Transformers in deep learning

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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