We may earn an affiliate commission when you visit our partners.
Cezanne Camacho,
Luis Serrano,
Jay Alammar,
Ortal Arel,
and
Kelvin Lwin
Take Udacity's Advanced Computer Vision & Deep Learning course and discover how to combine CNN and RNN networks to build an automatic image captioning application.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Intermediate Python
- Neural network basics
- Basic probability
- Object-oriented programming basics
- Deep learning framework proficiency
Read more
Take Udacity's Advanced Computer Vision & Deep Learning course and discover how to combine CNN and RNN networks to build an automatic image captioning application.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Intermediate Python
- Neural network basics
- Basic probability
- Object-oriented programming basics
- Deep learning framework proficiency
Read more
Take Udacity's Advanced Computer Vision & Deep Learning course and discover how to combine CNN and RNN networks to build an automatic image captioning application.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Intermediate Python
- Neural network basics
- Basic probability
- Object-oriented programming basics
- Deep learning framework proficiency
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/ccl6fb/advanced
Here's a deal for you
We found an offer that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
UDACITY40
What's inside
Syllabus
Learn about advances in CNN architectures and see how region-based CNN’s, like Faster R-CNN, have allowed for fast, localized object recognition in images.
Read more
Syllabus
Learn about advances in CNN architectures and see how region-based CNN’s, like Faster R-CNN, have allowed for fast, localized object recognition in images.
Read more
Learn about the YOLO (You Only Look Once) multi-object detection model and work with a YOLO implementation.
Explore how memory can be incorporated into a deep learning model using recurrent neural networks (RNNs). Learn how RNNs can learn from and generate ordered sequences of data.
Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.
Learn about a number of different hyperparameters that are used in defining and training deep learning models. We'll discuss starting values and intuitions for tuning each hyperparameter.
Attention is one of the most important recent innovations in deep learning. In this section, you'll learn how attention models work and go over a basic code implementation.
Learn how to combine CNNs and RNNs to build a complex, automatic image captioning model.
Train a CNN-RNN model to predict captions for a given image. Your main task will be to implement an effective RNN decoder for a CNN encoder.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers advances in CNN architectures, including localized object recognition
Explores the YOLO multi-object detection model and provides hands-on experience
Introduces recurrent neural networks (RNNs) and their applications
Provides insights into Long Short-Term Memory Networks (LSTMs)
Guides learners through essential hyperparameters for deep learning models
Introduces attention models and their implementation
Combines CNNs and RNNs for building image captioning models
Teaches the implementation of an RNN decoder for CNN encoders
Requires intermediate Python, neural network basics, and probability knowledge
Assumes proficiency in object-oriented programming and deep learning frameworks
Expects fluent communication skills in English
Save this course
Save Advanced Computer Vision and Deep Learning 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 Advanced Computer Vision and Deep Learning with these
activities:
Practice Python Programming
Show steps
Sharpen your Python programming skills to ensure you're comfortable with the language used in this course.
Browse courses on
Python
Show steps
-
Complete coding exercises and challenges on platforms like LeetCode or HackerRank.
-
Review Python syntax and best practices.
Review Basic Probability and Statistics
Show steps
Strengthen your foundational knowledge of probability and statistics to support your learning in this course.
Browse courses on
Probability
Show steps
-
Review key concepts of probability theory, such as random variables, distributions, and Bayes' theorem.
-
Refresh your understanding of statistical inference, including hypothesis testing and confidence intervals.
Review Deep Learning
Show steps
Introduce the fundamental concepts and algorithms of deep learning to reinforce your understanding of course materials.
View
Deep Learning
on Amazon
Show steps
-
Read the first three chapters of the book.
-
Work through the exercises at the end of each chapter.
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow Object Detection API Tutorial
Show steps
Enhance your understanding of object detection algorithms and their implementation in TensorFlow.
Browse courses on
Object Detection
Show steps
-
Follow the step-by-step tutorial on the TensorFlow Object Detection API website.
-
Experiment with different object detection models and datasets.
Write a blog post on Image Captioning
Show steps
Improve your understanding of image captioning techniques and demonstrate your knowledge through writing.
Browse courses on
Image Captioning
Show steps
-
Choose a specific image captioning model or approach.
-
Write a detailed blog post explaining the model and how it works.
-
Share your blog post with others and receive feedback.
Kaggle Object Detection Competition
Show steps
Sharpen your object detection skills by participating in a Kaggle competition.
Browse courses on
Object Detection
Show steps
-
Join the Kaggle Object Detection Competition.
-
Develop and submit your object detection model.
-
Analyze the results of your submission and iterate to improve your model.
Design and Implement an Image Captioning Web Application
Show steps
Demonstrate your mastery of image captioning by building a fully functional web application.
Browse courses on
Image Captioning
Show steps
-
Design the architecture and user interface of the web application.
