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Mo Rebaie
This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate offered at Coursera, which will help learners reinforce...
Read more
This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate offered at Coursera, which will help learners reinforce...
Read more
This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate offered at Coursera, which will help learners reinforce their skills and build more projects with Tensorflow.
In this 1-hour long project-based course, you will learn practically how to work on a basic computer vision task in the real world and build a neural network with Tensorflow, solve simple exercises, and get a bonus machine learning project implemented with Tensorflow. By the end of this project, you will have created a deep learning model in the computer vision with TensorFlow on a real-world dataset.
This class is for learners who want to use Python for building neural networks with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a practical deep learning project with TensorFlow project. Also, this project provides learners with further knowledge about solving computer vision tasks and improves their skills in Tensorflow which helps them in fulfilling their career goals by adding this project to their portfolios.
Register for this course and see more details by visiting:
OpenCourser.com/course/wthcf7/tensorflow
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what to watch for , and
possible dealbreakers
Offers hands-on labs and interactive materials, which strengthen the learning experience
Provides a practical approach to working on basic computer vision tasks in the real world
Helps learners build a neural network with Tensorflow and solve simple exercises
Offers a bonus machine learning project implemented with Tensorflow
Suited for learners who want to use Python for building neural networks with TensorFlow
Ideal for learners who are currently taking a basic deep learning course or have finished one
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Reviews summary
Tensorflow for ai: computer vision basics
TensorFlow for AI: Computer Vision Basics introduces learners to machine learning through the lens of a 1-hour project-based course. While it is considered easy for beginners who have some understanding of Python and neural networks, it is highly recommended for students who are currently taking or have already taken a foundational deep learning course. Reviews mention that the course efficiently covers basic computer vision tasks and strengthens learners' TensorFlow abilities. However, some students note that explanations could be clearer and that the course could provide more comprehensive walkthroughs.
Great for beginners.
"easy for the rookie"
Strengthens TensorFlow abilities.
"improves their skills in Tensorflow which helps them in fulfilling their career goals"
Covers practical computer vision tasks.
"learn practically how to work on a basic computer vision task"
Some explanations could be clearer.
"sometimes the explanations given by the trainer are not so clear"
Could provide more comprehensive walkthroughs.
"There could have been more detailed and comprehensive walkthroughs"
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 TensorFlow for AI: Computer Vision Basics with these
activities:
Connect with experienced professionals
Show steps
Gain valuable insights and accelerated your learning by seeking guidance from experienced professionals in the field of computer vision.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors and reach out to them.
-
Schedule meetings to discuss your goals and seek advice.
-
Maintain regular communication with your mentors.
Organize Course Materials
Show steps
Help you stay organized and keep track of course materials.
Show steps
-
Create a dedicated folder for the course.
-
Save all lecture notes, assignments, and other materials in the folder.
Review computer vision techniques
Show steps
Review fundamental computer vision techniques to strengthen your understanding of the course material.
Browse courses on
Computer Vision
Show steps
-
Revisit basic image processing operations.
-
Explore object detection algorithms.
-
Practice image segmentation techniques.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Practice Python Programming
Show steps
Ensure that you have a strong foundation in Python programming before starting the course.
Browse courses on
Python
Show steps
-
Solve coding exercises and challenges.
-
Build small Python projects.
Attend workshops on computer vision
Show steps
Expand your knowledge and connect with experts by attending workshops focused on computer vision and its applications.
Browse courses on
Computer Vision
Show steps
-
Identify workshops relevant to your learning goals.
-
Register for workshops and prepare in advance.
-
Actively participate in workshop activities and discussions.
Review of Deep Learning Concepts
Show steps
Help you refresh your memory on deep learning concepts before starting the course.
Browse courses on
Deep Learning
Show steps
-
Review the materials from the previous DeepLearning.AI TensorFlow Developer Professional Certificate course.
-
Read articles and watch videos on deep learning.
Follow tutorials on Tensorflow
Show steps
Enhance your understanding of TensorFlow by following guided tutorials that demonstrate its practical applications.
Browse courses on
TensorFlow
Show steps
-
Complete beginner-friendly tutorials on TensorFlow.
-
Explore advanced tutorials covering specific use cases.
-
Experiment with code examples to solidify your knowledge.
Guided Tutorials on TensorFlow
Show steps
Help you understand the fundamental concepts of TensorFlow and computer vision, and how to apply them in real-world scenarios.
Browse courses on
TensorFlow
Show steps
-
Follow the tutorials provided in the course materials.
-
Experiment with the code examples and try to build your own models.
Practice Exercises on Computer Vision Tasks
Show steps
Solidify your understanding of computer vision tasks and improve your skills in TensorFlow.
Browse courses on
Computer Vision
Show steps
-
Solve the exercises provided in the course materials.
-
Create your own datasets and build models to solve specific computer vision problems.
Build a Deep Learning Model with TensorFlow
Show steps
Provide you with hands-on experience in building and training deep learning models with TensorFlow.
Browse courses on
TensorFlow
Show steps
-
Choose a computer vision dataset and define the problem you want to solve.
