challenges
Builds a comprehensive understanding of deep learning networks for image analysis
Provides hands-on projects to apply deep learning concepts to real-world applications
Taught by recognized instructors in the field of deep learning and image analysis
Leverages free access to MATLAB, a widely used software in industry, reducing coding time and maximizing application focus
Course materials include a mix of videos, readings, and discussions, enhancing learning engagement
Save this course
Save Introduction to Deep Learning for Computer Vision to your list so you can find it easily later:
Save
Save
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 Introduction to Deep Learning for Computer Vision with these
activities:
Review Linear Algebra and Calculus Concepts
Show steps
Strengthen your foundational understanding of mathematical concepts crucial for image classification.
Browse courses on
Linear Algebra
Show steps
-
Revisit key concepts in linear algebra and calculus relevant to image processing and analysis.
-
Solve practice problems or review online tutorials to refresh your skills.
Read 'Deep Learning for Computer Vision' by Adrian Rosebrock
Show steps
Gain a comprehensive foundation in image classification concepts and techniques.
View
Melania
on Amazon
Show steps
-
Thoroughly read the book, taking notes and highlighting key concepts.
-
Complete the exercises and examples provided in the book to reinforce your understanding.
Organize and Review Course Materials Regularly
Show steps
Enhance your learning by organizing and actively reviewing course materials.
Show steps
-
Create a system for organizing notes, assignments, quizzes, and exams.
-
Review your notes regularly to reinforce concepts and identify areas for improvement.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Image Classification Video Tutorials
Show steps
Seek additional guidance and clarify concepts through interactive video tutorials.
Browse courses on
Image Classification
Show steps
-
Identify reputable online platforms or instructors offering image classification video tutorials.
-
Follow along with the tutorials, taking notes and experimenting with the provided code examples.
Attend a Local Machine Learning Meetup
Show steps
Connect with professionals in the field and gain insights on real-world applications of image classification.
Browse courses on
Image Classification
Show steps
-
Research and identify local machine learning meetups or conferences.
-
Attend the event and actively participate in discussions and networking opportunities.
Complete Image Classification Practice Problems
Show steps
Engage in repetitive practice exercises to solidify your understanding of image classification principles and algorithms.
Browse courses on
Image Classification
Show steps
-
Solve a series of image classification practice problems on a platform like LeetCode or Kaggle.
-
Analyze the provided datasets and identify key image features for classification tasks.
-
Implement various image classification algorithms to train and evaluate models based on the extracted features.
-
Experiment with different hyperparameters and optimization techniques to enhance model performance.
Create a Visual Guide to Image Classification Techniques
Show steps
Solidify your understanding by creating a visual representation of key image classification techniques.
Browse courses on
Image Classification
Show steps
-
Research and gather information on various image classification techniques and algorithms.
-
Design a visual guide using diagrams, charts, and examples to illustrate the concepts clearly.
-
Present your visual guide to classmates or share it online to receive feedback and refine your understanding.
Build an Image Classification Model for a Specific Use Case
Show steps
Apply your knowledge by developing an image classification model tailored to a practical real-world scenario.
Browse courses on
Image Classification
Show steps
-
Identify a specific image classification problem or use case that interests you or aligns with your career goals.
-
Gather and prepare a dataset relevant to your chosen use case.
-
Select an appropriate deep learning framework and model architecture for your task.
-
Train and evaluate your model using the prepared dataset.
-
Deploy and test your model in a practical setting to assess its performance.
Review Linear Algebra and Calculus Concepts
Show steps
Strengthen your foundational understanding of mathematical concepts crucial for image classification.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit key concepts in linear algebra and calculus relevant to image processing and analysis.
- Solve practice problems or review online tutorials to refresh your skills.
Read 'Deep Learning for Computer Vision' by Adrian Rosebrock
Show steps
Gain a comprehensive foundation in image classification concepts and techniques.
View
Melania
on Amazon
Show steps
Show steps
Show steps
- Thoroughly read the book, taking notes and highlighting key concepts.
- Complete the exercises and examples provided in the book to reinforce your understanding.
Organize and Review Course Materials Regularly
Show steps
Enhance your learning by organizing and actively reviewing course materials.
Show steps
Show steps
Show steps
- Create a system for organizing notes, assignments, quizzes, and exams.
