Deep Learning Capstone: Building Pipelines with PyTorch and Keras

Transitioning from theoretical deep learning to building a complete project pipeline can be a major hurdle. This course bridges that gap by guiding you through a structured, text-based capstone project. You will gain the confidence to design, execute, and evaluate neural network models from scratch. By working through a complete machine learning lifecycle, you will learn how to handle data pipelines, train models in both PyTorch and Keras, and systematically compare their performance. What you'll learn: - Understand the core architectural differences and workflows of PyTorch and Keras. - Configure robust data loading, preprocessing, and augmentation pipelines for deep learning. - Build and train convolutional neural networks using modern best practices in both frameworks. - Compare model performance using standard evaluation metrics and validation strategies. - Apply basic MLOps principles to track model experiments, parameters, and training runs. The journey begins with foundational pipeline concepts and terminology before diving into hands-on code examples. You will progress from raw data preparation to model architecture design, training loops, and comparative analysis. This course is designed for aspiring AI developers and students who have a basic understanding of Python and neural networks and want to build their first end-to-end project. Start building your deep learning portfolio today.