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Andrew Ng,
Kian Katanforoosh,
and
Younes Bensouda Mourri
In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
Read more
In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
Read more
In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
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What's inside
Syllabus
Foundations of Convolutional Neural Networks
Implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.
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Syllabus
Foundations of Convolutional Neural Networks
Implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Appropriate for learners with a background in computer vision
Builds on foundational computer vision knowledge and strengthens intermediate learners' existing foundation
Covers object detection, face recognition, and neural style transfer, offering practical applications and industry-relevant skills
Examines recent advancements and case studies in deep CNNs, exposing learners to innovative techniques
Offers opportunities for hands-on implementation of CNNs, providing practical experience
Taught by recognized instructors in the field of computer vision, Andrew Ng and Younes Bensouda Mourri
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Reviews summary
Convolutional neural networks fundamentals and applications
According to learners, this course provides a solid foundation in the principles of Convolutional Neural Networks (CNNs) and their applications. Many found the explanations clear and intuitive, particularly regarding core concepts. A significant strength highlighted by students are the practical programming assignments and hands-on labs, which are described as highly beneficial for solidifying understanding and applying the learned material. While the material covers various exciting applications like object detection, face recognition, and neural style transfer, some learners noted that certain sections or assignments could be challenging, requiring a strong grasp of prerequisites or extra effort. Overall, the course is viewed as an excellent resource within the Deep Learning Specialization, preparing students for real-world applications.
Explores exciting real-world CNN uses.
"I found the modules on object detection and face recognition particularly interesting and relevant."
"Applying CNNs to neural style transfer was a fun and insightful project."
"The course provides a great overview of how CNNs are used in current computer vision tasks."
"Learning about these applications motivates you to delve deeper into the subject."
Provides a strong base for future learning.
"This course gave me an excellent foundation for understanding more advanced topics in computer vision."
"I feel well-prepared to tackle other deep learning projects involving image data now."
"It's a crucial building block within the Deep Learning Specialization."
"Provided me with the core concepts needed to start working with CNNs professionally."
Concepts are explained clearly and build intuition.
"The explanations for how convolutions and pooling layers work were very clear and easy to follow."
"This course really helped me build a strong intuition about how CNNs function internally."
"Complex topics were broken down into digestible parts, making them easier to understand."
"I appreciated the visual explanations provided in the lectures; they made abstract ideas concrete."
Hands-on coding exercises solidify learning.
"The programming assignments were the highlight for me; they really cemented my understanding of the theory."
"I loved implementing concepts like neural style transfer and object detection in the labs."
"Assignments are challenging but are incredibly practical and give you real coding experience."
"Debugging the assignments was a great learning experience in itself."
Requires effort, especially without prerequisites.
"Some parts of the math and theory were quite difficult; prior knowledge helps a lot."
"The assignments can be time-consuming and require significant debugging."
"I had to revisit some lectures multiple times to fully grasp the concepts before attempting assignments."
"This isn't a 'walk in the park' course; be prepared to invest serious time and effort."
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 Convolutional Neural Networks with these
activities:
Review the book 'Deep Learning with Python'
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Gain a deeper understanding of the concepts covered in the course by reading a book on the subject.
View
Deep learning v jazyku Python - 2., rozšířené...
on Amazon
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-
Purchase or borrow the book.
-
Read the book and take notes on the key concepts.
-
Complete the exercises and assignments in the book.
Watch a tutorial on convolutional neural networks
Show steps
Get a quick overview of the basics of CNNs before diving into the course material.
Browse courses on
Convolutional Neural Networks
Show steps
-
Find a tutorial on YouTube or another online platform.
-
Watch the tutorial and take notes on the key concepts.
Implement a simple CNN in Python
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Solidify your understanding of CNNs by implementing one from scratch.
Browse courses on
Python
Show steps
-
Set up a Python environment with the necessary libraries.
-
Create a dataset of images for training and testing.
-
Design and implement a CNN architecture.
-
Train and evaluate the CNN on the dataset.
One other activity
Expand to see all activities and additional details
Show all four activities
Write a blog post about a CNN project you worked on
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Showcase your knowledge and understanding of CNNs by creating a project and sharing it with others.
