Free
CourseIntro to PyTorch and Neural Networks
Learn how to use PyTorch to build, train, and test artificial neural networks in this course.
This course includes
This course includes
Skill level
IntermediateTime to complete
Average based on combined completion rates — individual pacing in lessons, projects, and quizzes may vary3 hoursProjects
1Prerequisites
1 courseWe suggest you complete the following courses before you get started with Intro to PyTorch and Neural Networks:- Machine Learning: Introduction with Regression
About this course
Ready to start your journey into Neural Networks and PyTorch? In this course, you will learn how to create, train, and test artificial neural networks in PyTorch, one of the most popular deep learning frameworks in Python. You will learn about common loss functions and optimizer algorithms while building working neural networks to make predictions about real-world datasets.
Skills you'll gain
Build neural networks in PyTorch
Define activation and loss functions
Evaluate neural network performance
Create real-world predictive models
Syllabus
Intro to PyTorch and Neural Networks
Learn how to create, train, and test artificial neural networks in PyTorch.
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Meet the creator of the course
Ada Morse
Data Science Instructional Designer at CodecademyEarn a certificate of completion
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Intro to PyTorch and Neural Networks course ratings and reviews
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Frequently asked questions about Intro to PyTorch and Neural Networks
Neural Networks are the machine learning models that power the most advanced AI applications today. PyTorch is an increasingly popular Python framework for working with neural networks.
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Looking for something else?
Related resources
- Article
What are Neural Networks?
An artificial neural network is an interconnected group of nodes, an attempt to mimic to the vast network of neurons in a brain. - Article
Long Short Term Memory Networks
Long short-term memory networks (LSTMs) are often used in deep learning programs for natural language processing. - Article
Common Applications of Deep Learning
This article reviews some of deep learning's common applications.
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