Image Classification with CNNs using Keras
This 1-hour long project-based course on Coursera's Rhyme platform will teach you how to create and train a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend to solve Image Classification problems. You will get instant access to a cloud desktop with Python, Jupyter, and Tensorflow pre-installed, and you will be able to access the cloud desktop 5 times and instructions videos as many times as you want. This course is best suited for learners who are based in the North America region and have prior knowledge of python and convolutional neural networks. ▼
ADVERTISEMENT
Course Feature
Cost:
Paid
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
31st Jul, 2023
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [August 31st, 2023]
Skills and Knowledge:
- Understanding of convolutional neural networks (CNNs)
- Knowledge of how to create a CNN in Keras with a TensorFlow backend
- Ability to train CNNs to solve Image Classification problems
- Familiarity with the CIFAR-10 dataset
- Proficiency in Python programming
- Understanding of cloud computing and cloud desktops
- Ability to use Jupyter notebooks
Professional Growth:
This course contributes to professional growth by providing the following benefits:
1. Skill development: By completing this course, you will gain hands-on experience in creating and training Convolutional Neural Networks (CNNs) using Keras with a TensorFlow backend. This skill is highly valuable in the field of image classification and can be applied to various real-world problems.
2. Knowledge enhancement: The course covers the fundamentals of CNNs and their application in solving Image Classification problems. You will learn about the architecture and working principles of CNNs, as well as techniques for training and optimizing them. This knowledge will deepen your understanding of deep learning and enhance your expertise in the field.
3. Practical experience: The course is project-based, allowing you to apply the concepts and techniques learned in a real-world scenario. You will work on a subset of the popular CIFAR-10 dataset, which is widely used for image classification tasks. This hands-on experience will strengthen your problem-solving skills and enable you to tackle similar challenges in your professional career.
4. Access to pre-configured cloud desktop: The course is hosted on Coursera's hands-on project platform called Rhyme. This platform provides instant access to pre-configured cloud desktops with all the necessary software and data for the project. This eliminates the need for setting up the environment and allows you to focus solely on learning and practicing.
5. Flexibility and accessibility: The course can be accessed online through your internet browser. You can learn at your own pace and revisit the instructional videos as many times as needed. The course also provides 5 times access to the cloud desktop, ensuring that you have ample opportunity to complete the project and reinforce your learning.
Overall, this course equips you with practical skills, knowledge, and experience in CNNs and image classification. It enhances your professional profile and opens up opportunities in fields such as computer vision, artificial intelligence, and data science.
Further Education:
This course is suitable for preparing for further education. It covers the fundamentals of creating and training Convolutional Neural Networks (CNNs) using Keras with a TensorFlow backend. Image classification is a common problem in the field of computer vision, and understanding how to solve it using CNNs is a valuable skill for further studies in areas such as machine learning, artificial intelligence, and computer vision. Additionally, the course provides hands-on experience through the Rhyme platform, which allows you to practice and apply your knowledge in a practical manner.
Course Provider
Provider Coursera's Stats at AZClass
Discussion and Reviews
0.0 (Based on 0 reviews)
Start your review of Image Classification with CNNs using Keras