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Ryan Ahmed
In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Convolutional Neural Networks (CNNs).
- Import Key libraries, dataset and visualize images.
- Perform image normalization and convert from color-scaled to gray-scaled images.
- Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend.
- Compile and fit Deep Learning model to training data.
- Assess the performance of trained CNN and ensure its generalization using various KPIs.
Read more
In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Convolutional Neural Networks (CNNs).
- Import Key libraries, dataset and visualize images.
- Perform image normalization and convert from color-scaled to gray-scaled images.
- Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend.
- Compile and fit Deep Learning model to training data.
- Assess the performance of trained CNN and ensure its generalization using various KPIs.
Read more
In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Convolutional Neural Networks (CNNs).
- Import Key libraries, dataset and visualize images.
- Perform image normalization and convert from color-scaled to gray-scaled images.
- Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend.
- Compile and fit Deep Learning model to training data.
- Assess the performance of trained CNN and ensure its generalization using various KPIs.
- Improve network performance using regularization techniques such as dropout.
Register for this course and see more details by visiting:
OpenCourser.com/course/bnum3w/traffic
What's inside
Syllabus
Classify Traffic Signs Using Deep Learning for Self-Driving Cars
In this hands-on project, we will train deep learning models known as Convolutional Neural Networks (CNNs) to classify 43 traffic sign images. This project could be practically applied to self-driving cars. In this hands-on project we will go through the following tasks: (1) Import libraries and datasets (2) Images visualization (3) Convert images to gray-scale and perform normalization (4) Build deep learning model (5) Compile and train deep learning model (6) Assess trained model performance
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the theory and intuition behind Convolutional Neural Networks (CNNs), which are fundamental to deep learning and image recognition
Covers the practical application of CNNs in a self-driving car scenario, making the course relevant to the automotive industry
Involves hands-on exercises, providing learners with practical experience in building and training CNN models
Requires familiarity with Python, Keras, and TensorFlow, making it suitable for learners with some prior knowledge in deep learning
Practical application may require additional resources and technical expertise, such as hardware for self-driving cars
Save this course
Save Traffic Sign Classification Using Deep Learning in Python/Keras to your list so you can find it easily later:
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Reviews summary
Traffic sign classification in python
Learners say that Traffic Sign Classification Using Deep Learning in Python/Keras is a well received course offering hands-on guidance (guided project) for beginners to learn traffic sign recognition using Deep Learning and CNN in Python and Keras. The detailed explanations are helpful and the instructor delivers the knowledge effectively. Many learners are able to build their own project and get a good score (100/100) after taking this course.
Helpful explanations provided.
"The comprehensive explanations helped me a lot and now I can build a project of my own."
"Explaining the underlying concepts was done well and the overfitting mistakes done by users has been adressed well here."
Practical exercises included.
"Exceptional hands-on experience."
Valuable project experience.
"Great project materials. Great instructor; beautifully demonstrated."
Knowledgeable and clear instructor.
"The instructor explains very well each and every line of code."
Well received course.
"Learners say this course is well received."
"A few excercises to find syntax during the mini challenges was good."
"Final assignment involves right balance of conceptuality and hands on."
Insufficient resources.
"Resources might have been provided as the could desktop was not function properly and there was no proper response from instructor for messages."
"It's a good guided project. Don't trust Rhyme, the cloud desktop which is being used. I can't practice simultaneously. PS: There are no datasets available, so check Kaggle."
Experiences slow speeds and user-unfriendly tools.
"The external tool is not good. It is very slow and not user-friendly."
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 Traffic Sign Classification Using Deep Learning in Python/Keras with these
activities:
Review Prior Math Knowledge
Show steps
Reviewing key math skills and principles will bolster your preparation for CNN theory and application.
Browse courses on
Math
Show steps
-
Review key concepts in linear algebra, such as vectors, matrices, and basic operations.
-
Refresh your knowledge of calculus, including derivatives and integrals.
-
Practice applying mathematical concepts to real-world problems.
Complete TensorFlow CNN Tutorial
Show steps
Following along with the TensorFlow tutorial will provide you with practical experience in implementing CNNs.
Show steps
-
Set up your environment and install the necessary libraries.
-
Load and preprocess the MNIST dataset of handwritten digits.
-
Build a CNN model using TensorFlow's Keras API.
-
Train the model and evaluate its performance.
Host a Study Group on CNNs
Show steps
Hosting a study group will allow you to share your knowledge, collaborate with others, and deepen your understanding.
Show steps
-
Gather a group of interested peers.
-
Choose a topic for each session and prepare materials.
-
Facilitate discussions and encourage active participation.
-
Provide support and guidance to group members.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve CNN Practice Problems
Show steps
Solving practice problems will reinforce your understanding of CNN theory and mathematical foundations.