Show steps
-
Identify a problem or challenge that can be solved using a CNN.
-
Design and implement a CNN-based solution.
-
Write a blog post describing your project, the problem it solves, and the results you achieved.
Review the book 'Deep Learning with Python'
Show steps
Gain a deeper understanding of the concepts covered in the course by reading a book on the subject.
View
Deep learning v jazyku Python - 2., rozšířené...
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow the book.
- Read the book and take notes on the key concepts.
- Complete the exercises and assignments in the book.
Watch a tutorial on convolutional neural networks
Show steps
Get a quick overview of the basics of CNNs before diving into the course material.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Find a tutorial on YouTube or another online platform.
- Watch the tutorial and take notes on the key concepts.
Implement a simple CNN in Python
Show steps
Solidify your understanding of CNNs by implementing one from scratch.
Browse courses on
Python
Show steps
Show steps
Show steps
- Set up a Python environment with the necessary libraries.
- Create a dataset of images for training and testing.
- Design and implement a CNN architecture.
- Train and evaluate the CNN on the dataset.
One other activity
Expand to see all activities and additional details
Show all four activities
Write a blog post about a CNN project you worked on
Show steps
Showcase your knowledge and understanding of CNNs by creating a project and sharing it with others.
Show steps
Show steps
Show steps
- Identify a problem or challenge that can be solved using a CNN.
- Design and implement a CNN-based solution.
- Write a blog post describing your project, the problem it solves, and the results you achieved.
Career center
Learners who complete Convolutional Neural Networks will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
A Convolutional Neural Networks course provides a good foundation towards becoming a successful Computer Vision Engineer by providing the fundamental knowledge and tools needed to work with images and videos. Those who wish to enter the field of Computer Vision Engineering may find this course particularly relevant as it covers techniques commonly used in the field, such as object detection and face recognition.
Software Engineer
Software Engineer
The skills learned in a Convolutional Neural Networks course, such as building and training deep neural networks, can be applied by Software Engineers who work on developing and maintaining software for various applications. This course offers a solid foundation for Software Engineers who want to specialize in deep learning and computer vision.
Data Scientist
Data Scientist
A Convolutional Neural Networks course is highly relevant for those aspiring to become Data Scientists. It provides a comprehensive understanding of deep learning techniques specifically designed for analyzing and interpreting images and videos, which is becoming increasingly important in various domains, such as medical imaging, autonomous driving, and security.
Machine Learning Engineer
Machine Learning Engineer
A Convolutional Neural Networks course is beneficial for Machine Learning Engineers as it provides expertise in designing, developing, and implementing deep learning models for computer vision tasks. The course covers advanced techniques like transfer learning and object detection, equipping Machine Learning Engineers with specialized knowledge for working on complex image and video-related projects.
Research Scientist
Research Scientist
For those pursuing careers as Research Scientists in the field of computer vision or deep learning, a Convolutional Neural Networks course is highly recommended. It offers a deep dive into the latest developments and research advancements in this domain, providing a solid foundation for conducting cutting-edge research and contributing to the field.
Product Manager
Product Manager
Product Managers who work on developing computer vision-based products can benefit from a Convolutional Neural Networks course. It provides a technical understanding of the underlying algorithms and techniques, enabling them to make informed decisions, prioritize features, and effectively communicate with engineering teams.
Data Analyst
Data Analyst
A Convolutional Neural Networks course can be useful for Data Analysts who work with image or video data. It provides specialized knowledge in deep learning techniques tailored for analyzing and extracting insights from visual data, enhancing their ability to interpret and communicate data-driven insights.
Business Analyst
Business Analyst
While a Convolutional Neural Networks course is not directly related to the role of a Business Analyst, it may provide valuable knowledge for those involved in projects related to computer vision or image analysis. Understanding the capabilities and limitations of these technologies can aid in identifying business opportunities and developing effective strategies.
Project Manager
Project Manager
For Project Managers working on computer vision or deep learning projects, a Convolutional Neural Networks course can be helpful. It provides a technical foundation to understand the project's scope, timelines, and deliverables, enabling effective planning, resource allocation, and risk management.
