Packt
Advanced PyTorch Techniques and Applications
Packt

Advanced PyTorch Techniques and Applications

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

Recommended experience

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

Recommended experience

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

What you'll learn

  • Create and assess ML models for specific datasets, evaluating performance with proper metrics.

  • Design autoencoders for dimensionality reduction and build GANs for data simulation, analyzing quality.

  • Develop Graph Neural Networks for graph data and implement Transformers, including Vision Transformers.

  • Enhance models with semi-supervised learning using limited data, and deploy them with Flask on Google Cloud.

Details to know

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Recently updated!

September 2024

Assessments

5 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 12 modules in this course

In this module, we will explore the basics of recommender systems, starting from foundational concepts and progressing through hands-on coding exercises. You'll create datasets, develop and train models, and learn how to incorporate user and item information for improved recommendations. Finally, we will implement evaluation metrics to measure the system's performance.

What's included

5 videos2 readings

In this module, we will dive into autoencoders, covering both theoretical aspects and practical implementations. You will gain a solid understanding of how autoencoders work, their applications, and get hands-on experience coding these models.

What's included

3 videos

In this module, we will cover the essentials of generative adversarial networks, including an overview of their principles and coding implementations. You will learn to develop a GAN model and engage in exercises that challenge you to apply these techniques to specific tasks.

What's included

4 videos1 assignment

In this module, we will explore graph neural networks, starting with the basics and moving through coding implementations. You'll learn how to prepare data, train models, and evaluate their performance, all within the context of GNNs.

What's included

5 videos

In this module, we will delve into Transformers, beginning with foundational concepts and then focusing on their application to vision tasks. You'll gain hands-on experience in implementing and training a Vision Transformer on a custom dataset.

What's included

3 videos

In this module, we will introduce you to PyTorch Lightning, a powerful framework for PyTorch model development. You'll learn the basics, implement models, and explore techniques such as early stopping to optimize your training processes.

What's included

4 videos1 assignment

In this module, we will cover semi-supervised learning, beginning with foundational concepts and progressing through practical implementations. You will learn about supervised reference models, set up datasets, and develop models that effectively utilize both labeled and unlabeled data.

What's included

4 videos

In this module, we will explore the vast field of Natural Language Processing, from fundamental concepts to hands-on coding implementations. You'll learn to work with word embeddings, sentiment analysis, pre-trained models, and advanced topics like zero-shot classification and vector databases.

What's included

20 videos

In this module, we will cover a range of miscellaneous topics in machine learning, including architectures like ResNet and Inception, and concepts such as Extreme Learning Machines. Each topic will include both theoretical understanding and practical coding exercises.

What's included

6 videos1 assignment

In this module, we will focus on model debugging techniques, specifically using hooks. You'll learn the theoretical aspects and get hands-on experience implementing hooks to troubleshoot and optimize your models.

What's included

2 videos

In this module, we will explore the essentials of model deployment, covering both on-premise and cloud-based strategies. You'll learn to deploy models using Flask, consume data from APIs, and utilize Google Cloud for deploying model weights and REST APIs.

What's included

6 videos1 assignment

In this module, we will conclude the course by summarizing key concepts and techniques covered throughout. Additionally, we will provide resources and recommendations for further learning to help you continue your journey in advanced PyTorch techniques and applications.

What's included

1 video1 reading1 assignment

Instructor

Packt
Packt
170 Courses2,469 learners

Offered by

Packt

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