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A Quick Introduction to the Chow Liu Algorithm
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Jee Vang, Ph.D.
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サルでもわかるディープラーニング入門 (2017年) 人工知能に関しては何もわからないレベルから始めて 最後まで偏微分方程式とかさっぱりわからない、という状態で ディープラーニングの解説をしてみました ※初出 20170121 ※3分でわかるディープラーニング、を加筆(20170122) ※なぜディープラーニングが有効になったか、を加筆、TensorFlow playgroundをみて「クリックできればできる」は言い過ぎなのでその部分を訂正(20170123) ※ニューラルネットぽい概念図を加筆(20170124) ※「勝利の方程式』スライド加筆 (20170125) ※「問題解決の3段階」加筆 (20170126) ※「学習モデルをだます例」加筆 (20170301) A1701talk deep-learning-introduction-170301(20170301)
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第16回Creators MeetUp http://atnd.org/events/50388 技術的内容に対して「それは違うよ!」を激しく指摘することをマサカリを投げると例えてよく言われます。彼らの指摘は厳しく、とても厳しく、そんな言い方しなくても!ってくらい厳しい。そんなマサカリの受け方をレクチャーしちゃいますっ! ▼後日談ブログ記事 【祝ホットエントリー】「マサカリを受け止める心得」ってスライドを公開した後日談。やっぱ発信をやめたくはないなあと思えた。 http://www.rechiba3.net/entry/masakariafter/
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A Quick Introduction to the Chow Liu Algorithm
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素人がDeep Learningと他の機械学習の性能を比較してみた
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サルでもわかるディープラーニング入門 (2017年) (In Japanese)
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In biological research, scientists often need to use the information of the species to infer the evolutionary relationship among them. The evolutionary relationships are generally represented by a labeled binary tree, called the evolutionary tree (or phylogenetic tree). The phylogeny problem is computationally intensive, and thus it is suitable for parallel computing environment. In this paper, a fast algorithm for constructing Neighbor-Joining phylogenetic trees has been developed. The CPU time is drastically reduced as compared with sequential algorithms. The new algorithm includes three techniques: Firstly, a linear array A[N] is introduced to store the sum of every row of the distance matrix (the same as SK), which can eliminate many repeated (redundancy) computations, and the value of A[i] are computed only once at the beginning of the algorithm, and are updated by three elements in the iteration. Secondly, a very compact formula for the sum of all the branch lengths of OTUs (Operational Taxonomic Units) i and j has been designed. Thirdly, multiple parallel threads are used for computation of nearest neighboring pair.
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In this paper are proved a few properties about convergent sequences into a real 2-normed space ( ,|| , ||) L and into a 2-pre-Hilbert space ( ,( , | )) L , which are actually generalization of appropriate properties of convergent sequences into a pre-Hilbert space. Also, are given two characterization of a 2-Banach spaces. These characterization in fact are generalization of appropriate results in Banach spaces. 2010 Mathematics Subject Classification: Primary 46B20; Secondary 46C05
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As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
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A Quick Introduction
to the Chow-Liu Algorithm Jee Vang, Ph.D. [email_address]
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