- Review your notes regularly to reinforce concepts and identify areas for improvement.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Image Classification Video Tutorials
Show steps
Seek additional guidance and clarify concepts through interactive video tutorials.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Identify reputable online platforms or instructors offering image classification video tutorials.
- Follow along with the tutorials, taking notes and experimenting with the provided code examples.
Attend a Local Machine Learning Meetup
Show steps
Connect with professionals in the field and gain insights on real-world applications of image classification.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Research and identify local machine learning meetups or conferences.
- Attend the event and actively participate in discussions and networking opportunities.
Complete Image Classification Practice Problems
Show steps
Engage in repetitive practice exercises to solidify your understanding of image classification principles and algorithms.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Solve a series of image classification practice problems on a platform like LeetCode or Kaggle.
- Analyze the provided datasets and identify key image features for classification tasks.
- Implement various image classification algorithms to train and evaluate models based on the extracted features.
- Experiment with different hyperparameters and optimization techniques to enhance model performance.
Create a Visual Guide to Image Classification Techniques
Show steps
Solidify your understanding by creating a visual representation of key image classification techniques.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Research and gather information on various image classification techniques and algorithms.
- Design a visual guide using diagrams, charts, and examples to illustrate the concepts clearly.
- Present your visual guide to classmates or share it online to receive feedback and refine your understanding.
Build an Image Classification Model for a Specific Use Case
Show steps
Apply your knowledge by developing an image classification model tailored to a practical real-world scenario.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Identify a specific image classification problem or use case that interests you or aligns with your career goals.
- Gather and prepare a dataset relevant to your chosen use case.
- Select an appropriate deep learning framework and model architecture for your task.
- Train and evaluate your model using the prepared dataset.
- Deploy and test your model in a practical setting to assess its performance.
Career center
Learners who complete Introduction to Deep Learning for Computer Vision will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems, which are used to analyze images, videos, and other visual data. This course would be useful for Computer Vision Engineers because it provides a strong foundation in deep learning, which is a key technology for developing computer vision systems. The course also covers topics such as image classification, object detection, and segmentation, which are all important areas of computer vision research and development.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning systems, which are used to make predictions and decisions based on data. This course would be useful for Machine Learning Engineers because it provides a strong foundation in deep learning, which is a key technology for developing machine learning systems. The course also covers topics such as model selection, evaluation, and optimization, which are all important areas of machine learning research and development.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make decisions. This course would be useful for Data Scientists because it provides a strong foundation in deep learning, which is a key technology for analyzing data. The course also covers topics such as data visualization, feature engineering, and model selection, which are all important areas of data science research and development.
Software Engineer
Software Engineer
Software Engineers design and develop software applications. This course would be useful for Software Engineers because it provides a strong foundation in deep learning, which is a key technology for developing software applications. The course also covers topics such as software design, testing, and deployment, which are all important areas of software engineering research and development.
Research Scientist
Research Scientist
Research Scientists conduct research in various fields of science and engineering. This course would be useful for Research Scientists because it provides a strong foundation in deep learning, which is a key technology for conducting research in many fields of science and engineering. The course also covers topics such as research methods, data analysis, and scientific writing, which are all important areas of research.
Data Analyst
Data Analyst
Data Analysts analyze data to identify trends and patterns. This course would be useful for Data Analysts because it provides a strong foundation in deep learning, which is a key technology for analyzing data. The course also covers topics such as data visualization, statistical analysis, and data mining, which are all important areas of data analysis research and development.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. This course would be useful for Business Analysts because it provides a strong foundation in deep learning, which is a key technology for analyzing data. The course also covers topics such as business intelligence, data visualization, and decision making, which are all important areas of business analysis research and development.
Product Manager
Product Manager
Product Managers develop and manage products. This course would be useful for Product Managers because it provides a strong foundation in deep learning, which is a key technology for developing products. The course also covers topics such as product design, development, and marketing, which are all important areas of product management research and development.
Project Manager
Project Manager
Project Managers plan and execute projects. This course would be useful for Project Managers because it provides a strong foundation in deep learning, which is a key technology for managing projects. The course also covers topics such as project planning, execution, and control, which are all important areas of project management research and development.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. This course would be useful for Technical Writers because it provides a strong foundation in deep learning, which is a key technology for creating and maintaining technical documentation. The course also covers topics such as technical writing, editing, and publishing, which are all important areas of technical writing research and development.
