Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems (Paperback)

Many industries are eager to integrate AI and data-driven technologies into their systems and operations. But to build truly successful AI systems, you need a firm grasp of the underlying mathematics. This comprehensive guide bridges the gap in presentation between the potential and applications of AI and its relevant mathematical foundations. 
In an immersive and conversational style, the book surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-of-the-art models, rather than on dense academic theory. You'll explore topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more within an exclusive AI context geared toward computer vision, natural language processing, generative models, reinforcement learning, operations research, and automated systems. With a broad audience in mind, including engineers, data scientists, mathematicians, scientists, and people early in their careers, the book helps build a solid foundation for success in the AI and math fields. 
You'll be able to:
 

- Comfortably speak the languages of AI, machine learning, data science, and mathematics
- Unify machine learning models and natural language models under one mathematical structure
- Handle graph and network data with ease
- Explore real data, visualize space transformations, reduce dimensions, and process images
- Decide on which models to use for different data-driven projects
- Explore the various implications and limitations of AI

許多行業都渴望將人工智慧和數據驅動技術融入其系統和運營中。但要建立真正成功的人工智慧系統，您需要對底層數學有牢固的掌握。這本全面的指南填補了人工智慧潛力和應用之間的呈現差距，並介紹了相關的數學基礎。

本書以沉浸式和對話式風格，概述了在人工智慧領域中取得成功所需的數學知識，重點放在實際應用和最新模型上，而不是密集的學術理論。您將在專注於計算機視覺、自然語言處理、生成模型、強化學習、運營研究和自動化系統的獨特人工智慧背景下，探索回歸、神經網絡、卷積、優化、概率、圖形、隨機遊走、馬爾可夫過程、微分方程等主題。本書針對工程師、數據科學家、數學家、科學家和初入職場的人士等廣泛的讀者群體，幫助他們在人工智慧和數學領域建立堅實的基礎。

您將能夠：
- 舒適地使用人工智慧、機器學習、數據科學和數學的語言
- 在一個數學結構下統一機器學習模型和自然語言模型
- 輕鬆處理圖形和網絡數據
- 探索真實數據，可視化空間轉換，降低維度和處理圖像
- 選擇在不同數據驅動項目中使用的模型
- 探索人工智慧的各種影響和限制