-
Implement the image captioning model into the web application.
-
Deploy the web application and make it accessible to users.
Build a Real-Time Object Detection System
Show steps
Apply your knowledge of object detection to build a practical system that can process real-time data.
Browse courses on
Object Detection
Show steps
-
Choose a suitable hardware platform and camera.
-
Develop an object detection model and train it on a dataset.
-
Integrate the object detection model into a real-time system.
-
Test and evaluate the system in a real-world environment.
Practice Python Programming
Show steps
Sharpen your Python programming skills to ensure you're comfortable with the language used in this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Complete coding exercises and challenges on platforms like LeetCode or HackerRank.
- Review Python syntax and best practices.
Review Basic Probability and Statistics
Show steps
Strengthen your foundational knowledge of probability and statistics to support your learning in this course.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review key concepts of probability theory, such as random variables, distributions, and Bayes' theorem.
- Refresh your understanding of statistical inference, including hypothesis testing and confidence intervals.
Review Deep Learning
Show steps
Introduce the fundamental concepts and algorithms of deep learning to reinforce your understanding of course materials.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book.
- Work through the exercises at the end of each chapter.
Five other activities
Expand to see all activities and additional details
Show all eight activities
TensorFlow Object Detection API Tutorial
Show steps
Enhance your understanding of object detection algorithms and their implementation in TensorFlow.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Follow the step-by-step tutorial on the TensorFlow Object Detection API website.
- Experiment with different object detection models and datasets.
Write a blog post on Image Captioning
Show steps
Improve your understanding of image captioning techniques and demonstrate your knowledge through writing.
Browse courses on
Image Captioning
Show steps
Show steps
Show steps
- Choose a specific image captioning model or approach.
- Write a detailed blog post explaining the model and how it works.
- Share your blog post with others and receive feedback.
Kaggle Object Detection Competition
Show steps
Sharpen your object detection skills by participating in a Kaggle competition.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Join the Kaggle Object Detection Competition.
- Develop and submit your object detection model.
- Analyze the results of your submission and iterate to improve your model.
Design and Implement an Image Captioning Web Application
Show steps
Demonstrate your mastery of image captioning by building a fully functional web application.
Browse courses on
Image Captioning
Show steps
Show steps
Show steps
- Design the architecture and user interface of the web application.
- Implement the image captioning model into the web application.
- Deploy the web application and make it accessible to users.
Build a Real-Time Object Detection System
Show steps
Apply your knowledge of object detection to build a practical system that can process real-time data.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Choose a suitable hardware platform and camera.
- Develop an object detection model and train it on a dataset.
- Integrate the object detection model into a real-time system.
- Test and evaluate the system in a real-world environment.
Career center
Learners who complete Advanced Computer Vision and Deep Learning will develop knowledge and skills
that may be useful to these careers:
Research Scientist
Research Scientist
Research Scientists apply scientific methods to investigate problems, develop new theories, and create new products or processes. This course can help you develop the skills needed to be a successful Research Scientist by providing you with a strong foundation in computer vision and deep learning. You will learn how to use these technologies to solve real-world problems, such as object detection and recognition, image captioning, and natural language processing.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make informed decisions. This course can help prepare you for a career as a Data Scientist by providing you with the skills needed to collect, clean, and analyze data. You will also learn how to use machine learning and deep learning techniques to build predictive models.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design, develop, and deploy deep learning models. This course can help you develop the skills needed to be a successful Deep Learning Engineer by providing you with a strong foundation in deep learning. You will learn how to use these technologies to build deep learning models that can solve real-world problems, such as object detection and recognition, natural language processing, and speech recognition.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and deploy artificial intelligence systems. This course can help you develop the skills needed to be a successful Artificial Intelligence Engineer by providing you with a strong foundation in artificial intelligence. You will learn how to use these technologies to build artificial intelligence systems that can solve real-world problems.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design, develop, and deploy computer vision systems. This course can help you develop the skills needed to be a successful Computer Vision Engineer by providing you with a strong foundation in computer vision and deep learning. You will learn how to use these technologies to build computer vision systems that can solve real-world problems, such as object detection and recognition, image segmentation, and facial recognition.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you develop the skills needed to be a successful Software Engineer by providing you with a strong foundation in computer vision and deep learning. You will learn how to use these technologies to build innovative software applications.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course can help you develop the skills needed to be a successful Machine Learning Engineer by providing you with a strong foundation in computer vision and deep learning. You will learn how to use these technologies to build machine learning models that can solve real-world problems.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers design, develop, and test autonomous vehicles. This course may be useful for Autonomous Vehicle Engineers by providing them with a foundation in computer vision and deep learning. These technologies can be used to develop autonomous vehicles that can see and understand the world around them.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. This course may be useful for Robotics Engineers by providing them with a foundation in computer vision and deep learning. These technologies can be used to develop robots that can see and understand the world around them.
