INFO-H 518 Deep Learning Neural Networks
Prerequisites: Linear algebra, probability and statistics, partial derivatives, and programming. Note: Programming is in Python
Deep learning has resurged with the availability of massive datasets and affordable computing, enabling new applications in computer vision and natural language processing. This course introduces convolutional, recurrent, and other neural network architectures for deep learning. Students design, implement, and train these models to solve real-world problems.
- Solve problems in linear algebra, probability, optimization, and machine learning.
- Evaluate, in the context of a case study, the advantages and disadvantages of deep learning neural network architectures and other approaches.
- Implement deep learning models in Python using the PyTorch library and train them with real-world datasets.
- Design convolution networks for handwriting and object classification from images or video.
- Design recurrent neural networks with attention mechanisms for natural language classification, generation, and translation.
- Evaluate the performance of different deep learning models (e.g., with respect to the bias-variance trade-off, overfitting and underfitting, estimation of test error).
- Perform regularization, training optimization, and hyperparameter selection on deep models.
- Analyze a deep learning model’s hardware node and GPU scalability in preparation for deployment.