Technical Writer
Technical Writer
Technical Writers who specialize in documenting deep learning or computer vision technologies can benefit from a Convolutional Neural Networks course. It provides in-depth knowledge of the subject matter, enabling them to create accurate, clear, and comprehensive technical documentation for various audiences.
Sales Engineer
Sales Engineer
Sales Engineers who work with clients in the computer vision or deep learning industry may find a Convolutional Neural Networks course beneficial. It provides technical expertise to understand the products and solutions being sold, enabling them to effectively communicate their value and benefits to potential customers.
IT Consultant
IT Consultant
IT Consultants who specialize in computer vision or deep learning can enhance their knowledge and skills by taking a Convolutional Neural Networks course. It provides a deep understanding of the latest advancements and best practices in the field, enabling them to provide expert advice and guidance to clients.
UX Designer
UX Designer
UX Designers who work on projects involving computer vision or image-based interfaces may find a Convolutional Neural Networks course helpful. It provides insights into the capabilities and limitations of these technologies, enabling them to design user experiences that leverage visual data effectively.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts who work with visual data or apply deep learning techniques to financial analysis may benefit from a Convolutional Neural Networks course. It provides specialized knowledge in image analysis and deep learning algorithms, enhancing their ability to develop and implement data-driven models.
Marketing Manager
Marketing Manager
Marketing Managers who work on campaigns or products related to computer vision or deep learning can gain insights from a Convolutional Neural Networks course. It provides an understanding of the technology's capabilities and potential applications, enabling them to develop effective marketing strategies and communicate the value of these technologies to target audiences.
For more career information including salaries, visit:
OpenCourser.com/course/y5fy6n/convolutional
Featured in The Course Notes
This course is mentioned in our blog,
The Course Notes. Read
one article that features
Convolutional Neural Networks:
To read more articles from OpenCourser, visit:
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Reading list
We've selected 14 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
Convolutional Neural Networks.
Save
Comprehensive resource on deep learning and provides a solid foundation for understanding the theoretical underpinnings of convolutional neural networks.
Save
Provides foundational background knowledge for the course and is widely used as a reference text in many academic settings.
Provides a comprehensive overview of face recognition. It covers topics such as feature extraction, classification, and recognition. It valuable resource for learners who want to gain a deeper understanding of the field.
Provides hands-on experience with implementing computer vision algorithms using Python. It covers a range of topics related to convolutional neural networks.
Provides a practical guide to deep learning with Python. It covers topics such as building and training neural networks, and deploying them in real-world applications. It valuable resource for learners who want to gain hands-on experience with deep learning.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers topics such as image formation, feature extraction, object recognition, and scene understanding. It valuable resource for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of deep learning for computer vision. It covers topics such as image classification, object detection, and semantic segmentation. It valuable resource for learners who want to gain a deeper understanding of the field.
Provides a practical introduction to deep learning using the Fastai library. It is suitable for beginners and those with limited programming experience.
Provides a comprehensive introduction to artificial intelligence, machine learning, and deep learning. It is suitable for beginners and those with limited programming experience.
Provides a comprehensive overview of pattern recognition and machine learning algorithms. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for learners who want to gain a deeper understanding of the field.
Provides a comprehensive guide to TensorFlow, a popular deep learning framework. It covers topics such as building and training neural networks, and deploying them in real-world applications. It valuable resource for learners who want to gain hands-on experience with TensorFlow.
Provides a comprehensive overview of generative adversarial networks. It covers topics such as architecture, training, and applications. It valuable resource for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of recurrent neural networks. It covers topics such as architecture, training, and applications. It valuable resource for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of deep reinforcement learning. It covers topics such as architecture, training, and applications. It valuable resource for learners who want to gain a deeper understanding of the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y5fy6n/convolutional
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Effort
At the rate of 5 hours a week, it typically takes 5 weeks to complete this course
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Andrew Ng
Kian Katanforoosh
Younes Bensouda Mourri
Language
English
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This course belongs to a
collection
called
Deep Learning. It's comprised of five courses:
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks (viewing)
- Sequence Models
- Neural Networks and Deep Learning
Featured in The Course Notes
Read
one article that features
this course,
Convolutional Neural Networks
from our blog, The Course Notes:
To read more articles from OpenCourser, visit:
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