| Students are able to handle, preprocess, and visualize different types of datasets, and apply foundational deep learning techniques for practical problem-solving. Students are capable of developing, training, and optimizing artificial neural networks using deep learning frameworks like PyTorch, including understanding and applying techniques like gradient descent, batch normalization, and dropout.
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- Manipulating and Visualizing Various Data Types (k3)
Students can handle different kinds of datasets, perform data visualization, apply dimensionality reduction techniques, and cluster data effectively.
- Implementing and Training Basic Neural Networks (k4)
Students can build and train simple artificial neural networks, moving from linear and logistic regression to more complex architectures using frameworks such as PyTorch.
- Applying Neural Network Optimization Techniques (k4)
Students can apply optimization techniques for neural networks, including understanding gradient descent, using techniques like batch normalization, dropout, and transfer learning to enhance model performance.
- Experimenting with Deep Learning Models (k3)
Students are able to experiment with and analyze the performance of initial neural network models, including convolutional neural networks, through hands-on practice and iterative improvements.
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Students possess foundational knowledge of deep learning concepts, including neural networks, gradient descent, data visualization, and handling of various types of datasets. They understand the use of AI frameworks like PyTorch and are familiar with optimization techniques and deep learning architectures like Convolutional Neural Networks.
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