Game Developer
Game Developer
Game Developers design, develop, and test video games. This course may be useful for Game Developers by providing them with a foundation in computer vision and deep learning. These technologies can be used to develop video games that are more realistic and immersive.
Medical Imaging Analyst
Medical Imaging Analyst
Medical Imaging Analysts use computer vision and deep learning to analyze medical images. This course can help you develop the skills needed to be a successful Medical Imaging Analyst by providing you with a strong foundation in these technologies. You will learn how to use these technologies to develop medical imaging applications that can help doctors diagnose and treat diseases.
Visual Effects Artist
Visual Effects Artist
Visual Effects Artists use computer vision and deep learning to create visual effects for movies and television shows. This course may be useful for Visual Effects Artists by providing them with a foundation in these technologies. These technologies can be used to create visual effects that are more realistic and convincing.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products. This course may be useful for Product Managers by providing them with a foundation in computer vision and deep learning. These technologies can be used to develop new products that are more innovative and user-friendly.
Business Analyst
Business Analyst
Business Analysts use data to solve problems and make informed decisions. This course may be useful for Business Analysts by providing them with a foundation in computer vision and deep learning. These technologies can be used to collect and analyze data more efficiently.
Technical Writer
Technical Writer
Technical Writers create documentation for software and hardware products. This course may be useful for Technical Writers by providing them with a foundation in computer vision and deep learning. These technologies can be used to create documentation that is more accurate and informative.
For more career information including salaries, visit:
OpenCourser.com/course/ccl6fb/advanced
Reading list
We've selected seven 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
Advanced Computer Vision and Deep Learning.
Save
Comprehensive guide to deep learning, covering the theoretical foundations, algorithms, and applications of this field. It valuable resource for both students and researchers who want to learn more about deep learning.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers topics such as image formation, feature extraction, object detection, and image segmentation.
Provides a comprehensive overview of pattern recognition and machine learning algorithms. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
Practical guide to deep learning with Python. It covers topics such as building and training deep learning models, and deploying them to production.
Save
Practical guide to computer vision with OpenCV, a popular computer vision library. It covers topics such as image processing, feature extraction, and object detection.
Practical guide to deep learning for coders with Fastai and PyTorch. It covers topics such as building and training deep learning models with Fastai and PyTorch.
Comprehensive guide to generative adversarial networks (GANs). It covers topics such as the theory, algorithms, and applications of GANs.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ccl6fb/advanced
Share
Similar courses
Here are nine courses similar to
Advanced Computer Vision and Deep Learning.
Deep Learning : Convolutional Neural Networks with Python
OpenCourser.com/course/zqzy2l/deep
Deep Learning : Convolutional Neural Networks with Python
Most relevant
Fundamentals of CNNs and RNNs
OpenCourser.com/course/0vwqz0/fundamentals
Fundamentals of CNNs and RNNs
Most relevant
Create Image Captioning Models with Google Cloud
OpenCourser.com/course/nnskhp/create
Create Image Captioning Models with Google Cloud
Most relevant
TensorFlow Developer Certificate - Image Classification
OpenCourser.com/course/zgobot/tensorflow
TensorFlow Developer Certificate - Image Classification
Most relevant
Machine Learning Capstone: An Intelligent Application with Deep Learning
OpenCourser.com/course/7yjxyx/Coursera
Machine Learning Capstone: An Intelligent Application...
Most relevant
Deep Learning: Convolutional Neural Networks in Python
OpenCourser.com/course/6chq1o/deep
Deep Learning: Convolutional Neural Networks in Python
Most relevant
TensorFlow for CNNs: Image Segmentation
OpenCourser.com/course/0973j1/tensorflow
TensorFlow for CNNs: Image Segmentation
Most relevant
Complete Python Based Image Processing and Computer Vision
OpenCourser.com/course/ygaqy1/complete
Complete Python Based Image Processing and Computer Vision
Most relevant
Deep Learning - Convolutional Neural Networks
OpenCourser.com/course/nced63/udacity
Deep Learning - Convolutional Neural Networks
Most relevant
Effort
1 month
Via
Udacity
Instructors
Cezanne Camacho
Luis Serrano
Jay Alammar
Ortal Arel
Kelvin Lwin
Language
English
Good to know
Covers advances in CNN architectures, including localized object recognition
Explores the YOLO multi-object detection model and provides hands-on experience
Introduces recurrent neural networks (RNNs) and their applications
Provides insights into Long Short-Term Memory Networks (LSTMs)
Guides learners through essential hyperparameters for deep learning models
Introduces attention models and their implementation
Combines CNNs and RNNs for building image captioning models
Teaches the implementation of an RNN decoder for CNN encoders
Requires intermediate Python, neural network basics, and probability knowledge
Assumes proficiency in object-oriented programming and deep learning frameworks
Expects fluent communication skills in English
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser