| (*)Students have hands-on experience in implementing Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) models, and Transformers using Python and the PyTorch framework. They have the practical skills to apply these models to real-world sequence prediction tasks, such as time-series forecasting, natural language processing, and other advanced sequential data applications.
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(*)- Implementing RNNs for Sequential Data Prediction (k3)
Students can design, train, and optimize RNNs to predict sequential data, handling tasks like time-series forecasting and text generation.
- Training LSTM Models to Handle Long-Term Dependencies (k4)
Students are able to implement and train LSTM networks in PyTorch to effectively solve sequence prediction tasks that require long-term memory retention, overcoming the vanishing gradient problem.
- Working with Attention Mechanisms and Memory Models (k5)
Students can implement attention mechanisms and memory models such as Neural Turing Machines, enhancing model performance by allowing selective focus on relevant input 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 Sequence Models to NLP Applications (k5)
Students can use RNNs, LSTMs, and Transformers for natural language processing tasks, implementing practical applications such as next character prediction, language modeling, and sentiment analysis.
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(*)Students possess knowledge of sequence prediction fundamentals, covering RNNs, LSTMs, and the challenges associated with vanishing gradients. They also know of advanced techniques like attention models, memory models, Transformers, Mamba, and xLSTM, and how to apply them to real-world tasks such as time-series analysis and natural language processing.
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