Deep Learning and XAI with Python
Md. Zia Uddin · 2024
This chapter extensively explores the trajectory of artificial intelligence (AI), centering on machine learning and its substantial evolution into deep learning. Over the last four decades, deep learning, notably Convolutional Neural Networks (CNN), has emerged as a powerhouse in diverse research applications, particularly image processing. The chapter emphasizes the transformation from traditional machine learning to deep learning techniques, with CNNs gaining prominence for their robust discriminative power in image recognition. While CNNs excel in single-image pattern recognition, the narrative introduces Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, as adept at temporal event analysis. Challenges inherent in deep learning, such as sensitivity to noise, are addressed, with TensorFlow highlighted as a widely adopted open-source tool. The chapter introduces Neural Structured Learning (NSL) as a contemporary approach for event modeling, offering robust solutions across various research domains. A central theme is the growing significance of explainable AI (XAI) in practical applications, emphasizing transparency, fairness, and accountability. As the chapter concludes, it paves the way for an in-depth discussion on diverse machine learning techniques, incorporating the essential aspect of XAI. Overall, it is a comprehensive guide to AI&s;s past, present, and future, underscoring the need for interpretability in machine learning models.