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David Silver,
Thomas Hossler,
Antje Muntzinger,
Andreas Haja,
Aaron Brown,
Munir Jojo Verge,
and
Mathilde Badoual
Find additional content on deep learning here, including fully convolutional networks and semantic segmentation for scene understanding, as well as how to improve inference performance from a speed standpoint.
Register for this course and see more details by visiting:
OpenCourser.com/course/kwn42s/additional
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UDACITY40
What's inside
Syllabus
In this lesson you'll learn the motivation for Fully Convolutional Networks and how they are structured.
In this lesson you'll be introduced to the problem of Scene Understanding and the role FCNs play.
In this lesson you'll become familiar with various optimizations in an effort to squeeze every last bit of performance at inference.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches Fully Convolutional Networks and Semantic Segmentation, which are core skills for scene understanding, from a speed standpoint
Led by reputable data scientists such as David Silver and Thomas Hossler of Google DeepMind
Taught by a team from Udacity, a platform with a strong focus on data science education
Provides a strong foundation for those looking to build a career in scene understanding and related fields
Requires some background knowledge in computer vision and deep learning, but provides a solid overview for those with the appropriate prerequisites
Save this course
Save Additional Content: 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 Additional Content: Deep Learning with these
activities:
Organize: Course Materials
Show steps
Stay organized and enhance your learning by compiling course materials.
Show steps
-
Create a dedicated folder or notebook for the course.
-
Organize lecture notes, assignments, and other materials.
-
Review the materials regularly to reinforce your understanding.
Read: Deep Learning
Show steps
Build up foundational knowledge of Deep Learning in preparation for this course.
View
Deep Learning
on Amazon
Show steps
-
Acquire a copy of the book.
-
Read chapters 1-5.
-
Complete the exercises at the end of each chapter.
Find: FCN Expert
Show steps
Accelerate your learning by seeking guidance from an expert in FCNs.
Show steps
-
Attend conferences or workshops related to FCNs.
-
Reach out to professors or researchers in the field.
-
Connect with professionals on LinkedIn.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Study: FCNs with Classmates
Show steps
Enhance your understanding of FCNs through discussions and collaboration with peers.
Show steps
-
Form a study group with classmates.
-
Set regular meeting times.
-
Discuss lecture material, work on practice problems, and share resources.
Follow: Udacity's FCN Tutorial
Show steps
Supplement your learning by following a guided tutorial on FCNs.
Show steps
-
Find the Udacity tutorial on FCNs.
-
Follow the tutorial step-by-step.
-
Complete the exercises and quizzes.
Build: Simple CNN
Show steps
Apply your understanding of CNNs by building a simple image classification model.
Browse courses on
Convolutional Neural Networks
Show steps
-
Set up your development environment.
-
Gather a dataset of images.
-
Build a CNN model using a framework like TensorFlow or PyTorch.
-
Train and evaluate your model.
Attend: AI Meetup
Show steps
Connect with other professionals in the field and learn about the latest trends in Deep Learning.
Show steps
-
Find an AI meetup in your area.
-
Attend the meetup and introduce yourself.
-
Participate in discussions and ask questions.
Write: Blog Post
Show steps
Solidify your understanding of FCNs by explaining the concepts in a blog post.
Show steps
-
Choose a specific aspect of FCNs to focus on.
-
Research the topic thoroughly.
-
Write a clear and concise blog post explaining the concepts.
Participate: Semantic Segmentation Challenge
Show steps
Test your skills in FCNs and semantic segmentation by participating in a challenge.
Show steps
-
Find a semantic segmentation challenge.
-
Build a model and submit it to the challenge.
-
Analyze the results and make improvements.
Organize: Course Materials
Show steps
Stay organized and enhance your learning by compiling course materials.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for the course.
- Organize lecture notes, assignments, and other materials.
- Review the materials regularly to reinforce your understanding.
Read: Deep Learning
Show steps
Build up foundational knowledge of Deep Learning in preparation for this course.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Acquire a copy of the book.
- Read chapters 1-5.
- Complete the exercises at the end of each chapter.
Find: FCN Expert
Show steps
Accelerate your learning by seeking guidance from an expert in FCNs.
Show steps
Show steps
Show steps
- Attend conferences or workshops related to FCNs.
- Reach out to professors or researchers in the field.
- Connect with professionals on LinkedIn.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Study: FCNs with Classmates
Show steps
Enhance your understanding of FCNs through discussions and collaboration with peers.
Show steps
Show steps
Show steps
- Form a study group with classmates.
- Set regular meeting times.
- Discuss lecture material, work on practice problems, and share resources.
Follow: Udacity's FCN Tutorial
Show steps
Supplement your learning by following a guided tutorial on FCNs.
Show steps
Show steps
Show steps
- Find the Udacity tutorial on FCNs.
- Follow the tutorial step-by-step.
- Complete the exercises and quizzes.
Build: Simple CNN
Show steps
Apply your understanding of CNNs by building a simple image classification model.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Set up your development environment.
- Gather a dataset of images.
- Build a CNN model using a framework like TensorFlow or PyTorch.
- Train and evaluate your model.
