| Students have a comprehensive understanding of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models and know how to model and predict sequential data. They are able to implement, optimize, and apply these models to real-world tasks such as time-series prediction, natural language processing (NLP), and advanced sequence modeling techniques like Transformers, Mamba, and xLSTM.
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- Implementing Recurrent Neural Networks for Sequence Prediction (k3)
Students can design and implement basic RNN architectures to predict sequential data such as time-series or text, understanding the challenges of modeling temporal dependencies.
- Applying LSTMs to Solve the Vanishing Gradient Problem (k5)
Students are able to train LSTMs to handle long-term dependencies in sequential data, effectively mitigating the vanishing gradient problem common in standard RNNs.
- Working with Attention and Memory Models (k5)
Students can implement attention mechanisms and memory models, such as Neural Turing Machines, to improve the performance of sequence models by selectively focusing on important parts of the data.
- Building and Training Autoregressive and Transformer, Mamba, and xLSTM Models (k5)
Students are capable of implementing autoregressive sequence models like PixelRNN and advanced architectures like Transformers, applying these models to tasks such as language modeling and text generation.
- Applying RNNs, LSTMs, and Transformers in NLP Applications (k5)
Students can apply RNNs, LSTMs, and Transformers to natural language processing tasks, including next character prediction, language modeling, and sentiment analysis, gaining insights into their practical applications in NLP.
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Students have acquired knowledge of sequence prediction fundamentals, covering RNNs, LSTMs, and the challenges associated with vanishing gradients. They also have learned advanced techniques like attention models, memory models, Transformers, Mamba, and xLSTM, applying them to real-world tasks such as time-series analysis and natural language processing.
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