In this video, we dive deep into the world of Transformer models 🔥-the architecture behind many modern NLP breakthroughs, including GPT! We'll guide you through the process of building a Transformer from scratch, explaining key concepts like self-attention, multi-head attention, and positional encoding 🧠. Whether you're an experienced ML engineer or just starting out, this tutorial will break down the complexities of the Transformer model and show you how to implement it step by step using Python and popular libraries like PyTorch or TensorFlow 💻.
By the end of this video, you'll understand how Transformer models work, and you’ll have your very own Transformer model 🚀 that you can tweak and experiment with for tasks like translation, text generation, and more!
What You'll Learn:
Basics of Transformer architecture 🤖
Self-attention and multi-head attention mechanisms 🔗
Building blocks of a Transformer model 🛠️
Implementing the Transformer from scratch in code 👨‍💻
Real-world applications of Transformers in NLP 🌍
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