Attend: AI Meetup
Show steps
Connect with other professionals in the field and learn about the latest trends in Deep Learning.
Show steps
Show steps
Show steps
- Find an AI meetup in your area.
- Attend the meetup and introduce yourself.
- Participate in discussions and ask questions.
Write: Blog Post
Show steps
Solidify your understanding of FCNs by explaining the concepts in a blog post.
Show steps
Show steps
Show steps
- Choose a specific aspect of FCNs to focus on.
- Research the topic thoroughly.
- Write a clear and concise blog post explaining the concepts.
Participate: Semantic Segmentation Challenge
Show steps
Test your skills in FCNs and semantic segmentation by participating in a challenge.
Show steps
Show steps
Show steps
- Find a semantic segmentation challenge.
- Build a model and submit it to the challenge.
- Analyze the results and make improvements.
Career center
Learners who complete Additional Content: Deep Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Develop, deploy, and optimize machine learning models to solve real-world problems. Gain knowledge about fully convolutional networks and semantic segmentation for scene understanding, and learn how to squeeze every last bit of performance at inference so that you can excel in this exciting field.
Data Scientist
Data Scientist
Analyze data, build models, and improve business processes. Learn about fully convolutional networks, semantic segmentation for scene understanding, and inference performance to advance your career as a Data Scientist.
Computer Vision Engineer
Computer Vision Engineer
Develop and implement computer vision solutions for various industries, including healthcare, transportation, and retail. This course will help provide you with the foundation and application knowledge needed for success in this field.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Design, develop, and evaluate artificial intelligence systems. This course will provide you with the knowledge and skills to advance your career in this dynamic field.
Deep Learning Engineer
Deep Learning Engineer
Become a specialist in the application of deep neural networks. This course will provide you with the skills and knowledge to advance your career in this field.
Robotics Engineer
Robotics Engineer
Design, develop, and evaluate robotic systems. This course may be useful to you in learning about deep learning techniques and applications that can enhance the performance of your robotic systems.
Software Engineer
Software Engineer
Design and develop software solutions for a variety of industries. This course may be useful to you in learning about deep learning techniques and applications that can enhance the performance of your software solutions.
Quality Assurance Analyst
Quality Assurance Analyst
Test software and hardware products for defects. This course may be useful to you in learning about deep learning techniques and applications that can help you to improve the quality of your testing efforts.
Product Manager
Product Manager
Manage the development and marketing of products. This course may be useful to you in learning about deep learning techniques and applications that can enhance your product development efforts.
Business Analyst
Business Analyst
Analyze business processes and identify opportunities for improvement. This course may be useful to you in learning about deep learning techniques and applications that can provide insight into optimizing business processes.
Data Analyst
Data Analyst
Analyze data to extract insights and identify trends. This course may be useful to you in learning about deep learning techniques and applications that can enhance your data analysis capabilities.
IT Consultant
IT Consultant
Provide IT consulting services to businesses. This course may be useful to you in learning about deep learning techniques and applications that can help you to provide more effective IT consulting services.
Project Manager
Project Manager
Plan and execute projects. This course may be useful to you in learning about deep learning techniques and applications that can improve the efficiency of your project management efforts.
Technical Writer
Technical Writer
Write technical documentation for software and hardware products. This course may be useful to you in learning about deep learning techniques and applications that can help you to create more effective technical documentation.
Systems Analyst
Systems Analyst
Analyze and design computer systems. This course may be useful to you in learning about deep learning techniques and applications that can help you to improve the efficiency of your systems analysis and design efforts.
For more career information including salaries, visit:
OpenCourser.com/course/kwn42s/additional
Reading list
We've selected six 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
Additional Content: Deep Learning.
Save
Provides a comprehensive overview of deep learning, covering the latest advances in the field. It valuable resource for both beginners and experienced practitioners.
Covers deep learning techniques for computer vision tasks, such as image classification, object detection, and semantic segmentation. It valuable resource for anyone who wants to learn how to use deep learning for computer vision.
Provides a practical guide to deep learning using Python. It good choice for beginners who want to learn how to build and train deep learning models.
Covers deep learning techniques for natural language processing tasks, such as text classification, text generation, and machine translation. It valuable resource for anyone who wants to learn how to use deep learning for natural language processing.
Provides a practical guide to deep learning using Fastai and PyTorch. It good choice for beginners who want to learn how to build and train deep learning models.
Covers deep learning techniques for recommender systems. It valuable resource for anyone who wants to learn how to use deep learning for recommender systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kwn42s/additional
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Time
420 hours
Level
Advanced
Via
Udacity
Instructors
David Silver
Thomas Hossler
Antje Muntzinger
Andreas Haja
Aaron Brown
Munir Jojo Verge
Mathilde Badoual
Language
English
Good to know
Teaches Fully Convolutional Networks and Semantic Segmentation, which are core skills for scene understanding, from a speed standpoint
Led by reputable data scientists such as David Silver and Thomas Hossler of Google DeepMind
Taught by a team from Udacity, a platform with a strong focus on data science education
Provides a strong foundation for those looking to build a career in scene understanding and related fields
Requires some background knowledge in computer vision and deep learning, but provides a solid overview for those with the appropriate prerequisites
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