Enroll Course: https://www.udemy.com/course/transformer-pytorch/
The Coursera course “Transformer原理与代码精讲（PyTorch）” offers a comprehensive deep dive into one of the most influential models in modern AI — the Transformer. Originally developed for NLP tasks, Transformers have now expanded into computer vision and other fields, establishing themselves as a new paradigm in deep learning.
This course is perfect for learners who want to understand both the theoretical foundations and practical implementations of Transformers. The lecture segments thoroughly cover critical concepts such as attention mechanisms, self-attention, architecture overview, multi-head attention, positional encoding, residual connections, layer normalization, and feed-forward networks. It also explores the training process and real-world applications like machine translation.
What sets this course apart is its hands-on approach using Jupyter Notebooks. It guides students step-by-step through the code, including setup, architecture decoding, hyperparameter tuning, and training examples with synthetic data and actual machine translation tasks. This combination of theory and detailed coding walkthroughs makes it highly valuable for students aiming to build a solid understanding and practical skills.
I highly recommend this course to AI enthusiasts, software engineers, and researchers interested in mastering Transformer models. Whether you’re aiming to improve NLP applications or explore cross-domain innovations, this course provides the essential knowledge and skills to leverage Transformers effectively.
Enroll Course: https://www.udemy.com/course/transformer-pytorch/