IS&T RCS Tutorial - Deep Learning with PyTorch, Part One (Hands-on)

This is the first part of a two-part tutorial on PyTorch. Be sure to also register for Part Two on Thursday, February 13, from 1 PM to 3 PM to continue building your knowledge. What to Expect: This session introduces PyTorch, a popular and versatile Python library for deep learning, optimized for acceleration processing using GPUs. You’ll gain hands-on experience building and training neural networks for binary classification. Key Topics Covered: Why PyTorch? GPU acceleration using PyTorch Tensors PyTorch Autograd for automatic differentiation Working with Data Datasets and Data Loaders in PyTorch Building Neural Networks Developing deep learning models for binary classification using PyTorch Preparation: Experience with Python programming, especially using Jupyter Notebook, is required. Before the tutorial, ensure Python is installed on your machine. Detailed setup instructions and a conda environment file with the required packages will be shared. If you plan to use your own computer, the conda environment must be installed and activated in advance. Prerequisites: Familiarity with Python NumPy library. Basic understanding of machine learning and deep learning concepts and experience in using them. Additional Recommendation: For those new to machine learning, consider attending the preceding tutorials on Machine Learning with Python Scikit-Learn to build foundational knowledge. Get ready to dive into PyTorch and create powerful deep learning models!