-
Design and implement a deep learning model using TensorFlow.
-
Train and evaluate your model on the dataset.
Solve coding challenges
Show steps
Sharpen your problem-solving skills by solving coding challenges related to computer vision and machine learning.
Browse courses on
Coding
Show steps
-
Identify coding challenges relevant to the course topics.
-
Attempt to solve challenges independently.
-
Review solutions and learn from your mistakes.
Mentor junior learners
Show steps
Reinforce your understanding by helping others learn about computer vision and TensorFlow, while developing your communication and leadership skills.
Browse courses on
Mentoring
Show steps
-
Identify opportunities to mentor junior learners in computer vision.
-
Provide guidance and support to your mentees.
-
Reflect on your mentoring experiences and seek feedback to improve.
Attend TensorFlow Meetups and Conferences
Show steps
Connect with other learners and experts in the field of TensorFlow and deep learning.
Browse courses on
TensorFlow
Show steps
-
Find local TensorFlow meetups and conferences.
-
Attend the events and participate in discussions.
Develop a computer vision project
Show steps
Apply your knowledge to a practical project that demonstrates your ability to build and deploy a computer vision solution.
Browse courses on
Computer Vision
Show steps
-
Define the project scope and objectives.
-
Gather and preprocess data for your project.
-
Implement a computer vision model using TensorFlow.
-
Evaluate and refine your model's performance.
-
Deploy your project and showcase your results.
Contribute to TensorFlow Projects
Show steps
Get involved in the TensorFlow community and contribute to the development of the platform.
Browse courses on
TensorFlow
Show steps
-
Find open TensorFlow projects on platforms like GitHub.
-
Review the documentation and codebase.
-
Make contributions to the project.
Connect with experienced professionals
Show steps
Gain valuable insights and accelerated your learning by seeking guidance from experienced professionals in the field of computer vision.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors and reach out to them.
- Schedule meetings to discuss your goals and seek advice.
- Maintain regular communication with your mentors.
Organize Course Materials
Show steps
Help you stay organized and keep track of course materials.
Show steps
Show steps
Show steps
- Create a dedicated folder for the course.
- Save all lecture notes, assignments, and other materials in the folder.
Review computer vision techniques
Show steps
Review fundamental computer vision techniques to strengthen your understanding of the course material.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Revisit basic image processing operations.
- Explore object detection algorithms.
- Practice image segmentation techniques.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Practice Python Programming
Show steps
Ensure that you have a strong foundation in Python programming before starting the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Solve coding exercises and challenges.
- Build small Python projects.
Attend workshops on computer vision
Show steps
Expand your knowledge and connect with experts by attending workshops focused on computer vision and its applications.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Identify workshops relevant to your learning goals.
- Register for workshops and prepare in advance.
- Actively participate in workshop activities and discussions.
Review of Deep Learning Concepts
Show steps
Help you refresh your memory on deep learning concepts before starting the course.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Review the materials from the previous DeepLearning.AI TensorFlow Developer Professional Certificate course.
- Read articles and watch videos on deep learning.
Follow tutorials on Tensorflow
Show steps
Enhance your understanding of TensorFlow by following guided tutorials that demonstrate its practical applications.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Complete beginner-friendly tutorials on TensorFlow.
- Explore advanced tutorials covering specific use cases.
- Experiment with code examples to solidify your knowledge.
Guided Tutorials on TensorFlow
Show steps
Help you understand the fundamental concepts of TensorFlow and computer vision, and how to apply them in real-world scenarios.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow the tutorials provided in the course materials.
- Experiment with the code examples and try to build your own models.
Practice Exercises on Computer Vision Tasks
Show steps
Solidify your understanding of computer vision tasks and improve your skills in TensorFlow.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Solve the exercises provided in the course materials.
- Create your own datasets and build models to solve specific computer vision problems.
Build a Deep Learning Model with TensorFlow
Show steps
Provide you with hands-on experience in building and training deep learning models with TensorFlow.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Choose a computer vision dataset and define the problem you want to solve.
- Design and implement a deep learning model using TensorFlow.
- Train and evaluate your model on the dataset.
Solve coding challenges
Show steps
Sharpen your problem-solving skills by solving coding challenges related to computer vision and machine learning.
Browse courses on
Coding
Show steps
Show steps
Show steps
- Identify coding challenges relevant to the course topics.
- Attempt to solve challenges independently.
- Review solutions and learn from your mistakes.
Mentor junior learners
Show steps
Reinforce your understanding by helping others learn about computer vision and TensorFlow, while developing your communication and leadership skills.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior learners in computer vision.
- Provide guidance and support to your mentees.
- Reflect on your mentoring experiences and seek feedback to improve.
Attend TensorFlow Meetups and Conferences
Show steps
Connect with other learners and experts in the field of TensorFlow and deep learning.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find local TensorFlow meetups and conferences.
- Attend the events and participate in discussions.
Develop a computer vision project
Show steps
Apply your knowledge to a practical project that demonstrates your ability to build and deploy a computer vision solution.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Define the project scope and objectives.
- Gather and preprocess data for your project.