Technical Support Specialist
Technical Support Specialist
Technical Support Specialists provide technical support to users. This course may be useful for Technical Support Specialists because it provides a strong foundation in deep learning, which is a key technology for providing technical support. The course also covers topics such as troubleshooting, problem-solving, and communication, which are all important areas of technical support research and development.
Sales Engineer
Sales Engineer
Sales Engineers sell technical products and services. This course may be useful for Sales Engineers because it provides a strong foundation in deep learning, which is a key technology for selling technical products and services. The course also covers topics such as sales, marketing, and negotiation, which are all important areas of sales engineering research and development.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns. This course may be useful for Marketing Managers because it provides a strong foundation in deep learning, which is a key technology for planning and executing marketing campaigns. The course also covers topics such as marketing strategy, research, and analytics, which are all important areas of marketing management research and development.
Human Resources Manager
Human Resources Manager
Human Resources Managers manage human resources for organizations. This course may be useful for Human Resources Managers because it provides a strong foundation in deep learning, which is a key technology for managing human resources. The course also covers topics such as human resources management, employee relations, and organizational development, which are all important areas of human resources management research and development.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. This course may be useful for Financial Analysts because it provides a strong foundation in deep learning, which is a key technology for analyzing financial data. The course also covers topics such as financial analysis, modeling, and valuation, which are all important areas of financial analysis research and development.
For more career information including salaries, visit:
OpenCourser.com/course/c8ppkr/introduction
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Deep Learning for Computer Vision.
Provides a comprehensive overview of deep learning for computer vision, covering topics such as convolutional neural networks, transfer learning, and object detection. It valuable resource for anyone who wants to learn more about this field.
Save
Provides a comprehensive overview of computer vision algorithms, including topics such as image processing, feature extraction, and object recognition. It valuable resource for anyone who wants to learn more about the fundamentals of computer vision.
Provides a gentle introduction to deep learning using Python. It valuable resource for anyone who wants to get started with deep learning.
Provides a comprehensive overview of pattern recognition and machine learning, including topics such as statistical pattern recognition, neural networks, and support vector machines. It valuable resource for anyone who wants to learn more about the fundamentals of pattern recognition and machine learning.
Provides a comprehensive overview of statistical learning, including topics such as linear regression, logistic regression, and support vector machines. It valuable resource for anyone who wants to learn more about the fundamentals of statistical learning.
Save
Provides a comprehensive overview of deep learning, including topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone who wants to learn more about the fundamentals of deep learning.
Provides a comprehensive overview of computer vision, including topics such as image processing, feature extraction, and object recognition. It valuable resource for anyone who wants to learn more about the fundamentals of computer vision.
Provides a comprehensive overview of pattern classification, including topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about the fundamentals of pattern classification.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for anyone who wants to learn more about the fundamentals of machine learning.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It valuable resource for anyone who wants to learn more about the fundamentals of Bayesian reasoning and machine learning.
Provides a comprehensive overview of probabilistic graphical models. It valuable resource for anyone who wants to learn more about the fundamentals of probabilistic graphical models.
Provides a comprehensive overview of reinforcement learning. It valuable resource for anyone who wants to learn more about the fundamentals of reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c8ppkr/introduction
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Level
Intermediate
Via
Coursera
Institution
MathWorks
Instructors
Matt Rich
Megan Thompson
Amanda Wang
Brandon Armstrong
Mehdi Alemi
Language
English
This course belongs to a
collection
called
Deep Learning for Computer Vision. It's comprised of three courses:
- Introduction to Deep Learning for Computer Vision (viewing)
- Deep Learning for Object Detection
- Advanced Deep Learning Techniques for Computer Vision
Good to know
Develops foundational deep learning skills in image classification, preparing learners for real-world computer vision challenges
Builds a comprehensive understanding of deep learning networks for image analysis
Provides hands-on projects to apply deep learning concepts to real-world applications
Taught by recognized instructors in the field of deep learning and image analysis
Leverages free access to MATLAB, a widely used software in industry, reducing coding time and maximizing application focus
Course materials include a mix of videos, readings, and discussions, enhancing learning engagement
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