Python Debugging For Ai Machine Learning And Cloud Computing
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Python Debugging for AI, Machine Learning, and Cloud Computing
This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior. The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you'll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You'll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performance degradation. This includes tracing, logging, and analyziing memory dumps using native WinDbg and GDB debuggers. Upon completing this book, you will have the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications. You will: Employ a pattern-oriented approach to Python debugging that starts with diagnostics of common software problems Use tips and tricks to get the most out of popular IDEs, notebooks, and command-line Python debugging Understand Python internals for interfacing with operating systems and external modules Perform Python memory dump analysis, tracing, and logging.
Artificial Intelligence and Security
The 4-volume set LNCS 11632 until LNCS 11635 constitutes the refereed proceedings of the 5th International Conference on Artificial Intelligence and Security, ICAIS 2019, which was held in New York, USA, in July 2019. The conference was formerly called “International Conference on Cloud Computing and Security” with the acronym ICCCS. The total of 230 full papers presented in this 4-volume proceedings was carefully reviewed and selected from 1529 submissions. The papers were organized in topical sections as follows: Part I: cloud computing; Part II: artificial intelligence; big data; and cloud computing and security; Part III: cloud computing and security; information hiding; IoT security; multimedia forensics; and encryption and cybersecurity; Part IV: encryption and cybersecurity.
TEXT BOOK OF ARTIFICIAL INTELLIGENCE
Author: Dr. Rakesh Singh, Dr. Shuchi Dave, Prof. Sushil K. Kashaw, Prof. (Dr.) Sandeep Gangrade, Lalbihari Barik
language: en
Publisher: Shashwat Publication
Release Date: 2025-04-25
Textbook of Artificial Intelligence is a comprehensive guide for students, educators, and professionals seeking foundational and advanced knowledge in AI. It begins with a clear definition and history of Artificial Intelligence, helping readers understand its roots and evolution. The book explores real-world applications of AI across various industries including healthcare, finance, education, and autonomous systems. Core AI branches like Machine Learning, Deep Learning, NLP, Robotics, and Computer Vision are introduced with practical insights. In-depth coverage of Intelligent Agents explains their structure, types, and operating environments. The Problem Solving section walks readers through classic algorithms like BFS, DFS, A*, and adversarial search techniques. Knowledge Representation and Reasoning introduces propositional logic, predicate logic, semantic nets, and uncertainty models like Bayesian networks. Machine Learning fundamentals cover supervised, unsupervised, and reinforcement learning, alongside key algorithms and neural networks. Advanced topics like CNNs, RNNs, Transformers, GANs, and NLP tasks are well-structured for deeper understanding. Dedicated chapters on AI in real-world applications showcase use cases in robotics, vision, and recommender systems. Hands-on tools like TensorFlow, PyTorch, Keras, and data handling with Pandas and NumPy are introduced for practical learning. The book encourages ethical thinking with discussions on AI fairness, privacy, transparency, and regulation. A special focus on the future of AI covers trends like generative models, autonomous agents, and human-AI collaboration. Well-organized content helps learners connect theory to practical implementation and innovation. Step-by-step examples and algorithm breakdowns make complex topics easy to understand. Each chapter includes conceptual summaries, illustrations, and review questions for better retention. Perfect for beginners and intermediate learners, as well as educators designing AI curricula. Prepares students for research and industry careers with real-world insights and project ideas. Bridges the gap between traditional AI principles and modern AI technologies. A valuable reference for anyone passionate about building intelligent systems and exploring the world of AI.