Unlock the full potential of PyTorch with this comprehensive course designed for advanced users. Starting with Recommender Systems, you’ll explore how to build and evaluate these models, incorporating user and item information to enhance recommendations. Moving on to Autoencoders, the course guides you through their fundamentals and practical implementation, providing a solid foundation for dimensionality reduction and data compression tasks.
Advanced PyTorch Techniques and Applications
Ce cours fait partie de Spécialisation PyTorch Ultimate 2024 - From Basics to Cutting-Edge
Instructeur : Packt - Course Instructors
Inclus avec
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Ce que vous apprendrez
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.
Compétences que vous acquerrez
- Catégorie : Transformers
- Catégorie : Autoencoders
- Catégorie : Recommender Systems
- Catégorie : PyTorch Lightning
- Catégorie : GANs
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Il y a 12 modules dans ce cours
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.
Inclus
5 vidéos2 lectures
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.
Inclus
3 vidéos
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.
Inclus
4 vidéos1 devoir
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.
Inclus
5 vidéos
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.
Inclus
3 vidéos
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.
Inclus
4 vidéos1 devoir
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.
Inclus
4 vidéos
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.
Inclus
20 vidéos
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.
Inclus
6 vidéos1 devoir
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.
Inclus
2 vidéos
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.
Inclus
6 vidéos1 devoir
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.
Inclus
1 vidéo1 lecture1 devoir
Instructeur
Offert par
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DeepLearning.AI
DeepLearning.AI
Johns Hopkins University
DeepLearning.AI
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Foire Aux 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.