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 foundational understanding of machine learning and deep learning concepts.

• Classification Models
• In this module, we will delve into the realm of classification models, focusing on their types, evaluation metrics, and implementation. You will learn about key concepts such as the confusion matrix and ROC curve, and engage in practical exercises to build and evaluate multi-class classification models.
• CNN: Image Classification
• In this module, we will explore the power of convolutional neural networks (CNNs) in image classification tasks. You will learn about the CNN architecture, preprocess images for optimal results, and gain hands-on experience in implementing binary and multi-class image classification models.
• CNN: Audio Classification
• In this module, we will focus on using convolutional neural networks for audio classification. You will get a comprehensive introduction to the topic, learn how to conduct exploratory data analysis on audio data, and engage in practical exercises to build and evaluate your own audio classification models.
• CNN: Object Detection
• In this module, we will dive into object detection using convolutional neural networks. You will learn about essential accuracy metrics, implement popular object detection algorithms like YOLO, and utilize GPU resources for training and inference to build robust object detection models.
• Style Transfer
• In this module, we will cover the fascinating topic of neural style transfer. You will understand the underlying principles, implement style transfer algorithms through coding, and explore various creative applications to transform images in unique ways.
• Pre-Trained Networks and Transfer Learning
• In this module, we will delve into pre-trained networks and transfer learning. You will learn how to leverage pre-trained models, implement transfer learning techniques through coding exercises, and understand the advantages of applying these concepts to various machine learning tasks.
• Recurrent Neural Networks
• In this module, we will introduce recurrent neural networks (RNNs) and their applications. You will explore the basics of RNNs, implement Long Short-Term Memory (LSTM) networks through practical coding exercises, and engage in tasks designed to deepen your understanding of these powerful models.