Show steps
-
Find practice problems online or in textbooks that cover CNNs.
-
Attempt to solve the problems on your own.
-
Check your solutions against provided answer keys or consult with an expert if needed.
Develop a CNN Model for Image Classification
Show steps
Creating your own CNN model will provide you with a tangible demonstration of your understanding and a valuable portfolio piece.
Show steps
-
Choose a dataset for your image classification task.
-
Preprocess the data and prepare it for training.
-
Design and implement a CNN model using Keras or another suitable library.
-
Train the model and evaluate its performance.
-
Write a report summarizing your findings.
Participate in a Kaggle Competition on CNNs
Show steps
Participating in a Kaggle competition will challenge you to apply your CNN skills and compete with others.
Show steps
-
Find a Kaggle competition that focuses on CNNs.
-
Download the competition data.
-
Build a CNN model to address the competition task.
-
Submit your model to the competition leaderboard.
Review Prior Math Knowledge
Show steps
Reviewing key math skills and principles will bolster your preparation for CNN theory and application.
Browse courses on
Math
Show steps
Show steps
Show steps
- Review key concepts in linear algebra, such as vectors, matrices, and basic operations.
- Refresh your knowledge of calculus, including derivatives and integrals.
- Practice applying mathematical concepts to real-world problems.
Complete TensorFlow CNN Tutorial
Show steps
Following along with the TensorFlow tutorial will provide you with practical experience in implementing CNNs.
Show steps
Show steps
Show steps
- Set up your environment and install the necessary libraries.
- Load and preprocess the MNIST dataset of handwritten digits.
- Build a CNN model using TensorFlow's Keras API.
- Train the model and evaluate its performance.
Host a Study Group on CNNs
Show steps
Hosting a study group will allow you to share your knowledge, collaborate with others, and deepen your understanding.
Show steps
Show steps
Show steps
- Gather a group of interested peers.
- Choose a topic for each session and prepare materials.
- Facilitate discussions and encourage active participation.
- Provide support and guidance to group members.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve CNN Practice Problems
Show steps
Solving practice problems will reinforce your understanding of CNN theory and mathematical foundations.
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks that cover CNNs.
- Attempt to solve the problems on your own.
- Check your solutions against provided answer keys or consult with an expert if needed.
Develop a CNN Model for Image Classification
Show steps
Creating your own CNN model will provide you with a tangible demonstration of your understanding and a valuable portfolio piece.
Show steps
Show steps
Show steps
- Choose a dataset for your image classification task.
- Preprocess the data and prepare it for training.
- Design and implement a CNN model using Keras or another suitable library.
- Train the model and evaluate its performance.
- Write a report summarizing your findings.
Participate in a Kaggle Competition on CNNs
Show steps
Participating in a Kaggle competition will challenge you to apply your CNN skills and compete with others.
Show steps
Show steps
Show steps
- Find a Kaggle competition that focuses on CNNs.
- Download the competition data.
- Build a CNN model to address the competition task.
- Submit your model to the competition leaderboard.
Career center
Learners who complete Traffic Sign Classification Using Deep Learning in Python/Keras will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in developing and applying deep learning models. This course provides a comprehensive introduction to deep learning, covering the theory, techniques, and applications. By mastering the concepts in this course, you will gain the expertise to drive innovation in various domains, such as computer vision, natural language processing, and robotics.
Data Scientist
Data Scientist
Data Scientists are in high demand as they possess the skills to collect, analyze, and interpret large datasets. This course provides a solid foundation in deep learning, a technique gaining increasing popularity in data analysis. By mastering the concepts taught in this course, you will be well-equipped to handle complex data-driven tasks and contribute to advancements in various industries, including healthcare, finance, and transportation.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and implementing machine learning models. This course will help you build a strong foundation in deep learning, a critical technique used in many machine learning applications. By completing this course, you will enhance your ability to design, train, and evaluate machine learning models, making you a valuable asset to organizations seeking to harness the power of AI.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers focus on developing computer systems that can interpret and understand visual data. This course provides a solid foundation in deep learning, a powerful technique widely used in computer vision applications. By completing this course, you will be well-positioned to contribute to the development of cutting-edge computer vision systems, such as image recognition and object detection systems.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers are responsible for designing, developing, and implementing AI systems. This course provides a comprehensive introduction to deep learning, a fundamental technology in AI. By mastering the concepts in this course, you will gain the skills to create intelligent systems that can learn, reason, and make decisions, enabling you to drive innovation in various fields, including healthcare, finance, and manufacturing.
