Why RNNs Struggle with Long Sequences | Memory Problem & Gradient Issues скачать в хорошем качестве

Why RNNs Struggle with Long Sequences | Memory Problem & Gradient Issues

Deep Learning Foundations Playlist ▶    • Deep Learning Mastery   In the previous videos, we explored Recurrent Neural Networks (RNNs) and how they are used to process sequence data such as text, speech, and time series. But what happens when the sequence becomes very long? In this video, we discuss the key limitations of simple RNNs when dealing with long sequences. In particular, we look at two important challenges: • Memory limitations — why RNNs struggle to remember information from earlier parts of long sequences • Unstable gradients — including vanishing gradients and exploding gradients, which can make training difficult We also discuss practical techniques that help stabilize training, including gradient clipping, weight initialization, learning rate considerations, and layer normalization. Understanding these limitations is important because it motivates the development of more advanced recurrent architectures such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit), which we will explore in the next video. These ideas are foundational for understanding how modern AI systems process sequential data and how architectures evolved before the arrival of Transformers and modern Large Language Models (LLMs). Video Chapters: 00:00 Introduction 01:20 Long Sequence Problem in RNNs 08:24 Unstable Gradients (Vanishing & Exploding) 12:00 Memory Limitation in RNNs 12:53 What Comes Next: LSTM & GRU If you enjoy learning AI concepts explained clearly and intuitively, consider subscribing to Pallence AI. If you’d like a more structured and hands-on approach to learning Deep Learning, you can also check out my full course: 🎓 Deep Learning Mastery: Build Your AI Foundation https://www.udemy.com/course/deep-lea... #RNN #DeepLearning #MachineLearning #AI #NeuralNetworks #VanishingGradients #AIInterviewQuestions