We may earn an affiliate commission when you visit our partners.
Parth Dhameliya
In this 2-hour long guided-project course, you will load a pretrained state of the art model CNN and you will train in PyTorch to classify radio signals with input as spectogram images. The data that you will use, consists of spectogram images (spectogram is a representation of audio signals) and there are targets such as ( Squiggle, Noises, Narrowband, etc). Furthermore, you will apply spectogram augmentation for classification task to augment spectogram images. Moreover, you are going to create train and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to classify radio signals given any 2D Spectogram of radio signal input images.
Register for this course and see more details by visiting:
OpenCourser.com/course/a9378t/classify
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
What's inside
Syllabus
Project Overview
In this 2-hour long guided-project course, you will load a pretrained state of the art model CNN and you will train in PyTorch to classify radio signals with input as spectogram images. The data that you will use, consists of spectogram images (spectogram is a representation of audio signals) and there are targets such as ( Squiggle, Noises, Narrowband, etc). Furthermore, you will apply spectogram augmentation for classification task to augment spectogram images. Moreover, you are going to create train and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to classify radio signals given any 2D Spectogram of radio signal input images.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores spectogram augmentation to augment spectogram images, which is relevant to image classification
Provides a step-by-step guide to creating a training and evaluation function for a PyTorch model
Involves loading and training a pre-trained CNN model in PyTorch, which is valuable for learners new to deep learning
Develops practical skills in classifying radio signals using spectogram images, which is applicable in various domains
Uses a guided project approach to engage learners and provide hands-on experience
Save this course
Save Classify Radio Signals with PyTorch to your list so you can find it easily later:
Save
Save
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 Classify Radio Signals with PyTorch with these
activities:
Compile materials for the course
Show steps
Compiling materials will help you refresh your understanding of the foundational concepts.
Browse courses on
CNN
Show steps
-
Review the course syllabus.
-
Gather your textbooks, notebooks, and other materials.
-
Create a study schedule.
Practice reading spectogram images
Show steps
Practicing reading spectogram images will help you develop the skills necessary to identify and classify radio signals.
Browse courses on
CNN
Show steps
-
Find a dataset of spectogram images.
-
Load the dataset into your preferred programming environment.
-
Write a program to display the spectogram images and their labels.
-
Practice reading the spectogram images and identifying the radio signals.
Follow a tutorial on how to use CNNs for radio signal classification
Show steps
Following a tutorial on how to use CNNs for radio signal classification will help you gain the skills necessary to build and train your own CNN model.
Browse courses on
CNN
Show steps
-
Find a tutorial on how to use CNNs for radio signal classification.
-
Follow the tutorial step-by-step.
-
Ask questions in the tutorial's discussion forum if you get stuck.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice using the PyTorch library
Show steps
Practicing using the PyTorch library will help you develop the skills necessary to build and train deep learning models.
Browse courses on
PyTorch
Show steps
-
Find a dataset to work with.
-
Load the dataset into your preferred programming environment.
-
Write a program to build and train a deep learning model using PyTorch.
-
Evaluate the performance of your model.
Join a study group
Show steps
Joining a study group will allow you to discuss the course material with other students and get help with difficult concepts.
Show steps
-
Find a study group.
-
Attend the study group meetings.
-
Participate in the discussions.
Build and train a CNN model for radio signal classification
Show steps
Building and training a CNN model for radio signal classification will help you apply the skills you learned in the course to a real-world problem.
Browse courses on
CNN
Show steps
-
Gather a dataset of spectogram images.
-
Preprocess the dataset.
-
Build a CNN model.
-
Train the CNN model.
-
Evaluate the CNN model.
Attend a conference or workshop on radio signal classification
Show steps
Attending a conference or workshop on radio signal classification will allow you to learn from experts in the field and network with other professionals.
Browse courses on
CNN
Show steps
-
Find a conference or workshop on radio signal classification.
-
Register for the conference or workshop.
-
Attend the conference or workshop.
-
Network with other attendees.
Practice using the CNN model to classify radio signals
Show steps
Practicing using the CNN model to classify radio signals will help you develop the skills necessary to use the model to solve real-world problems.
Browse courses on
CNN
Show steps
-
Gather a dataset of spectogram images.
-
Load the dataset into your preferred programming environment.
-
Load the CNN model.
-
Use the CNN model to classify the radio signals.
-
Evaluate the performance of the CNN model.
Contribute to an open-source project related to radio signal classification
Show steps
Contributing to an open-source project related to radio signal classification will allow you to apply your skills to a real-world project and learn from other developers.
Browse courses on
CNN
Show steps
-
Find an open-source project related to radio signal classification.
-
Read the project's documentation.
-
Make a contribution to the project.
