AI/Machine Learning Inference Explained: A Beginner's Guide
AI/Machine Learning Inference Explained: A Beginner's Guide

Author: M.B. Chatfield

Publisher:

Published:

Total Pages: 91

ISBN-13:

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Unleash the Power of AI with Machine Learning Inference In today's data-driven world, artificial intelligence (AI) is rapidly transforming industries and reshaping our lives. At the heart of this revolution lies machine learning, which empowers computers to learn from vast amounts of data and make intelligent decisions without explicit programming. AI/Machine Learning Inference Explained: A Beginner's Guide is your comprehensive guide to understanding and implementing inference, the crucial process of applying machine learning models to real-world problems. Through clear explanations you'll gain a solid foundation in the principles and practices of inference, enabling you to: Grasp the fundamental concepts of machine learning and AI Discover the different types of machine learning models Understand the role of inference in machine learning Learn various inference techniques, including classification, regression, and anomaly detection Apply inference to solve real-world problems in various domains Whether you're a tech enthusiast, a data analyst, or a budding AI professional, this book will equip you with the knowledge and skills you need to harness the power of machine learning inference and make a meaningful impact in the AI landscape. Embrace the future of AI: Start learning machine learning inference today! #AIinference #MachineLearning #AI #FutureofTechnology #DataScience #ArtificialIntelligence #MachineLearningBook #AIbook #AIforBeginners #PredictiveAnalytics #AIInnovation #BigData #DeepLearning #TechTrends #DataDriven #LearnAI #AIApplications

AI Inference Explained: A Beginner's Guide to Understanding
AI Inference Explained: A Beginner's Guide to Understanding

Author: M.B. Chatfield

Publisher:

Published:

Total Pages: 104

ISBN-13:

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Demystify AI Inference and Unleash the Power of Machine Learning In the era of big data and artificial intelligence (AI), the ability to extract meaningful insights from vast amounts of information is crucial. AI inference, the process of applying machine learning models to real-world data, is at the heart of this transformative technology. AI Inference Explained: A Beginner's Guide to Understanding is your comprehensive guide to unlocking the power of AI inference. Through clear explanations, engaging examples, and hands-on exercises, you'll gain a solid foundation in the principles and practices of inference, enabling you to: Grasp the fundamental concepts of AI and machine learning Discover the different types of machine learning models Understand the role of inference in machine learning Learn various inference techniques, including classification, regression, and anomaly detection Apply inference to solve real-world problems in various domains Develop and implement machine learning inference models using popular Python libraries Whether you're a tech enthusiast, a data analyst, or a budding AI professional, this book will equip you with the knowledge and skills you need to navigate the world of AI inference and make a meaningful impact. Embrace the future of AI: Start learning machine learning inference today!

Advances in Financial Machine Learning
Advances in Financial Machine Learning

Author: Marcos Lopez de Prado

Publisher: John Wiley & Sons

Published: 2018-01-23

Total Pages: 395

ISBN-13: 1119482119

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Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Mathematics for Machine Learning
Mathematics for Machine Learning

Author: Marc Peter Deisenroth

Publisher: Cambridge University Press

Published: 2020-04-23

Total Pages: 392

ISBN-13: 1108569323

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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Interpretable Machine Learning
Interpretable Machine Learning

Author: Christoph Molnar

Publisher: Lulu.com

Published: 2020

Total Pages: 320

ISBN-13: 0244768528

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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

TinyML
TinyML

Author: Pete Warden

Publisher: O'Reilly Media

Published: 2019-12-16

Total Pages: 504

ISBN-13: 1492052019

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Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

Data Centric Artificial Intelligence: A Beginner’s Guide
Data Centric Artificial Intelligence: A Beginner’s Guide

Author: Parikshit N. Mahalle

Publisher: Springer Nature

Published: 2023-11-11

Total Pages: 137

ISBN-13: 9819963532

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This book discusses the best research roadmaps, strategies, and challenges in data-centric approach of artificial intelligence (AI) in various domains. It presents comparative studies of model-centric and data-centric AI. It also highlights different phases in data-centric approach and data-centric principles. The book presents prominent use cases of data-centric AI. It serves as a reference guide for researchers and practitioners in academia and industry.

AI and Machine Learning for Coders
AI and Machine Learning for Coders

Author: Laurence Moroney

Publisher: O'Reilly Media

Published: 2020-10-01

Total Pages: 393

ISBN-13: 1492078166

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If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

Introduction to Applied Linear Algebra
Introduction to Applied Linear Algebra

Author: Stephen Boyd

Publisher: Cambridge University Press

Published: 2018-06-07

Total Pages: 477

ISBN-13: 1316518965

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A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.