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Transformers

In recent years, Transformers have emerged as one of the most groundbreaking developments in machine learning and artificial intelligence. Their ability to process complex sequences of data has revolutionized how we model natural language, images, and other data structures. This learning path presents various resources, including lectures, research articles, and tutorials, to help you gain a deep understanding of these powerful models.

Prerequisites

  • Basic knowledge of Machine Learning and Deep Learning: Understanding the basics of neural networks, how they work, and how they are trained is essential. Concepts such as backpropagation, gradient descent, and overfitting should be familiar.
  • Knowledge of Linear Algebra and Statistics: Mathematical foundations, particularly linear algebra (matrices, vectors) and statistics, are crucial, as many of the concepts in Transformers are mathematically demanding.
  • Familiarity with Python and libraries like TensorFlow or PyTorch: Practical experience with Python and popular deep learning frameworks is helpful for understanding the implementations and conducting experiments with Transformers.
  • Basic understanding of Natural Language Processing (NLP): Since Transformers were originally developed for NLP, a basic knowledge of this field, such as tokenization and embeddings, is beneficial.

Learning Path

This learning path offers a structured introduction to the world of Transformers. It is designed to help you understand the theoretical foundations, develop practical skills, and learn about current applications of this technology. Whether you're a beginner or an advanced learner, this path will guide you through the key aspects of Transformers.

Fundamentals

Step Title Type Description
1 Transformers from Scratch Article This tutorial explains the basic concepts and math behind Transformers in a clear and straightforward way, making it accessible for beginners who want to understand the fundamentals. There is also a source on Github to play with.
2 The Transformer Model Tutorial A hands-on tutorial by TensorFlow that walks you through the basic implementation of a Transformer model. Ideal for those who prefer learning by doing.
3 Transformers for Beginners Video This video tutorial provides an introductory overview of Transformer models, perfect for beginners who are new to the concept by Sebastian Raschka.

Advanced Topics

Step Title Type Description
1 Stanford CS25: V4 I Overview of Transformers Video The Stanford CS25 course offers weekly lectures by leading researchers on the latest developments in Transformer models like GPT and DALL-E. The course has gained significant popularity, with renowned guest speakers and millions of views on YouTube. Expanded events and livestreams are planned for 2024, which will also be open to the public.
2 The Illustrated Transformer Article A highly visual and approachable introduction to Transformers. Jay Alammar uses diagrams and step-by-step explanations to break down how Transformers work, making it accessible even to those new to the concept.
3 Attention Is All You Need - Annotated Transformer Article/Code The "Annotated Transformer" provides a simplified explanation of the original Transformer paper, "Attention Is All You Need," by Vaswani et al. It includes detailed explanations and code to help understand the core concepts.