Classify Radio Signals from Space using Keras
This project-based course on Coursera's Rhyme platform will teach you the basics of using Keras with TensorFlow to solve an image classification problem. You will use 2D spectrograms of deep space radio signals collected by the Allen Telescope Array at the SETI Institute to build and train a convolutional neural network from scratch. With instant access to a cloud desktop with Python, Jupyter, and Tensorflow pre-installed, you can focus on learning and get the most out of this course. This course is best suited for learners based in North America, with access to the cloud desktop 5 times. ▼
ADVERTISEMENT
Course Feature
Cost:
Paid
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
10th 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:
- Basics of using Keras with TensorFlow
- Image classification
- Building and training a convolutional neural network from scratch
- Working with 2D spectrograms of deep space radio signals
- Accessing and using cloud desktops
- Working with Python, Jupyter, and Tensorflow
Professional Growth:
This course contributes to professional growth in several ways:
1. Learning Keras and TensorFlow: Keras is a popular deep learning framework, and TensorFlow is a widely used backend for deep learning models. By learning how to use Keras with TensorFlow, you will gain valuable skills that are in high demand in the industry. This knowledge can be applied to various other projects and tasks involving deep learning.
2. Image classification: The course focuses on solving an image classification problem using deep learning techniques. Image classification is a fundamental task in computer vision and has applications in various fields such as healthcare, autonomous vehicles, and surveillance. By gaining experience in image classification, you will enhance your understanding of computer vision and expand your skillset.
3. Working with real-world data: The course utilizes 2D spectrograms of deep space radio signals collected by the Allen Telescope Array at the SETI Institute. Working with real-world data provides a valuable opportunity to understand the challenges and complexities involved in handling and processing real-world datasets. This experience will be beneficial when working on similar projects in the future.
4. Building and training a convolutional neural network (CNN): CNNs are a powerful class of deep learning models commonly used for image classification tasks. By building and training a CNN from scratch using Keras, you will gain hands-on experience in designing and implementing deep learning models. This knowledge can be applied to various other CNN-based projects and tasks.
5. Hands-on project experience: The course is project-based and runs on Coursera's hands-on project platform called Rhyme. This platform provides a practical and interactive learning experience where you can work on projects in a hands-on manner directly in your browser. By completing this project, you will have tangible evidence of your skills and experience, which can be showcased to potential employers or clients.
Overall, this course provides a valuable opportunity to learn and apply deep learning techniques, gain experience with real-world data, and enhance your skills in image classification and building CNNs. These skills and experiences will contribute to your professional growth and make you more competitive in the field of deep learning and computer vision.
Further Education:
This course is suitable for preparing for further education. It covers the basics of using Keras with TensorFlow and teaches you how to solve an image classification problem. The project involves building and training a convolutional neural network from scratch using Keras. This knowledge and experience will be valuable for further education in the field of machine learning and deep learning.
Course Provider
Provider Coursera's Stats at AZClass
Discussion and Reviews
0.0 (Based on 0 reviews)
Start your review of Classify Radio Signals from Space using Keras