PyTorch Machine Learning Minicourse - CNNs, RNNs, and Generative Models

Learn the fundamentals of PyTorch through this comprehensive minicourse originally presented at CoDaS-HEP, covering essential machine learning concepts and deep learning architectures. Begin with an introduction to machine learning principles before diving into supervised learning and classification techniques. Explore convolutional neural networks (CNNs) and their applications, including specialized kernels for one-dimensional data processing. Advance to unsupervised learning approaches and generative models, then conclude with recurrent neural networks (RNNs) for sequential data analysis. Master practical PyTorch implementation through hands-on examples across these core deep learning topics, gaining the skills needed to build and train neural networks for various applications in high-energy physics and beyond.