Compile materials for the course
Show steps
Compiling materials will help you refresh your understanding of the foundational concepts.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Review the course syllabus.
- Gather your textbooks, notebooks, and other materials.
- Create a study schedule.
Practice reading spectogram images
Show steps
Practicing reading spectogram images will help you develop the skills necessary to identify and classify radio signals.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Find a dataset of spectogram images.
- Load the dataset into your preferred programming environment.
- Write a program to display the spectogram images and their labels.
- Practice reading the spectogram images and identifying the radio signals.
Follow a tutorial on how to use CNNs for radio signal classification
Show steps
Following a tutorial on how to use CNNs for radio signal classification will help you gain the skills necessary to build and train your own CNN model.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Find a tutorial on how to use CNNs for radio signal classification.
- Follow the tutorial step-by-step.
- Ask questions in the tutorial's discussion forum if you get stuck.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice using the PyTorch library
Show steps
Practicing using the PyTorch library will help you develop the skills necessary to build and train deep learning models.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Find a dataset to work with.
- Load the dataset into your preferred programming environment.
- Write a program to build and train a deep learning model using PyTorch.
- Evaluate the performance of your model.
Join a study group
Show steps
Joining a study group will allow you to discuss the course material with other students and get help with difficult concepts.
Show steps
Show steps
Show steps
- Find a study group.
- Attend the study group meetings.
- Participate in the discussions.
Build and train a CNN model for radio signal classification
Show steps
Building and training a CNN model for radio signal classification will help you apply the skills you learned in the course to a real-world problem.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Gather a dataset of spectogram images.
- Preprocess the dataset.
- Build a CNN model.
- Train the CNN model.
- Evaluate the CNN model.
Attend a conference or workshop on radio signal classification
Show steps
Attending a conference or workshop on radio signal classification will allow you to learn from experts in the field and network with other professionals.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Find a conference or workshop on radio signal classification.
- Register for the conference or workshop.
- Attend the conference or workshop.
- Network with other attendees.
Practice using the CNN model to classify radio signals
Show steps
Practicing using the CNN model to classify radio signals will help you develop the skills necessary to use the model to solve real-world problems.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Gather a dataset of spectogram images.
- Load the dataset into your preferred programming environment.
- Load the CNN model.
- Use the CNN model to classify the radio signals.
- Evaluate the performance of the CNN model.
Contribute to an open-source project related to radio signal classification
Show steps
Contributing to an open-source project related to radio signal classification will allow you to apply your skills to a real-world project and learn from other developers.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Find an open-source project related to radio signal classification.
- Read the project's documentation.
- Make a contribution to the project.
Career center
Learners who complete Classify Radio Signals with PyTorch will develop knowledge and skills
that may be useful to these careers:
Radio Frequency Engineer
Radio Frequency Engineer
Radio Frequency Engineers design and test radio frequency (RF) systems used in a variety of applications, including communications, radar, and navigation. This course would be particularly relevant to Radio Frequency Engineers who wish to develop their skills in classifying radio signals using machine learning techniques.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course would be particularly relevant to Data Scientists who wish to develop their skills in classifying radio signals using machine learning techniques.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models to solve real-world problems. This course would be particularly relevant to Machine Learning Engineers who wish to develop their skills in classifying radio signals using machine learning techniques.
Signal Processing Engineer
Signal Processing Engineer
Signal Processing Engineers design and implement algorithms and systems for processing signals. This course would be particularly relevant to Signal Processing Engineers who wish to develop their skills in classifying radio signals using machine learning techniques.
Electrical Engineer
Electrical Engineer
Electrical Engineers design, develop, test, and maintain electrical systems and components. This course may be useful to Electrical Engineers who wish to develop their skills in classifying radio signals using machine learning techniques.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and implement computer systems and applications. This course may be useful to Computer Scientists who wish to develop their skills in classifying radio signals using machine learning techniques.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful to Software Engineers who wish to develop their skills in classifying radio signals using machine learning techniques.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. This course may be useful to Robotics Engineers who wish to develop their skills in classifying radio signals using machine learning techniques, as robots often use radio signals for communication and navigation.
Aerospace Engineer
Aerospace Engineer
Aerospace Engineers design, develop, and maintain aircraft, spacecraft, and other aerospace systems. This course may be useful to Aerospace Engineers who wish to develop their skills in classifying radio signals using machine learning techniques, as aircraft and spacecraft often use radio signals for communication and navigation.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers design, develop, and maintain medical devices and systems. This course may be useful to Biomedical Engineers who wish to develop their skills in classifying radio signals using machine learning techniques, as medical devices often use radio signals for communication and monitoring.
