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Amit Yadav
In this 1.5 hour long project-based course, you will learn to create and train a Convolutional Neural Network (CNN) with an existing CNN model architecture, and its pre-trained weights. We will use the MobileNet model architecture along with its weights trained on the popular ImageNet dataset. By using a model with pre-trained weights, and then training just the last layers on a new dataset, we can drastically reduce the training time required to fit the model to the new data . The pre-trained model has already learned to recognize thousands on simple and complex image features, and we are using its output as the input to the last layers that we are training.
Read more
In this 1.5 hour long project-based course, you will learn to create and train a Convolutional Neural Network (CNN) with an existing CNN model architecture, and its pre-trained weights. We will use the MobileNet model architecture along with its weights trained on the popular ImageNet dataset. By using a model with pre-trained weights, and then training just the last layers on a new dataset, we can drastically reduce the training time required to fit the model to the new data . The pre-trained model has already learned to recognize thousands on simple and complex image features, and we are using its output as the input to the last layers that we are training.
Read more
In this 1.5 hour long project-based course, you will learn to create and train a Convolutional Neural Network (CNN) with an existing CNN model architecture, and its pre-trained weights. We will use the MobileNet model architecture along with its weights trained on the popular ImageNet dataset. By using a model with pre-trained weights, and then training just the last layers on a new dataset, we can drastically reduce the training time required to fit the model to the new data . The pre-trained model has already learned to recognize thousands on simple and complex image features, and we are using its output as the input to the last layers that we are training.
In order to be successful in this project, you should be familiar with Python, Neural Networks, and CNNs.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/qzbmkl/classification
What's inside
Syllabus
Image Classification with Transfer Learning in Keras
In this project based course, you will learn to create and train a Convolutional Neural Network (CNN) with an existing CNN model architecture, and its pre-trained weights. We will use the MobileNet model architecture along with its weights trained on the popular ImageNet dataset. By using a model with pre-trained weights, and then training just the last layers on a new dataset, we can drastically reduce the training time required to fit the model to the new data . The pre-trained model has already learned to recognize thousands on simple and complex image features, and we are using its output as the input to the last layers that we are training.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Amit Yadav, a seasoned data scientist and machine learning engineer
Suitable for learners with a basic understanding of Python, neural networks, and convolutional neural networks
Utilizes the popular ImageNet dataset and the MobileNet model architecture for transfer learning
Covers essential concepts of image classification with transfer learning in Keras
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Reviews summary
Transfer learning with keras workshop
Learners say this course is great for a fast-track start in applying a concept in real code with hands-on projects. Explanations could be improved, especially for beginners. The projects in this course are well received and easy to follow, allowing students to learn quickly. The lack of reusable assignments and the platform's slow interface were the most common complaints. Overall, learners recommend this course to hobbyists and those needing to learn these skills for their job.
Beginner-friendly
"Great tutorial with hands on."
"If you're a hobbyist or need to learn these skills for your job, this is a superb fast-track to getting something that works ready for use."
Practical, hands-on projects
"nice"
"good"
"wonderful and simple project"
"Great course, surely learnt a lot."
Lack of reusable assignments
"still misses a key step: how to save and reuse the modified model without having to rebuild it from scratch?"
Platform interface sometimes slow
"La interfaz en la plataforma de la nube fue un poco lenta, así que me alcanzó el tiempo suficiente para terminar la programación, sin embargo, no me quedó tiempo para volver a repasar el código para reforzar lo aprendido."
Explanation quality could be improved
"More detailed explanation could be given about functions used, parameters"
"Its first time I went to the Keras and TensorFlow they are super easy to implement."
"Learning a topic using Hands on project is way better than passive learning in my opinion. Explanation could've been much better."