- Implement a computer vision model using TensorFlow.
- Evaluate and refine your model's performance.
- Deploy your project and showcase your results.
Contribute to TensorFlow Projects
Show steps
Get involved in the TensorFlow community and contribute to the development of the platform.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find open TensorFlow projects on platforms like GitHub.
- Review the documentation and codebase.
- Make contributions to the project.
Career center
Learners who complete TensorFlow for AI: Computer Vision Basics 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. This course can help you gain practical experience in computer vision tasks and improve your skills in TensorFlow, which is essential for building computer vision models.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design and develop deep learning models. This course can provide you with hands-on experience in building deep learning models with TensorFlow, which will help you succeed as a Deep Learning Engineer.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop AI systems. This course can help you gain experience in building neural networks with TensorFlow, which is a key skill in AI development.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to identify trends and patterns. This course may help you gain a foundation in TensorFlow, which can help you create more accurate and efficient models that can uncover valuable insights from data.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems. This course may help you build a foundation in TensorFlow, which can help you create more efficient and scalable cloud-based solutions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop, build, and maintain machine learning models. This course may help you build a foundation in TensorFlow, which can help you create better models and improve your accuracy as a Machine Learning Engineer.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data. This course may help you build a foundation in TensorFlow, which can help you create more accurate and efficient models that can uncover valuable insights from data.
Business Analyst
Business Analyst
Business Analysts analyze business processes and develop solutions to improve efficiency. This course may help you build a foundation in TensorFlow, which can help you create more accurate and efficient models that can uncover valuable insights from data.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course may help you gain proficiency in TensorFlow, which can assist you in creating more accurate and efficient models that can uncover valuable insights from data.
DevOps Engineer
DevOps Engineer
DevOps Engineers bridge the gap between development and operations teams. This course may help you build a foundation in TensorFlow, which can help you create more efficient and reliable software systems.
Technical Writer
Technical Writer
Technical Writers create documentation for software and hardware products. This course may help you build a foundation in TensorFlow, which can help you create more accurate and easy-to-understand documentation for products that use machine learning or artificial intelligence.
Research Scientist
Research Scientist
Research Scientists conduct research in various fields. This course can help you gain experience in building neural networks with TensorFlow, which can be useful for conducting research in machine learning or artificial intelligence.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products. This course can provide you with hands-on experience in building neural networks with TensorFlow, which can be useful for developing products that use machine learning or artificial intelligence.
Project Manager
Project Manager
Project Managers oversee the planning, execution, and completion of projects. This course may help you build a foundation in TensorFlow, which can help you create more efficient and effective project plans.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you gain experience in building neural networks with TensorFlow, which can be useful for developing software that uses machine learning or artificial intelligence.
For more career information including salaries, visit:
OpenCourser.com/course/wthcf7/tensorflow
Reading list
We've selected 11 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
TensorFlow for AI: Computer Vision Basics.
Provides a practical introduction to deep learning for computer vision using Python. It covers topics such as convolutional neural networks, object detection, and image segmentation. It valuable resource for anyone interested in using deep learning for computer vision tasks.
Save
Provides a comprehensive overview of computer vision algorithms and techniques, covering topics such as image processing, feature extraction, and object recognition. It valuable resource for anyone interested in learning more about the fundamentals of computer vision.
Provides a comprehensive introduction to TensorFlow, a popular open-source machine learning library. It covers topics such as building and training neural networks, working with data, and deploying models. It valuable resource for anyone interested in using TensorFlow for deep learning tasks.
Provides a practical introduction to machine learning using Python. It covers topics such as data preprocessing, model selection, and evaluation. It valuable resource for anyone interested in learning more about machine learning.
Provides a comprehensive overview of deep learning with Python. It valuable resource for anyone interested in learning more about how to use Python for deep learning tasks.
Provides a comprehensive overview of machine learning with R. It valuable resource for anyone interested in learning more about how to use R for machine learning tasks.
Provides a comprehensive overview of machine learning with Python. It valuable resource for anyone interested in learning more about how to use Python for machine learning tasks.
Provides a comprehensive overview of artificial intelligence concepts and algorithms. It valuable resource for anyone interested in learning more about the theoretical foundations of artificial intelligence.
Save
Provides a comprehensive overview of deep learning concepts and algorithms. It valuable resource for anyone interested in learning more about the theoretical foundations of deep learning.
Provides a comprehensive overview of computer vision concepts and algorithms. It valuable resource for anyone interested in learning more about the theoretical foundations of computer vision.
Provides a comprehensive overview of computer vision concepts and algorithms. It valuable resource for anyone interested in learning more about the theoretical foundations of computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wthcf7/tensorflow
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Mo Rebaie
Good to know
Offers hands-on labs and interactive materials, which strengthen the learning experience
Provides a practical approach to working on basic computer vision tasks in the real world
Helps learners build a neural network with Tensorflow and solve simple exercises
Offers a bonus machine learning project implemented with Tensorflow
Suited for learners who want to use Python for building neural networks with TensorFlow
Ideal for learners who are currently taking a basic deep learning course or have finished one
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