Chemical Engineer
Chemical Engineer
Chemical Engineers design, develop, and maintain chemical plants and processes. This course may be useful to Chemical Engineers who wish to develop their skills in classifying radio signals using machine learning techniques, as chemical plants often use radio signals for communication and control.
Civil Engineer
Civil Engineer
Civil Engineers design, develop, and maintain infrastructure systems, such as roads, bridges, and buildings. This course may be useful to Civil Engineers who wish to develop their skills in classifying radio signals using machine learning techniques, as infrastructure systems often use radio signals for communication and monitoring.
Environmental Engineer
Environmental Engineer
Environmental Engineers design, develop, and maintain environmental systems, such as water treatment plants and pollution control systems. This course may be useful to Environmental Engineers who wish to develop their skills in classifying radio signals using machine learning techniques, as environmental systems often use radio signals for communication and monitoring.
Industrial Engineer
Industrial Engineer
Industrial Engineers design, develop, and maintain industrial systems, such as factories and warehouses. This course may be useful to Industrial Engineers who wish to develop their skills in classifying radio signals using machine learning techniques, as industrial systems often use radio signals for communication and control.
Materials Engineer
Materials Engineer
Materials Engineers design, develop, and maintain materials, such as metals, plastics, and ceramics. This course may be useful to Materials Engineers who wish to develop their skills in classifying radio signals using machine learning techniques, as materials often have unique electromagnetic properties that can be detected using radio signals.
For more career information including salaries, visit:
OpenCourser.com/course/a9378t/classify
Reading list
We've selected 12 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
Classify Radio Signals with PyTorch.
Provides a comprehensive introduction to deep learning, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about deep learning, regardless of their background or experience.
Provides a practical introduction to machine learning, using Python and popular libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for anyone interested in learning how to apply machine learning to real-world problems.
Provides a comprehensive introduction to pattern recognition and machine learning, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about pattern recognition and machine learning, regardless of their background or experience.
Save
Provides a comprehensive introduction to deep learning, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about deep learning, regardless of their background or experience.
Save
Provides a comprehensive introduction to machine learning, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about machine learning, regardless of their background or experience.
Provides a comprehensive introduction to speech and language processing, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about speech and language processing, regardless of their background or experience.
Provides a comprehensive introduction to natural language processing, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about natural language processing, regardless of their background or experience.
Save
Provides a comprehensive introduction to computer vision, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about computer vision, regardless of their background or experience.
Provides a comprehensive introduction to robotics, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about robotics, regardless of their background or experience.
Provides a comprehensive introduction to control systems engineering, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about control systems engineering, regardless of their background or experience.
Provides a comprehensive introduction to signals and systems, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about signals and systems, regardless of their background or experience.
Provides a comprehensive introduction to digital signal processing, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone interested in learning more about digital signal processing, regardless of their background or experience.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/a9378t/classify
Share
Similar courses
Here are nine courses similar to
Classify Radio Signals with PyTorch.
Classify Radio Signals from Space using Keras
OpenCourser.com/course/k2tego/classify
Classify Radio Signals from Space using Keras
Most relevant
Facial Expression Recognition with PyTorch
OpenCourser.com/course/aip4no/facial
Facial Expression Recognition with PyTorch
Most relevant
Object Localization with TensorFlow
OpenCourser.com/course/k9blun/object
Object Localization with TensorFlow
Most relevant
Classification of COVID19 using Chest X-ray Images in Keras
OpenCourser.com/course/c44vef/classification
Classification of COVID19 using Chest X-ray Images in...
Most relevant
Automate Machine Learning Using Databricks AutoML
OpenCourser.com/course/r0murk/automate
Automate Machine Learning Using Databricks AutoML
Most relevant
Cifar-10 Image Classification with Keras and Tensorflow 2.0
OpenCourser.com/course/4bzuje/cifar
Cifar-10 Image Classification with Keras and Tensorflow...
Most relevant
Deep Learning for Object Detection
OpenCourser.com/course/arw4hx/deep
Deep Learning for Object Detection
Most relevant
Build a Deep Learning Based Image Classifier with R
OpenCourser.com/course/4vpfar/build
Build a Deep Learning Based Image Classifier with R
Most relevant
Introduction to Neural Networks with PyTorch
OpenCourser.com/course/6tsot1/introduction
Introduction to Neural Networks with PyTorch
Most relevant
Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Good to know
Explores spectogram augmentation to augment spectogram images, which is relevant to image classification
Provides a step-by-step guide to creating a training and evaluation function for a PyTorch model
Involves loading and training a pre-trained CNN model in PyTorch, which is valuable for learners new to deep learning
Develops practical skills in classifying radio signals using spectogram images, which is applicable in various domains
Uses a guided project approach to engage learners and provide hands-on experience
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser