Packt
Building and Training Neural Networks with PyTorch
Packt

Building and Training Neural Networks with PyTorch

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

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.

Details to know

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Recently updated!

September 2024

Assessments

4 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the PyTorch Ultimate 2024 - From Basics to Cutting-Edge Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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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

Instructor

Packt
Packt
170 Courses2,469 learners

Offered by

Packt

Recommended if you're interested in Software Development

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