Data Science & AI - Technology for Creating Value from Data

This book clearly explains why statistics is the most reliable weapon in business and scientific decision-making. You can learn the importance of data-based judgment and how statistical thinking changes our perception through specific examples. Even without expertise in statistics, you can understand its essential value and applications.

• Statistics is the most reliable method for correctly discerning cause and effect
• Sampling and randomized controlled trials form the foundation of scientific evidence
• Statistics reveals biases that cannot be detected by intuition or experience alone
• Data literacy is an essential skill for surviving in modern society

統計学が最強の学問である

西内 啓 / ダイヤモンド社 / 2013-01-24

ーーーーーーーーーーーーーーーーー ビジネス書大賞(2014...

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This book is an introductory text that allows you to fundamentally understand how deep learning works by implementing it from scratch in Python without relying on external libraries. By learning while actually writing code rather than just theory, you gain understanding of the essence of neural networks. It's ideal for those who want to understand the internal structure of deep learning, which tends to become a black box, while working hands-on.

• The essence of deep learning becomes visible by building from scratch without using libraries
• You can intuitively understand how backpropagation works through implementation
• You can build convolutional neural network structures step by step
• You can solidify the foundations of deep learning from both theoretical and implementation perspectives

ゼロから作るDeep Learning ―Pythonで学ぶディープラーニングの理論と実装

斎藤康毅 / オライリージャパン / 2016-09-24

ディープラーニングの本格的な入門書。外部のライブラリに頼らず...

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This book systematically compiles practical data analysis techniques by authors who have achieved top rankings in Kaggle data analysis competitions. It is rich with knowledge that can be used directly in practice, including feature engineering and model evaluation/validation. You can learn specific know-how for getting results by actually working, not just theory.

• Feature engineering greatly affects model performance
• Proper validation strategy is key to preventing leakage and improving generalization performance
• Ensemble learning combines the strengths of multiple models
• Acquire practical techniques cultivated in actual competitions

Kaggleで勝つデータ分析の技術

門脇 大輔, 阪田 隆司, 保坂 桂佑, 平松 雄司 / 技術評論社 / 2019-10-09

Kaggleをはじめよう! データサイエンスの認知の高まりと...

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This book is a practical textbook that comprehensively covers machine learning theory and implementation. It covers a wide range of topics from classification and regression problems to deep learning and reinforcement learning, explaining implementation methods using scikit-learn and TensorFlow in detail. You can develop the ability to write working code while grasping the overall picture of machine learning.

• Learn the mathematical background and implementation of machine learning algorithms simultaneously
• Acquire practical skills in using scikit-learn and TensorFlow
• Covers the entire picture of machine learning including deep learning and reinforcement learning
• Applied skills are developed by writing code after understanding the theory

[第3版]Python機械学習プログラミング 達人データサイエンティストによる理論と実践 (impress top gear)

Sebastian Raschka, Vahid Mirjalili, 福島真太朗, 株式会社クイープ / インプレス / 2020-10-22

世界各国で翻訳された 機械学習本ベストセラーの第3版! 分類...

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