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 Classification with Transfer Learning in Keras with these
activities:
Review CNN Basics
Show steps
Refreshes your understanding of Convolutional Neural Networks, setting a strong foundation for the course materials
Browse courses on
Convolutional Neural Networks
Show steps
-
Read a summary of CNN architectures
-
Review the mathematical operations behind CNNs
-
Watch a tutorial video on CNNs
Deep Learning with Python
Show steps
Provides a comprehensive review of Deep Learning principles and implementation techniques, reinforcing the concepts covered in the course
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
-
Read key chapters on CNN architectures and applications
-
Solve exercises on building and training CNN models
CNN Workshop with Hands-on Lab
Show steps
Offers personalized guidance from experts, allowing you to deepen your understanding of CNNs and their applications
Browse courses on
CNN
Show steps
-
Attend a CNN workshop
-
Engage in hands-on lab exercises
-
Interact with industry professionals and learn from their experience
Four other activities
Expand to see all activities and additional details
Show all seven activities
CNN Practice Problems
Show steps
Strengthens your understanding of CNNs through targeted practice, reducing the risk of misconceptions
Browse courses on
Convolutional Neural Networks
Show steps
-
Solve practice problems on CNN architectures
-
Work through exercises on CNN training
-
Debug common CNN errors
TensorFlow Object Detection Tutorial
Show steps
Provides practical experience in implementing object detection models, extending your knowledge of CNN applications
Browse courses on
TensorFlow
Show steps
-
Follow the official TensorFlow Object Detection Tutorial
-
Build an object detection model using TensorFlow
-
Evaluate the model's performance
Image Classification Project
Show steps
Provides hands-on practice in building and training your own Image Classification model, reinforcing concepts learned in the course
Show steps
-
Gather a dataset of images
-
Preprocess the images
-
Build a CNN model
-
Train the model
-
Evaluate the model's performance
Contribute to an Open-Source CNN Project
Show steps
Enhances your understanding of CNNs through practical experience, fostering critical thinking and problem-solving skills
Browse courses on
Open Source
Show steps
-
Choose a project to contribute to
-
Clone the project and set up your environment
-
Identify an area to contribute to
-
Make changes and submit a pull request
Review CNN Basics
Show steps
Refreshes your understanding of Convolutional Neural Networks, setting a strong foundation for the course materials
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Read a summary of CNN architectures
- Review the mathematical operations behind CNNs
- Watch a tutorial video on CNNs
Deep Learning with Python
Show steps
Provides a comprehensive review of Deep Learning principles and implementation techniques, reinforcing the concepts covered in the course
View
Deep Learning with TensorFlow and Keras - Third...
on Amazon
Show steps
Show steps
Show steps
- Read key chapters on CNN architectures and applications
- Solve exercises on building and training CNN models
CNN Workshop with Hands-on Lab
Show steps
Offers personalized guidance from experts, allowing you to deepen your understanding of CNNs and their applications
Browse courses on
CNN
Show steps
Show steps
Show steps
- Attend a CNN workshop
- Engage in hands-on lab exercises
- Interact with industry professionals and learn from their experience
Four other activities
Expand to see all activities and additional details
Show all seven activities
CNN Practice Problems
Show steps
Strengthens your understanding of CNNs through targeted practice, reducing the risk of misconceptions
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Solve practice problems on CNN architectures
- Work through exercises on CNN training
- Debug common CNN errors
TensorFlow Object Detection Tutorial
Show steps
Provides practical experience in implementing object detection models, extending your knowledge of CNN applications
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow the official TensorFlow Object Detection Tutorial
- Build an object detection model using TensorFlow
- Evaluate the model's performance
Image Classification Project
Show steps
Provides hands-on practice in building and training your own Image Classification model, reinforcing concepts learned in the course
Show steps
Show steps
Show steps
- Gather a dataset of images
- Preprocess the images
- Build a CNN model
- Train the model
- Evaluate the model's performance
Contribute to an Open-Source CNN Project
Show steps
Enhances your understanding of CNNs through practical experience, fostering critical thinking and problem-solving skills
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Choose a project to contribute to
- Clone the project and set up your environment
- Identify an area to contribute to
- Make changes and submit a pull request
Career center
Learners who complete Classification with Transfer Learning in Keras will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design and develop deep learning models for a variety of applications, such as image recognition, natural language processing, and speech recognition. This course in Classification with Transfer Learning in Keras can help you build a strong foundation in deep learning by providing you with hands-on experience in creating and training a CNN using pre-trained weights. This experience will be valuable for Deep Learning Engineers who need to be able to quickly and efficiently develop and deploy deep learning models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models for a variety of applications. This course in Classification with Transfer Learning in Keras can help you build a strong foundation in machine learning by providing you with hands-on experience in creating and training a CNN using pre-trained weights. This experience will be valuable for Machine Learning Engineers who need to be able to quickly and efficiently develop and deploy machine learning models.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems for a variety of applications, such as object detection, facial recognition, and medical imaging. This course in Classification with Transfer Learning in Keras can help you build a foundation for success in this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs) using pre-trained weights. By using a model with pre-trained weights, you can significantly reduce the training time required to fit the model to a new dataset, which is a valuable skill for Computer Vision Engineers who often work with large and complex datasets.
AI Architect
AI Architect
AI Architects design and develop the architecture of AI systems. This course in Classification with Transfer Learning in Keras can help you build a foundation for success in this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs) using pre-trained weights. By using a model with pre-trained weights, you can significantly reduce the training time required to fit the model to a new dataset, which is a valuable skill for AI Architects who need to be able to quickly and efficiently develop and deploy AI systems.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, organizing, and interpreting large amounts of data to identify trends and patterns. This course in Classification with Transfer Learning in Keras can help build a foundation for success in this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs) using pre-trained weights. By using a model with pre-trained weights, you can significantly reduce the training time required to fit the model to a new dataset, which is a valuable skill for Data Scientists who often work with large and complex datasets.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course in Classification with Transfer Learning in Keras can help you build a foundation for success in this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs) using pre-trained weights. By using a model with pre-trained weights, you can significantly reduce the training time required to fit the model to a new dataset, which is a valuable skill for Software Engineers who often work on projects that involve large and complex datasets.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, engineering, and medicine. This course in Classification with Transfer Learning in Keras can help build a foundation for success in this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs) using pre-trained weights. By using a model with pre-trained weights, you can significantly reduce the training time required to fit the model to a new dataset, which is a valuable skill for Research Scientists who often work on projects that involve large and complex datasets.
