This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras.
- Special Relativity
- Quantum Theory
- Quantum Mechanics
- Quantum-Mechanical Theory of Atoms
- Chemical Bonds and Solid-State Physics
- Nuclear Physics
- Particle Physics
- Atomic Structure and Molecules
- Kinetic Molecular Theory and Chemical Reactions
- Thermodynamics
- Calorimetry and Colligative Properties
- Solutions, Solvents and Solubility
- Acid and Base Reactions
- Electrochemistry
- Cell Structure and Viruses
- Enzyme Activity and Cell Respiration
- Cell Cycle, Reproduction and Embryology
- Genetics
- Nervous and Musculoskeletal System
- Endocrine System
- Digestive and Excretory Systems
- Cardiovascular and Respiratory Systems
- Immune System