The Role of Residual Connections and Layer Normalization in Neural Networks and Gen AI Models скачать в хорошем качестве

The Role of Residual Connections and Layer Normalization in Neural Networks and Gen AI Models

Discover the power of residual connections and layer normalization in this comprehensive tutorial! Uncover how these essential components stabilize training, combat vanishing gradients, and enhance learning efficiency in Transformer architectures. Learn their unique roles, benefits, and distinctions, and explore how they contribute to the success of cutting-edge AI models. Course Link HERE: https://sds.courses/genAI You can also find us here: Website: https://www.superdatascience.com/ Facebook:   / superdatascience   Twitter:   / superdatasci   LinkedIn:   / superdatascience   Contact us at: support@superdatascience.com Chapters: 00:00 Introduction to Residual Connections and Layer Normalization 00:31 What Are Residual Connections? 01:06 Benefits of Residual Connections 01:42 Understanding Layer Normalization 02:12 How Layer Normalization Works 02:43 Benefits of Layer Normalization 03:17 Recommended Research Papers and Next Steps #AI #MachineLearning #Transformers #DeepLearning #ArtificialIntelligence #ResidualConnections #LayerNormalization #NeuralNetworks #AITraining #CodingTutorial #DeepLearningTips #VanishingGradient #AIExplained #LearnAI #deeplearningtutorial From this video, you will learn: Residual Connections in Transformers: How they preserve earlier information and improve training efficiency. Combating Vanishing Gradients: The role of residuals in addressing this challenge during model training. Layer Normalization Explained: How it differs from batch normalization and its advantages for parallelization. Stabilizing Neural Network Training: The benefits of normalizing network layers for enhanced performance. Practical Use Cases: Why these techniques are critical for modern AI applications and Transformer success. Key Research Papers: Insights from "Deep Residual Learning for Image Recognition" and "Layer Normalization." Differences Between Layer and Batch Norm: A clear comparison to understand their specific use cases. Training Efficiency Tips: How residuals and norms improve learning in AI models. Real-World AI Stability: Practical applications of residuals and layer normalization for robust neural networks. Advancing Transformer Architectures: How these concepts make AI models scalable and effective. Additional Reading Layer Normalization - Jimmy Lei Ba et al. (2016) https://arxiv.org/abs/1607.06450 Deep Residual Learning for Image Recognition Kaiming He et al. (2015) https://arxiv.org/abs/1512.03385