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Classify Radio Signals with PyTorch

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.

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What's inside

Syllabus

Project Overview

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Know what's good

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, 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

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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

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Compiling materials will help you refresh your understanding of the foundational concepts.

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Review the course syllabus.
Gather your textbooks, notebooks, and other materials.
Create a study schedule.

Practice reading spectogram images

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Practicing reading spectogram images will help you develop the skills necessary to identify and classify radio signals.

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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

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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.

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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

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Practice using the PyTorch library

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Practicing using the PyTorch library will help you develop the skills necessary to build and train deep learning models.

Browse courses on PyTorch

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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

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Joining a study group will allow you to discuss the course material with other students and get help with difficult concepts.

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Find a study group.
Attend the study group meetings.
Participate in the discussions.

Build and train a CNN model for radio signal classification

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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.

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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

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Attending a conference or workshop on radio signal classification will allow you to learn from experts in the field and network with other professionals.

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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

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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.

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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

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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.

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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 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.

See salaries and explore the career path for Radio Frequency Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning 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.

See salaries and explore the career path for Signal Processing 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.

See salaries and explore the career path for Electrical Engineer

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.

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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.

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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.

See salaries and explore the career path for Robotics 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.

See salaries and explore the career path for Aerospace 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.

See salaries and explore the career path for Biomedical 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.

See salaries and explore the career path for Chemical 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.

See salaries and explore the career path for Civil 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.

See salaries and explore the career path for Environmental 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.

See salaries and explore the career path for Industrial 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.

See salaries and explore the career path for Materials Engineer

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.

Deep Learning with Python, Second Edition

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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.

Classify Radio Signals with PyTorch

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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