Research Scientist
Research Scientist
Research Scientists are responsible for conducting research and developing new technologies. This course provides a comprehensive introduction to deep learning, a rapidly evolving field with a wide range of applications in scientific research. By completing this course, you will gain the skills to apply deep learning to your research projects, enabling you to make significant contributions to your field of study.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, analyzing, and interpreting data to extract valuable insights. This course provides a solid foundation in deep learning, a powerful technique gaining increasing popularity in data analysis. By completing this course, you will enhance your ability to handle complex data-driven tasks and contribute to data-driven decision-making, making you a valuable asset to organizations seeking to leverage data for competitive advantage.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and implementing software applications. This course provides a comprehensive introduction to deep learning, an emerging technology with a wide range of applications in software development. By completing this course, you will gain the skills to incorporate deep learning into your software solutions, enabling you to develop innovative and intelligent applications.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for developing and implementing mathematical and statistical models to analyze financial data. This course provides a solid foundation in deep learning, a powerful technique gaining increasing popularity in quantitative finance. By completing this course, you will enhance your ability to develop sophisticated trading strategies and risk management models, making you a valuable asset to investment firms and financial institutions.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and developing solutions to improve efficiency. This course provides a solid foundation in deep learning, a powerful technique gaining increasing popularity in business analysis. By completing this course, you will enhance your ability to identify and solve complex business problems, making you a valuable asset to organizations seeking to improve their operations.
Product Manager
Product Manager
Product Managers are responsible for defining, developing, and launching new products. This course provides a comprehensive introduction to deep learning, an emerging technology with a wide range of applications in product development. By completing this course, you will gain the skills to leverage deep learning to develop innovative products that meet customer needs and drive business growth.
Consultant
Consultant
Consultants are responsible for providing advice and solutions to businesses. This course provides a comprehensive introduction to deep learning, an emerging technology with a wide range of applications in consulting. By completing this course, you will gain the skills to leverage deep learning to develop innovative solutions for your clients, enabling you to drive business growth and improve their operations.
Educator
Educator
Educators are responsible for teaching and training students. This course provides a comprehensive introduction to deep learning, an emerging technology with a wide range of applications in education. By completing this course, you will gain the skills to leverage deep learning to develop innovative teaching methods and materials, enabling you to improve student learning outcomes and prepare them for the future.
Entrepreneur
Entrepreneur
Entrepreneurs are responsible for starting and running their own businesses. This course provides a solid foundation in deep learning, a powerful technology with a wide range of applications in entrepreneurship. By completing this course, you will gain the skills to leverage deep learning to develop innovative products and services, enabling you to create successful businesses and drive economic growth.
Technical Writer
Technical Writer
Technical Writers are responsible for creating documentation and other written materials to explain technical concepts. This course provides a comprehensive introduction to deep learning, an emerging technology with a wide range of applications in technical writing. By completing this course, you will gain the skills to leverage deep learning to develop clear and concise technical documentation, enabling you to effectively communicate complex technical information to your audience.
For more career information including salaries, visit:
OpenCourser.com/course/bnum3w/traffic
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
Traffic Sign Classification Using Deep Learning in Python/Keras.
Comprehensive guide to deep learning using Python. It covers all the essential concepts, from theory to implementation, and valuable resource for anyone interested in learning about this field.
Save
Provides a comprehensive overview of computer vision algorithms and techniques, covering topics such as image formation, feature detection, object recognition, and image segmentation. It valuable resource for anyone interested in learning about the fundamentals of computer vision.
Provides a comprehensive overview of pattern recognition and machine learning, covering both the theory and implementation of these models. It valuable resource for anyone interested in learning more about this field.
Provides a comprehensive overview of deep learning with PyTorch, covering both the theory and implementation of these models. It valuable resource for anyone interested in learning more about this field.
Provides a comprehensive overview of deep learning for computer vision, covering topics such as image classification, object detection, and image segmentation. It valuable resource for anyone interested in learning more about this field.
Provides a comprehensive introduction to neural networks and deep learning. It covers topics such as supervised and unsupervised learning, convolutional neural networks, and recurrent neural networks. It good choice for anyone who wants to learn about the fundamentals of neural networks and deep learning.
Provides a comprehensive introduction to TensorFlow, a popular deep learning library. It covers topics such as tensor operations, neural networks, and image classification. It good choice for anyone who wants to learn about deep learning using TensorFlow.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bnum3w/traffic
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Teaches the theory and intuition behind Convolutional Neural Networks (CNNs), which are fundamental to deep learning and image recognition
Covers the practical application of CNNs in a self-driving car scenario, making the course relevant to the automotive industry
Involves hands-on exercises, providing learners with practical experience in building and training CNN models
Requires familiarity with Python, Keras, and TensorFlow, making it suitable for learners with some prior knowledge in deep learning
Practical application may require additional resources and technical expertise, such as hardware for self-driving cars
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