Can You Make An Ai With Python
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Artificial Intelligence Programming with Python
A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learning Expansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learning Practical AI and Python “cheat sheet” quick references This hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.
Practical Generative AI with Python
This book covers the fundamentals of generative AI, providing an in-depth understanding of key concepts, algorithms, and techniques that power AI-driven content creation. Starting with an introduction to the basics of generative AI, the book explains the theoretical foundations and evolution of generative models, highlighting the significance of this technology in various domains such as image synthesis, text generation, and more. Readers will explore the different types of machine learning, including supervised, unsupervised, and reinforcement learning, and understand their role in the development of generative models. The guide dives into essential Python libraries like TensorFlow, PyTorch, NumPy, and Pandas, offering a hands-on approach to building generative models from scratch. Each chapter is packed with practical examples, case studies, and real-world scenarios that demonstrate the application of these models in various fields, including art, music, and conversational AI. Key topics include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, autoregressive models, and transformer-based models like GPT. The book also addresses the ethical considerations surrounding generative AI, providing insights into the challenges of bias, fairness, and misinformation. Readers will benefit from step-by-step tutorials that guide them through the process of implementing and optimizing generative models, complete with code examples and hands-on exercises. Additionally, the book offers advanced techniques for improving model performance and stability, ensuring that readers are well-prepared to tackle complex AI projects. Whether you're a beginner looking to understand the basics of generative AI or an experienced developer aiming to enhance your skills, "Mastering Generative AI with Python: A Hands-On Guide" serves as an essential resource for anyone interested in the rapidly evolving field of generative AI.
PRACTICAL GUIDE TO ARTIFICIAL INTELLIGENCE FOR SECURE SOFTWARE SYSTEMS
Author: Virender Dhiman
language: en
Publisher: Xoffencer international book publication house
Release Date: 2024-07-04
There is no doubt that the world today is a lot different than it was fifty or even thirty years ago, from the standpoint of technology. Just imagine when we landed the first man on the moon back in 1969. All of the computers that were used at NASA were all mainframe computers, developed primarily by IBM and other related computer companies. These computers were very large and massive—in fact, they could even occupy an entire room. Even the computers that were used on the Saturn V rocket and in the Command and Lunar Excursion Modules were also of the mainframe type. Back then, even having just 5 MB of RAM memory in a small computer was a big thing. By today’s standards, the iPhone is lightyears away from this kind of computing technology, and in just this one device, we perhaps have enough computing power to send the same Saturn V rocket to the moon and back at least 100 times. But just think about it, all that was needed back then was just this size of memory. The concepts of the Cloud, virtualization, etc. were barely even heard of. The computers that were designed back then, for example, had just one specific purpose: to process the input and output instructions (also known as “I/O”) so that the spacecrafts could have a safe journey to the moon, land on it, and return safely back to Earth once again. Because of these limited needs (though considered to be rather gargantuan at the time), all that was needed was just that small amount of memory. But by today’s standards, given all of the applications that we have today, we need at least 1,000 times that much just to run the simplest of Cloud-based applications. But also back then, there was one concept that was not even heard of quite yet: Cybersecurity. In fact, even the term of “Cyber” was not even heard of. Most of the security issues back then revolved around physical security. Take, for example, NASA again. The main concern was only letting the authorized and legitimate employees into Mission Control. Who would have thought that back then there was even the slightest possibility that a Cyberattacked could literally take over control of the computers and even potentially steer the Saturn V rocket away from its planned trajectory