Explainable Artificial Intelligence (XAI) is a rapidly growing subfield of artificial intelligence (AI) that aims to make AI models and systems more transparent and understandable to humans. The goal of XAI is to bridge the gap between the black box nature of many AI models and human interpretability, to enable users to understand why and

Stochastic processes are an essential mathematical framework for understanding and modeling random phenomena. They have numerous applications in fields such as finance, engineering, and biology. One of the most well-known and influential works in this field is Stochastic Processes: Theory for Applications by Robert G. Gallager. Gallager is a renowned American electrical engineer and information

Introduction Deep Learning has revolutionized the field of artificial intelligence and has become one of the most sought-after skills for developers and data scientists. With the increasing availability of data and computing power, it has become easier for individuals to learn and apply deep learning techniques in their projects. However, the complexity and unfamiliarity of

Introduction to Machine Learning: Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar Machine learning has become an integral part of many industries and applications in recent years. It is a subset of artificial intelligence that focuses on developing algorithms and techniques that enable computers to learn from data without being explicitly programmed. One of the most

Graph representation learning is a rapidly growing field in machine learning, with a large body of research and advancements being led by William L. Hamilton. Hamilton is a Canadian computer scientist, currently serving as an assistant professor at the School of Engineering and Applied Sciences at Harvard University. He received his PhD from Stanford University

Introduction to Hands-On Machine Learning with Scikit-Learn Keras and TensorFlow Hands-On Machine Learning with Scikit-Learn Keras and TensorFlow is a popular book written by Aurélien Géron. This book is a practical guide that covers all the essential topics you need to know in order to build and deploy machine learning models using Scikit-Learn, Keras, and

Bayesian reasoning and machine learning are two powerful tools that have revolutionized the field of artificial intelligence. At the forefront of this advancement is David Barber, a well-known computer scientist and author whose work has greatly contributed to the development of these fields. David Barber is a Professor of Applied Mathematics and Professor of Computer

What are Neural Networks? Neural networks are a type of machine learning algorithm modeled after the human brain. They are used to recognize patterns in data and make predictions based on those patterns. Each neural network is made up of layers of interconnected nodes, with each node representing a single feature or concept. The connections

Introduction to Reinforcement Learning – Marco Wiering and Martijn van Otterlo Reinforcement Learning (RL) is a powerful machine learning approach that has been gaining popularity in recent years due to its ability to learn and adapt to its environment without being explicitly programmed. This method has been successfully applied in diverse fields such as robotics,

The Hundred-Page Machine Learning Book, written by data scientist Andriy Burkov, is a compact and concise guide to understanding the fundamentals of machine learning. At just 250 pages, this book offers a condensed and accessible introduction to the complex world of machine learning. Burkov, who holds a PhD in Artificial Intelligence and has over a