[Book] How to Advance AI Machine Learning with Limited Data (No. 2269)
2269 AI and Machine Learning
【Available for preview】 ☆ No need for a large amount of training data! Data augmentation through generative AI, transfer learning, and multi-task learning!
Book Title: How to Advance AI and Machine Learning with Limited Data, and Develop Explainable AI －－－－－－－－－－－－－－－－－－－－－ ☆ Why did AI come to that answer! What are the grounds for judgment and reliability! －－－－－－－－－－－－－－－－－－－－－ ■ Key Points of Each Chapter Chapter 1 ★ Analytical methods based on types/models! ★ How to accurately measure model performance! Chapter 2 ★ Identifying truly necessary data and recognizing missing data! ★ The impact of data missingness on decision-making! Chapter 3 ★ How to properly document experimental records! ★ How to appropriately accumulate and manage research data! Chapter 4 ★ Simply repeating learning does not lead to improved prediction accuracy! ★ Data augmentation methods that take advantage of hallucinations in generative AI! Chapter 5 ★ Reducing development costs through shortened learning time, rapid model updates! ★ How to achieve advanced language processing capabilities through performance improvement!! Chapter 6 ★ Ensuring fairness, explainability, and transparency in AI and machine learning! Chapter 7 ★ Considerations when using data for machine learning! ★ To realize trustworthy AI!
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■ Table of Contents Chapter 1: Types and Methods of AI (Artificial Intelligence) / Machine Learning Chapter 2: Data Processing, Cleansing, and Feature Selection Chapter 3: Data Collection, Storage, and Database Construction Chapter 4: Methods for Training and Utilizing Machine Learning and AI with Limited Data Chapter 5: Improving Accuracy of Machine Learning and AI and Reducing Training Time Chapter 6: Explainable AI / Black Box Analysis Techniques and Their Implementation and Utilization in Business Chapter 7: Fairness, Quality Assurance, and Reliability Evaluation －－－－－－－－－－－－－－－－－－－－－ ● Publication Date: October 31, 2024 ● Format: A4, 389 pages ● Authors: 55 individuals ● ISBN: 978-4-86798-048-4 －－－－－－－－－－－－－－－－－－－－－
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