Practical Mathematics for AI and Deep Learning

Mathematical Codebook to Navigate Through the Fast-changing AI LandscapeDescription To construct a system that may be referred to as having ‘Artificial Intelligence’ it is important to develop the capacity to design algorithms capable of performing data-based automated decision-making in conditions of uncertainty. Now to accomplish this goal one needs to have an in-depth understanding of the more sophisticated components of linear algebra vector calculus probability and statistics. This book walks you through every mathematical algorithm as well as its architecture its operation and its design.. This book will teach you the common terminologies used in artificial intelligence such as models data parameters of models and dependent and independent variables. The Bayesian linear regression the Gaussian mixture model the stochastic gradient descent and the backpropagation algorithms are explored with implementation beginning from scratch. The vast majority of the sophisticated mathematics required for complicated AI computations such as autoregressive models cycle GANs and CNN optimization are explained and compared. What you will learn ● Learn to think like a professional data scientist by picking the best-performing AI algorithms. ● Expand your mathematical horizons to include the most cutting-edge AI methods. ● Explore several neural network designs as a starting point for constructing your own NLP and Computer Vision architecture. Who this book is for Everyone interested in AI and its computational foundations including machine learning data science deep learning computer vision and natural language processing both researchers and professionals will find this book to be an excellent companion. This book can be useful as a quick reference for practitioners who already use a variety of mathematical topics but do not completely understand the underlying principles.