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Deep Learning and Physics
What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machi...
Putting Ourselves Back in the Equation
Distant galaxies, dark matter, black holes – elusive, incomprehensible and inhospitable – these are the building blocks of modern physics. But where do we fit in this picture? 'A delightful account of one of the deepest and most fascinating explorations going on today.' CARLO ROVELLI, AUTHOR OF WHITE HOLES For centuries, we have separated mind from matter. While physicists have pursued a theory of ‘everything’ with single-minded purpose, the matter of the mind, of human consciousness, has been conveniently sidestepped and ignored – consigned to priests, philosophers and poets. With the ambition of Stephen Hawking, Carlo Rovelli and Brian Cox, Putting Ourselves Back in the Equation ...
Proceedings Of The East Asia Joint Symposium On Fields And Strings 2021
This volume contains the proceedings of the East Asia Joint Symposium on Fields and Strings 2021, held at the Media Center of Osaka City University on November 22-27, 2021. About 160 physicists from all over East Asia attended physically or joined online this symposium and more than 50 researchers presented their results in the invited lectures, the short talks or the poster session. Quantum field theory and string theory in the context of several exciting developments were discussed, which include frontiers of supersymmetric gauge theory, anomalies and higher form symmetries, and several issues on quantum gravity and black holes.
Thinking About Statistics
Simply stated, this book bridges the gap between statistics and philosophy. It does this by delineating the conceptual cores of various statistical methodologies (Bayesian/frequentist statistics, model selection, machine learning, causal inference, etc.) and drawing out their philosophical implications. Portraying statistical inference as an epistemic endeavor to justify hypotheses about a probabilistic model of a given empirical problem, the book explains the role of ontological, semantic, and epistemological assumptions that make such inductive inference possible. From this perspective, various statistical methodologies are characterized by their epistemological nature: Bayesian statistics...
人工知能 Vol.36 No.6（2021年11月号）
話題の人工知能の粋を集めた学会誌 学会誌人工知能に関する専門家からのさまざまな研究結果、レポートを載せ、この分野における最新の情報を掲載しています。 ★このような方におすすめ 人工知能に関心のある一般の方、人工知能の研究者 ●主要目次● 巻頭言 特集：「若手研究者による2050 年の未来予測～ムーンショット型研究開発 ミレニア・プログラムより～」 特集：「2021 年度人工知能学会全国大会（第35 回）」 アーティクル：情報解析と著作権──「機械学習パラダイス」としての日本 論文 レクチャーシリーズ：「AI 哲学マップ」〔第5回〕 私のブックマーク 学生フォーラム〔第108回〕 会議報告 書評 アーティクル：表紙解説
