Introduction to Deep Learning

Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data using artificial neural networks. The course covers fundamental principles, underlying mathematics, optimization concepts (gradient descent, backpropagation), network modules (linear, convolution, pooling layers), and common architectures (CNNs, RNNs). Applications demonstrated include computer vision, natural language processing, and reinforcement learning. Students will use the PyTorch deep learning library for implementation and complete a final project on a real-world scenario.

A brief summary of the core objectives: Master deep learning theory, implement models using PyTorch, understand specialized architectures (CNNs, RNNs, Transformers), and apply these concepts to computer vision, NLP, and sequential decision-making.

1. Explain the mathematical foundations and core optimization techniques (Gradient Descent, Backpropagation) necessary for training deep neural networks.
2. Utilize the PyTorch deep learning framework to efficiently implement, train, and debug modern network architectures using CUDA acceleration and efficient data handling techniques.
3. Design and analyze specialized architectures, including Convolutional Neural Networks (CNNs) for image data and the Transformer model for sequential dependencies.
4. Apply deep learning techniques to solve practical problems in core application domains: Computer Vision, Natural Language Processing, and Reinforcement Learning.
5. Evaluate models based on robustness, interpretability, and ethical fairness, comparing the strengths of various advanced paradigms (e.g., Generative Models, Semi-Supervised Learning).