Transformers in Deep Learning | Introduction to Transformers

We dive into Transformers in Deep Learning, a revolutionary architecture that powers today's cutting-edge models like GPT and BERT. We’ll break down the core concepts behind attention mechanisms, self-attention, and how Transformers handle sequential data. We will see the limitations of RNNs, and why Transformers are so powerful. This is a 1st part of my Transformers in Deep Learning Course, providing an overview of Transformers, and its importance. Whether you're a beginner or looking to deepen your understanding, my Transformers in Deep Learning Course playlist will guide you through the in-depth working of Transformers. ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Timestamps: 0:00 Intro 1:55 RNN Limitations 4:44 Why Word Embedding is a problem? 7:20 Self Attention Overview 11:57 Scale of Transformers? 12:54 Parallelisation in Transformers 15:21 Transfer Learning in Transformers 18:55 Multi-modality in Transformers 20:28 Outro ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Follow my entire playlist on Recurrent Neural Network (RNN) : 📕 RNN Playlist:    • What is Recurrent Neural Network in Deep L...   ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ ✔ CNN Playlist:    • What is CNN in deep learning? Convolutiona...   ✔ Complete Neural Network:    • How Neural Networks work in Machine Learni...   ✔ Complete Logistic Regression Playlist:    • Logistic Regression Machine Learning Examp...   ✔ Complete Linear Regression Playlist:    • What is Linear Regression in Machine Learn...   ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ If you want to ride on the Lane of Machine Learning, then Subscribe ▶ to my channel here:    / @machinelearningwithjay