Embark on a journey through the intricacies of neural networks using PyTorch, a powerful framework favored by professionals and researchers alike. The course begins with an in-depth exploration of classification models, where you'll learn to tackle different types of classification problems, utilize confusion matrices, and interpret ROC curves. As you progress, you'll engage in hands-on exercises to prepare data, build dataset classes, and construct network classes tailored for multi-class classification.
Building and Training Neural Networks with PyTorch
This course is part of PyTorch Ultimate 2024 - From Basics to Cutting-Edge Specialization
Recommended experience
What you'll learn
Build and train neural networks using PyTorch for various tasks.
Implement classification models with multi-class, multi-label datasets, and CNNs for image and audio classification.
Apply object detection techniques using the YOLO algorithm.
Explore neural style transfer, transfer learning, and implement RNNs and LSTM networks.
Skills you'll gain
Details to know
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September 2024
4 assignments
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There are 7 modules in this course
In this module, we will delve into the realm of classification models, focusing on their types, evaluation metrics, and implementation. You will learn about key concepts such as the confusion matrix and ROC curve, and engage in practical exercises to build and evaluate multi-class classification models.
What's included
16 videos2 readings
In this module, we will explore the power of convolutional neural networks (CNNs) in image classification tasks. You will learn about the CNN architecture, preprocess images for optimal results, and gain hands-on experience in implementing binary and multi-class image classification models.
What's included
11 videos
In this module, we will focus on using convolutional neural networks for audio classification. You will get a comprehensive introduction to the topic, learn how to conduct exploratory data analysis on audio data, and engage in practical exercises to build and evaluate your own audio classification models.
What's included
5 videos1 assignment
In this module, we will dive into object detection using convolutional neural networks. You will learn about essential accuracy metrics, implement popular object detection algorithms like YOLO, and utilize GPU resources for training and inference to build robust object detection models.
What's included
13 videos
In this module, we will cover the fascinating topic of neural style transfer. You will understand the underlying principles, implement style transfer algorithms through coding, and explore various creative applications to transform images in unique ways.
What's included
3 videos1 assignment
In this module, we will delve into pre-trained networks and transfer learning. You will learn how to leverage pre-trained models, implement transfer learning techniques through coding exercises, and understand the advantages of applying these concepts to various machine learning tasks.
What's included
3 videos
In this module, we will introduce recurrent neural networks (RNNs) and their applications. You will explore the basics of RNNs, implement Long Short-Term Memory (LSTM) networks through practical coding exercises, and engage in tasks designed to deepen your understanding of these powerful models.
What's included
4 videos1 reading2 assignments
Recommended if you're interested in Software Development
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.