| Students have hands-on experience in implementing and training advanced deep learning models, including generative and unsupervised techniques, using Python and the PyTorch framework. They have practical skills to solve complex tasks such as image classification, segmentation, and data generation, applying advanced CNNs, variational autoencoders, GANs, and Bayesian approaches.
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- Building Advanced CNNs for Real-World Applications (k5)
Students can implement convolutional neural networks (CNNs) for tasks like image classification, object detection, and semantic segmentation, training models on complex datasets.
- Implementing and Training Variational Autoencoders (VAEs) (k5)
Students are able to design and train VAEs to generate data, perform unsupervised learning, and explore latent representations, using PyTorch to build and optimize these models.
- Working with Generative Adversarial Networks (GANs) (k5)
Students can implement GANs, training models to generate realistic data samples and addressing challenges like mode collapse and training instability through hands-on experimentation.
- Applying Bayesian Deep Learning Techniques (k5)
Students are capable of using Bayesian deep learning techniques to estimate model uncertainty and apply probabilistic inference, gaining insights into how uncertainty impacts model predictions.
- Experimenting with Energy-Based Models and Unsupervised Learning (k5)
Students can work with energy-based models and other unsupervised learning techniques, applying them to tasks such as clustering, anomaly detection, and unsupervised data generation.
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Students have practical knowledge of implementing advanced deep learning models, including CNNs, VAEs, GANs, and Bayesian approaches, using Python and PyTorch. They have learned to apply these models to real-world tasks, experiment with unsupervised learning methods, and solve challenges in training and optimization, gaining insights into the practical applications of deep learning.
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