How Residual Connections in Transformers stabilize its training? скачать в хорошем качестве

How Residual Connections in Transformers stabilize its training?

Training deep neural networks like Transformers is challenging. They suffering from vanishing gradients, ineffective weight updates, and slow convergence. In this video, we break down one of the most powerful solutions: Residual Connections in Transformers What you’ll learn in this video: ✅ Why training deep networks is difficult? ✅ How residual connections work and why they are game-changers ✅ The key advantages of residual connections ✅ How residual connections fit seamlessly into Transformers (inside the Add & Norm step) By the end of this video, you’ll understand why residual connections in transformers stabilize its training and boost deep learning performance—especially in architectures like BERT and GPT! ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Timestamps: 0:00 Intro 0:18 Problems in training a Deep Network 2:45 Residual Connections 5:05 Advantages & concerns of Residual Connections 8:20 Residual Connections in Transformers 10:14 Outro ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Follow my entire Transformers playlist : 📕 Transformers Playlist:    • Transformers in Deep Learning | Introducti...   ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ ✔ 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...   ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