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Cifar-10 Image Classification with Keras and Tensorflow 2.0
Ryan Ahmed
Ryan Ahmed
In this guided project, we will build, train, and test a deep neural network model to classify low-resolution images containing airplanes, cars, birds, cats, ships, and trucks in Keras and Tensorflow 2.0. We will use Cifar-10 which is a benchmark dataset that stands for the Canadian Institute For Advanced Research (CIFAR) and contains 60,000 32x32 color images. This project is practical and directly applicable to many industries.
Register for this course and see more details by visiting:
OpenCourser.com/course/4bzuje/cifar
What's inside
Syllabus
Project Overview
In this guided project, we will build, train, and test a deep neural network model to classify low-resolution images containing airplanes, cars, birds, cats, ships, and trucks in Keras and Tensorflow 2.0. We will use Cifar-10 which is a benchmark dataset that stands for the Canadian Institute For Advanced Research (CIFAR) and contains 60,000 32x32 color images. This project is practical and directly applicable to many industries.
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Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Course provides hands-on practice building, training, and testing deep neural network models in Keras and Tensorflow 2.0
Students will gain experience with the Cifar-10 dataset, a benchmark dataset for image classification
Project is practical and directly applicable to various industries
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Activities
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in Cifar-10 Image Classification with Keras and Tensorflow 2.0 with these
activities:
Seek guidance from experienced professionals in the field
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Gain valuable insights and support from experts who can provide personalized guidance and advice.
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Mentorship
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Identify potential mentors who have experience in deep neural networks and image classification
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Reach out to these individuals and express your interest in mentorship
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Meet with your mentor regularly to discuss your progress and seek advice
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Follow the guidance and feedback provided by your mentor
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Connect with other students to share knowledge, discuss course material, and clarify concepts.
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Search for study groups or online forums related to deep neural networks
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Join a group that aligns with your learning style and schedule
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Participate in discussions, ask questions, and share your insights
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Collaborate on projects or assignments with other members of your group
Build a deep neural network from scratch
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Deepen your understanding of neural network architecture and gain practical experience in building and training models.
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Neural Network Architecture
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Design the architecture of your neural network, including the number of layers, nodes, and activation functions
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Implement the network using Keras and Tensorflow 2.0
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Train and evaluate your model on the Cifar-10 dataset
-
Optimize the hyperparameters of your model to improve its performance
-
Document your code and share it on GitHub
Show all three activities
Seek guidance from experienced professionals in the field
Show steps
Gain valuable insights and support from experts who can provide personalized guidance and advice.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors who have experience in deep neural networks and image classification
- Reach out to these individuals and express your interest in mentorship
- Meet with your mentor regularly to discuss your progress and seek advice
- Follow the guidance and feedback provided by your mentor
Join a study group or online forum
Show steps
Connect with other students to share knowledge, discuss course material, and clarify concepts.
Show steps
Show steps
Show steps
- Search for study groups or online forums related to deep neural networks
- Join a group that aligns with your learning style and schedule
- Participate in discussions, ask questions, and share your insights
- Collaborate on projects or assignments with other members of your group
Build a deep neural network from scratch
Show steps
Deepen your understanding of neural network architecture and gain practical experience in building and training models.
Browse courses on
Neural Network Architecture
Show steps
Show steps
Show steps
- Design the architecture of your neural network, including the number of layers, nodes, and activation functions
- Implement the network using Keras and Tensorflow 2.0
- Train and evaluate your model on the Cifar-10 dataset
- Optimize the hyperparameters of your model to improve its performance
- Document your code and share it on GitHub
Show all three activities
Career center
Learners who complete Cifar-10 Image Classification with Keras and Tensorflow 2.0 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models to solve real-world problems. This course is highly relevant to Machine Learning Engineers as it provides a practical introduction to deep learning, one of the most important subfields of machine learning. By completing this course, Machine Learning Engineers can gain hands-on experience in using Keras and Tensorflow 2.0, two of the most popular deep learning frameworks.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems to solve real-world problems. This course is highly relevant to Computer Vision Engineers as it provides a practical introduction to deep learning, the state-of-the-art technique for computer vision. By completing this course, Computer Vision Engineers can gain hands-on experience in using Keras and Tensorflow 2.0, two of the most popular deep learning frameworks.
