Building and Training Neural Networks with PyTorch

Embark on a journey through the intricacies of neural networks using PyTorch, a powerful framework favored by professionals and researchers alike. The course begins with an in-depth exploration of classification models, where you'll learn to tackle different types of classification problems, utilize confusion matrices, and interpret ROC curves. As you progress, you'll engage in hands-on exercises to prepare data, build dataset classes, and construct network classes tailored for multi-class classification. Moving forward, the course delves into Convolutional Neural Networks (CNNs) for image and audio classification. You'll discover the architecture of CNNs, implement image preprocessing techniques, and develop both binary and multi-class image classification models. Additionally, the course covers advanced topics like layer calculations and the application of CNNs in audio classification, ensuring you gain a holistic understanding of these powerful models. The journey continues with a focus on object detection, where you'll explore accuracy metrics, labeling formats, and the YOLO (You Only Look Once) algorithm. Practical coding exercises will guide you through the setup, data preparation, model training, and inference processes. Furthermore, you'll delve into neural style transfer, pre-trained networks, transfer learning, and recurrent neural networks (RNNs), including hands-on coding with LSTM networks. This course is designed for data scientists, AI professionals, and developers eager to master neural networks using PyTorch. Prerequisites include experience with Python and a fundamental understanding of machine learning and deep learning concepts.