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Osmani Addy: Beyond Vibe Coding

Beyond Vibe Coding

Leveraging Your Experience in the Age of AI-Assisted Coding

Series On Intelligence Science

This popular science compendium provides an approximate, holistic but first ever solution to the four Holy Grail questions: How does a brain work? How does the brain learn? How does its consciousness arise? How does learning require consciousness?The volume explains how human brains require and learn consciousness and why the new AI will overcome the current lack of conscious learning algorithms in AI. For human societies, it suggests how governments can make their taxpayers safer, more prosper and happier. For future AI, it calls for a thorough investigation in scientific infrastructures like government and private funding agencies, publication venues, professional societies and administrators that evaluate research. Why did a conscious learning algorithm not have a healthy environment to study? Why could rampant misconducts of data deletion in deep learning be allowed to grossly exaggerate AI performances for so long? The author's real-life accounts reveal deep reasons.The useful reference text benefits laymen in all walks of life, as well as professionals, researchers, academics and students in any areas.

Conscious Learning: Humans And Machines

Malware detection is exceedingly challenging, requiring deeper expertise in two domains: machine learning (ML) and malware analysis.This comprehensive collection guides ML practitioners and researchers into the state-of-the-art and underlying insights needed to work in and research problems at the intersection of machine learning and cyber security, particularly around the problem of malware targeting the Windows platform.This useful volume's contributions come from a range of experts, representing large companies, major universities, and government labs, all working at the frontiers of malware detection and remediation research.

Machine Learning For Malware Detection

Understand How to Use Artificial Intelligence in Your HR Role

AI for People Professionals

You've experienced the shiny, point-and-click surface of your Linux computer—now dive below and explore its depths with the power of the command line. The Linux Command Line takes you from your very first terminal keystrokes to writing full programs in Bash, the most popular Linux shell (or command line). Along the way you'll learn the timeless skills handed down by generations of experienced, mouse-shunning gurus: file navigation, environment configuration, command chaining, pattern matching with regular expressions, and more. In addition to that practical knowledge, author William Shotts reveals the philosophy behind these tools and the rich heritage that your desktop Linux machine has inherited from Unix supercomputers of yore. As you make your way through the book's short, easily-digestible chapters, you'll learn how to: <ul><li>Create and delete files, directories, and symlinks</li><li>Administer your system, including networking, package installation, and process management</li><li>Use standard input and output, redirection, and pipelines</li><li>Edit files with Vi, the world's most popular text editor</li><li>Write shell scripts to automate common or boring tasks</li><li>Slice and dice text files with cut, paste, grep, patch, and sed</li></ul> Once you overcome your initial "shell shock," you'll find that the command line is a natural and expressive way to communicate with your computer. Just don't be surprised if your mouse starts to gather dust.

A Complete Introduction

The Linux Command Line, 3rd Edition

An Introduction to String Algorithms

Foundations, Technologies, and Prospects for the Future

Autonomous Vehicle Safety Solutions

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Deep Learning Crash Course

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: <ul> <li>A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes</li> <li>Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML</li> <li>Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis</li> </ul> Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

Tiny Machine Learning: Design Principles and Applications

Learn How to Code through Interactive Examples

Python Practice Lab

Machine Learning For Dummies

Modern Trends In Financial Engineering

Multiscale Financial Data Analytics and Machine Learning offers a systematic and comprehensive study on the multiscale approach to financial data analytics and machine learning. This book covers an array of multiscale methods to discover the properties of various timescales embedded in a financial time series, including noise-assisted empirical mode decomposition methods. Important interpretable multiscale outputs from the estimation are recognized as a new set of features that can be used for machine learning. The feature selection problem for machine learning is examined in this volume.This book offers an applied quantitative approach that combines novel analytical methodologies and practical applications to a wide array of examples with real-world data. It is self-contained and organized in its presentation. The explanations of the methodologies are both accessible and detailed enough to capture the interest of the curious student or researcher. Step-by-step descriptions of the algorithms are provided for straightforward implementation.

Multiscale Financial Data Analytics And Machine Learning

Principles, Algorithms, and Applications of the Free Energy Principle for Engineers

Fundamentals of Active Inference

Beyond Vibe Coding

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