AI Algorithms
Foundations, Applications, and AdvancementsBy Anand Vemula
Length1h 29m
About this audiobook
This comprehensive volume offers an in-depth exploration of artificial intelligence algorithms, structured into five core parts. Beginning with foundational concepts, it introduces symbolic and statistical AI, emphasizing mathematical underpinnings such as linear algebra, probability, and optimization. Classical AI techniques like search algorithms and constraint satisfaction are explored in depth before transitioning into the domain of machine learning.
In supervised and unsupervised learning chapters, readers gain insights into regression, classification, clustering, and dimensionality reduction. More advanced topics such as ensemble methods, neural networks—including CNNs, RNNs, and transformers—are detailed with practical and theoretical rigor. Reinforcement learning is examined through frameworks like MDPs, Q-learning, and policy gradients.
The book further delves into evolutionary and probabilistic algorithms, detailing genetic strategies, swarm intelligence, Bayesian networks, and Monte Carlo methods. Applications in natural language processing and computer vision—covering chatbots, object detection, and GANs—are presented with modern techniques like AutoML, neural architecture search, and transfer learning.
A dedicated section on applications and ethics discusses real-world AI use in healthcare, finance, and robotics, along with the challenges of bias, explainability, and governance. Finally, the book explores future directions: the quest for AGI, the promise of quantum AI, and the transformative impact of AI on labor and society.
Balancing technical depth with clarity, this book serves as a valuable resource for students, practitioners, and researchers seeking a robust understanding of both the fundamentals and frontiers of AI.
Audiobook details
GenreEducation and Learning, Self-Help
Length1 hr 29 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateMay 28, 2025
LanguageEnglish
Table of contents
1Chapter 1: Introduction to Artificial Intelligence
2Chapter 2: Mathematical and Statistical Background
3Chapter 3: Search and Problem Solving
4Chapter 4: Supervised Learning
5Chapter 5: Unsupervised Learning
Show all chaptersShow less
6Chapter 6: Ensemble Methods
7Chapter 7: Neural Networks and Deep Learning
8Chapter 8: Reinforcement Learning
9Chapter 9: Evolutionary Algorithms
10Chapter 10: Probabilistic Models
11Chapter 11: AI in Natural Language Processing
12Chapter 12: AI for Computer Vision
13Chapter 13: AutoML and Meta-Learning
14Chapter 14: AI in Industry
15Chapter 15: Ethics, Fairness, and Interpretability
16Chapter 16: Future Directions
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