Intro to PyTorch and Neural Networks
Offered By: Codecademy
Course Description
Overview
Learn how to use PyTorch to build, train, and test artificial neural networks in 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.
* Build neural networks in PyTorch
* Define activation and loss functions
* Evaluate neural network performance
* Create real-world predictive models
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.
* 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.
- Lesson: Intro to PyTorch and Neural Networks
- Project: Predicting Electric Vehicle Charging Loads
- Quiz: Intro to PyTorch and Neural Networks
- Informational: Next Steps
Taught by
Kenny Lin
Related Courses
Macroeconometric ForecastingInternational Monetary Fund via edX Machine Learning With Big Data
University of California, San Diego via Coursera Data Science at Scale - Capstone Project
University of Washington via Coursera Structural Equation Model and its Applications | 结构方程模型及其应用 (粤语)
The Chinese University of Hong Kong via Coursera Data Science in Action - Building a Predictive Churn Model
SAP Learning