Self Attention in Transformers Transformers in Deep Learning

Transformers in deep learning, in this course we will learn about the powerful Transformer architecture that revolutionized natural language processing and computer vision. We’ll begin with the fundamentals of self-attention—the core concept that enables models to weigh relationships between different input tokens. Then, we'll explore the structure of Transformer blocks, including encoders, decoders, positional encoding, and multi-head attention. You’ll gain hands-on experience implementing Transformers using frameworks like PyTorch or TensorFlow. The course also covers advanced topics such as fine-tuning pretrained models (like BERT or GPT), training on large datasets, and applying Transformers to tasks like machine translation, text classification, and image recognition. Whether you're aiming to build state-of-the-art AI applications or simply understand the tech behind modern models, this course provides a solid foundation in Transformers and their real-world use cases. Learn With Jay