Data Analyst
Data Analyst
Data Analysts clean, prepare, and analyze data to provide insights to businesses. This course is relevant because it provides a solid foundation in data analysis concepts, tools, and techniques. The hands-on experience in building and training a deep neural network model will be particularly helpful for Data Analysts who want to develop expertise in image classification. This course can help Data Analysts enhance their skills in data exploration, visualization, and statistical modeling, which are essential for uncovering valuable insights from data.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems and make predictions. This course is relevant to Data Scientists because it provides a foundation in deep learning, a powerful technique for extracting insights from data. The hands-on experience in building and training a deep neural network model will be particularly helpful for Data Scientists who want to develop expertise in image classification.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and deploy artificial intelligence systems. This course is relevant to Artificial Intelligence Engineers who want to develop expertise in deep learning, a powerful technique for artificial intelligence. The hands-on experience in building and training a deep neural network model will be particularly helpful for Artificial Intelligence Engineers who want to develop AI systems for image recognition and classification.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course is relevant to Software Engineers who want to develop expertise in deep learning, a rapidly growing field with applications in a wide range of industries. The hands-on experience in building and training a deep neural network model will be particularly helpful for Software Engineers who want to develop software for image recognition and classification.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines to support data-driven applications. This course is relevant to Data Engineers who want to develop expertise in deep learning, a powerful technique for extracting insights from data. The hands-on experience in building and training a deep neural network model will be particularly helpful for Data Engineers who want to develop data pipelines for image recognition and classification.
Research Scientist
Research Scientist
Research Scientists conduct research to advance scientific knowledge and develop new technologies. This course may be useful for Research Scientists who want to develop expertise in deep learning, a rapidly growing field with applications in a wide range of scientific disciplines. The hands-on experience in building and training a deep neural network model will be particularly helpful for Research Scientists who want to develop new image recognition and classification algorithms.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial statements and make investment decisions. This course may be useful for Financial Analysts who want to develop expertise in deep learning, a powerful technique for financial data analysis. The hands-on experience in building and training a deep neural network model will be particularly helpful for Financial Analysts who want to develop new trading strategies.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course may be useful for Operations Research Analysts who want to develop expertise in deep learning, a powerful technique for solving complex optimization problems. The hands-on experience in building and training a deep neural network model will be particularly helpful for Operations Research Analysts who want to develop new methods for solving supply chain management and logistics problems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful for Quantitative Analysts who want to develop expertise in deep learning, a powerful technique for financial data analysis. The hands-on experience in building and training a deep neural network model will be particularly helpful for Quantitative Analysts who want to develop new trading strategies.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. This course may be useful for Business Analysts who want to develop expertise in deep learning, a powerful technique for extracting insights from data. The hands-on experience in building and training a deep neural network model will be particularly helpful for Business Analysts who want to develop data-driven solutions for image recognition and classification.
Product Manager
Product Manager
Product Managers develop and manage products to meet the needs of customers. This course may be useful for Product Managers who want to develop expertise in deep learning, a rapidly growing field with applications in a wide range of industries. The hands-on experience in building and training a deep neural network model will be particularly helpful for Product Managers who want to develop products for image recognition and classification.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks to businesses. This course may be useful for Risk Analysts who want to develop expertise in deep learning, a powerful technique for extracting insights from data. The hands-on experience in building and training a deep neural network model will be particularly helpful for Risk Analysts who want to develop new methods for assessing financial risks.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand consumer behavior and develop marketing campaigns. This course may be useful for Marketing Analysts who want to develop expertise in deep learning, a powerful technique for extracting insights from data. The hands-on experience in building and training a deep neural network model will be particularly helpful for Marketing Analysts who want to develop data-driven marketing campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/4bzuje/cifar
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
Cifar-10 Image Classification with Keras and Tensorflow 2.0.
Provides a comprehensive overview of deep learning, covering fundamental concepts, architectures, and applications. It valuable resource for those seeking a deeper understanding of the underlying principles of deep learning.
Provides practical guidance on machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It includes hands-on examples and case studies, making it a great resource for those looking to apply deep learning techniques to real-world problems.
Offers a comprehensive introduction to computer vision, covering topics such as image processing, feature extraction, and object detection. It provides a solid foundation for those interested in building computer vision applications with Python.
Provides a comprehensive introduction to deep learning using the R programming language. It covers the fundamentals of deep learning, as well as practical guidance on building and training deep learning models in R.
Provides a visual and intuitive introduction to deep learning. It uses colorful illustrations and simple examples to explain complex concepts in an engaging and accessible way.
Provides a comprehensive introduction to machine learning using the R programming language. It covers a wide range of machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a visual and intuitive introduction to deep learning. It uses colorful illustrations and simple examples to explain complex concepts in an engaging and accessible way.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4bzuje/cifar
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Course provides hands-on practice building, training, and testing deep neural network models in Keras and Tensorflow 2.0
Students will gain experience with the Cifar-10 dataset, a benchmark dataset for image classification
Project is practical and directly applicable to various industries
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