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This advanced course on Recurrent Neural Networks (RNNs) addresses key challenges like the vanishing gradient problem and provides solutions such as Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTM) networks. You'll start with an overview of improved RNN modules and delve into bidirectional RNNs and attention models, establishing a strong foundation in advanced RNN concepts. Practical implementation using TensorFlow is emphasized, with projects like text generation and stock price prediction to solidify your learning. This course ensures you gain the skills necessary to tackle real-world AI problems confidently. Through video tutorials, real-world projects, and hands-on exercises, you'll acquire the advanced knowledge and skills needed to excel in AI. By the end, you'll develop and apply advanced RNN models, understand and implement GRUs, LSTMs, and attention mechanisms, utilize TensorFlow for RNN models, and apply these models to projects like text generation and stock price prediction. Designed for data scientists, machine learning engineers, and AI enthusiasts with a solid understanding of basic RNNs and neural networks, the course combines in-depth theoretical lessons with extensive practical applications.