[Announcements] New AI Book with a Chapter on Making Lighter AI for Edge Devices

[Reza Rawassizadeh]

*** New AI Book with a Chapter on Making Lighter AI for Edge Devices ****

Mastering AI, machine learning, and data science often means piecing
together concepts scattered across countless resources, from
statistics and visualizations to foundational models and large
language models. This book, the result of eight years of effort,
brings it all together in one accessible, engaging package. It
clarifies artificial intelligence and data science, blending core
mathematical principles with a clear, reader-friendly approach.
Unlike traditional textbooks that lean heavily on equations and
mathematical formalization, the author starts with minimal
prerequisites, layering deeper math as the reader progresses. Each
concept, algorithm, or model is unpacked through clear, hands-on
examples that build the reader's skills step by step. It strikes a
balance between theoretical foundations and practical application,
serving as both an academic reference and a practical guide.

The book uses humor, casual language, and comics to make the
challenging concepts and topics relatable and fun.

Here is the Amazon link for the book: https://www.amazon.com/dp/B0DV3WWMBD

Table of Contents
        Part I: Introduction & Preliminary Requirements
                Chapter 1: Basic Concepts
                Chapter 2: Visualization
                Chapter 3: Probability and Statistics

        Part II: Unsupervised Learning
                Chapter 4: Clustering
                Chapter 5: Frequent Itemset, Sequence Mining and
Information Retrieval

        Part III: Data Engineering
                Chapter 6: Feature Engineering
                Chapter 7: Dimensionality Reduction and Data Decomposition

        Part IV: Supervised Learning
                Chapter 8: Regression Analysis
                Chapter 9: Classification

        Part V: Neural Network
                Chapter 10: Neural Networks and Deep Learning
                Chapter 11: Self-Supervised Deep Learning
                Chapter 12: Deep Learning Models and Applications
(Text, Vision, and Audio)

        Part VI: Reinforcement Learning
                Chapter 13: Reinforcement Learning

        Part VII: Other Algorithms and Concepts
                Chapter 14: Making Lighter Neural Network and Machine
Learning Models <— Focus on Edge AI
                Chapter 15: Graph Mining Algorithms
                Chapter 16: Concepts and Challenges of Working with Data
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