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学生对 Packt 提供的 Advanced PyTorch Techniques and Applications 的评价和反馈

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12 个评分

课程概述

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. 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. Generative Adversarial Networks (GANs) are covered next, where you’ll learn to implement and apply GANs to various scenarios, sharpening your skills in creating realistic data simulations. The course also delves into Graph Neural Networks (GNNs), teaching you to handle graph data for tasks like node classification. You’ll then explore the Transformers architecture, including its adaptation for vision tasks with Vision Transformers (ViT), providing you with the skills to tackle complex sequence and vision problems. In addition to model building, the course emphasizes PyTorch Lightning for streamlined model development and early stopping techniques to optimize training. Semi-supervised learning methods are also covered, helping you leverage both labeled and unlabeled data for improved model performance. The extensive Natural Language Processing (NLP) section ensures you master word embeddings, sentiment analysis, and advanced techniques like zero-shot classification. The course concludes with essential topics in model deployment, using frameworks like Flask and Google Cloud to bring your models to production. This course is designed for data scientists, machine learning engineers, and AI researchers with a solid foundation in PyTorch. Prerequisites include a strong understanding of machine learning fundamentals, proficiency in Python programming, and prior experience with PyTorch....

热门审阅

XW

Jan 2, 2025

This course fits me well and I gained lots of coding knowledge and practice in PyTorch implementations of ML, and DL. I learned a lot and feel great! Thank you, Bert Gollnick!

FK

Jul 10, 2025

This is excellent course from beginner to expert level.

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1 - Advanced PyTorch Techniques and Applications 的 4 个评论（共 4 个）

创建者 Sudipto B

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Jun 27, 2025

I think It's a great course to get your PyTorch fundamentals right; specially if one attempts/ completes all the codes themselves after going through the sections. (In some sections some contents are a little bit outdated, however considering how fast the field is moving, that's not unexpected or unfair in any way.) Some little added sections on the Parameter Efficient Fine Tuning techniques like LoRA etc. would have made this course more valuable. A brilliant course nevertheless.

创建者 Xu W

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Jan 2, 2025

This course fits me well and I gained lots of coding knowledge and practice in PyTorch implementations of ML, and DL. I learned a lot and feel great! Thank you, Bert Gollnick!

创建者 Furhi k

•

Jul 10, 2025

This is excellent course from beginner to expert level.

创建者 Tamas S

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Sep 12, 2025

The course offers a valuable collection of code resources; however, the lectures lack detail and are not presented in clear language. Despite these drawbacks, I would still recommend the course for its practical code examples.