- Diagnosing and Addressing the Vanishing Gradient Problem (k4)
Students can recognize the vanishing gradient issue in deep learning models, visualize its impact, and apply techniques to mitigate its effects.
- Implementing and Experimenting with Advanced Neural Network Architectures (k4)
Students are able to implement and experiment with a variety of deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, adapting them to different problem domains.
- Applying Deep Learning to Specific Fields (k3)
Students can apply deep learning techniques to new fields like bioinformatics and natural language processing, understanding the unique challenges and opportunities in these areas.
- Solving Basic Reinforcement Learning Problems (k4)
Students are able to implement reinforcement learning algorithms and apply them to simple scenarios, understanding the principles and practical applications of reinforcement learning.
- Evaluating and Comparing AI Models in Applied Contexts (k5)
Students can evaluate the performance of different deep learning architectures and reinforcement learning approaches across a range of applications, critically comparing their effectiveness and suitability for specific tasks.
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Students have advanced knowledge of deep learning topics such as the vanishing gradient problem, a variety of neural network architectures (e.g., CNNs, RNNs, LSTMs), and their practical applications. They also understand reinforcement learning basics and its application in various fields, with exposure to the use of AI in bioinformatics and natural language processing.
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