Data Analyst
Data Analyst
Data Analysts collect, organize, and interpret data to identify trends and patterns. This course in Classification with Transfer Learning in Keras can help build a foundation for success in this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs) using pre-trained weights. By using a model with pre-trained weights, you can significantly reduce the training time required to fit the model to a new dataset, which is a valuable skill for Data Analysts who often work with large and complex datasets.
Business Analyst
Business Analyst
Business Analysts collect and analyze data to help businesses make informed decisions. This course in Classification with Transfer Learning in Keras can help you build a foundation for success in this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs) using pre-trained weights. By using a model with pre-trained weights, you can significantly reduce the training time required to fit the model to a new dataset, which is a valuable skill for Business Analysts who often work with large and complex datasets.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data. This course in Classification with Transfer Learning in Keras may be useful for this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs). This experience may be valuable for Quantitative Analysts who need to be able to quickly and efficiently analyze large and complex datasets.
Product Manager
Product Manager
Product Managers oversee the development and marketing of products. This course in Classification with Transfer Learning in Keras may be useful for this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs). This experience may be valuable for Product Managers who need to be able to quickly and efficiently develop and deploy new products.
Project Manager
Project Manager
Project Managers plan and execute projects. This course in Classification with Transfer Learning in Keras may be useful for this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs). This experience may be valuable for Project Managers who need to be able to quickly and efficiently manage projects that involve large and complex datasets.
Consultant
Consultant
Consultants provide advice and guidance to businesses. This course in Classification with Transfer Learning in Keras may be useful for this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs). This experience may be valuable for Consultants who need to be able to quickly and efficiently analyze and solve problems for their clients.
Teacher
Teacher
Teachers educate students in a variety of subjects. This course in Classification with Transfer Learning in Keras may be useful for this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs). This experience may be valuable for Teachers who need to be able to quickly and efficiently teach students about new technologies.
Writer
Writer
Writers create written content for a variety of purposes. This course in Classification with Transfer Learning in Keras may be useful for this field by providing you with the skills needed to create and train Convolutional Neural Networks (CNNs). This experience may be valuable for Writers who need to be able to quickly and efficiently write about new technologies.
For more career information including salaries, visit:
OpenCourser.com/course/qzbmkl/classification
Reading list
We've selected nine 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
Classification with Transfer Learning in Keras.
Provides a comprehensive overview of statistical learning, covering the theory and practice of statistical learning algorithms. It valuable resource for anyone interested in learning about statistical learning, and it provides a solid foundation for the topics covered in this course.
Provides a comprehensive overview of algorithms, covering the theory and practice of algorithms. It valuable resource for anyone interested in learning about algorithms, and it provides a solid foundation for the topics covered in this course.
Provides a comprehensive overview of deep learning, including the theory and practice of training deep neural networks. It valuable resource for anyone interested in learning about deep learning, and it provides a solid foundation for the topics covered in this course.
Save
Provides a comprehensive overview of deep learning, covering the theory and practice of training deep neural networks. It valuable resource for anyone interested in learning about deep learning, and it provides a solid foundation for the topics covered in this course.
Save
Provides a comprehensive overview of machine learning, covering the theory and practice of machine learning algorithms. It valuable resource for anyone interested in learning about machine learning, and it provides a solid foundation for the topics covered in this course.
Provides a comprehensive overview of computer science, covering the theory and practice of computer science algorithms. It valuable resource for anyone interested in learning about computer science, and it provides a solid foundation for the topics covered in this course.
Provides a comprehensive overview of data structures and algorithms, covering the theory and practice of data structures and algorithms. It valuable resource for anyone interested in learning about data structures and algorithms, and it provides a solid foundation for the topics covered in this course.
Provides a practical introduction to machine learning, using Python and the scikit-learn, Keras, and TensorFlow libraries. It covers a wide range of machine learning topics, including data preprocessing, model training, and evaluation.
Provides a comprehensive overview of deep learning for computer vision, covering the theory and practice of training deep neural networks for image classification, object detection, and segmentation. It valuable resource for anyone interested in learning about deep learning for computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qzbmkl/classification
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Taught by Amit Yadav, a seasoned data scientist and machine learning engineer
Suitable for learners with a basic understanding of Python, neural networks, and convolutional neural networks
Utilizes the popular ImageNet dataset and the MobileNet model architecture for transfer learning
Covers essential concepts of image classification with transfer learning in Keras
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