National Security Agency encryption devices
The TSEC/KL-7, also known as Adonis was an off-line non-reciprocal rotor encryption machine. The KL-7 had rotors to encrypt the text, most of which moved in a complex pattern, controlled by notched rings. The non-moving rotor was fourth from the left of the stack. The KL-7 also encrypted the message indicator. (Wikipedia).
From playlist Music.
From playlist 708 hw 3
查看本系列视频中的其他几个视频： Part 1 – 导入数据: https://youtu.be/lyaIDHyOlFc Part 2 – 预处理数据: https://youtu.be/ais_Oj6Fx-E Part 3 – 分析数据: https://youtu.be/I1EQx3O2qCk Part 4 -可视化数据: https://youtu.be/rLeMOEWkoa8 Part 5 – 模型预测: https://youtu.be/BBtnVjVYI2k Part 6 – 大数据扩展: https://youtu.be/xGzMMkFURb8 Part 7
From playlist MATLAB 数据科学系列视频
查看本系列视频中的其他几个视频： Part 1 – 导入数据: https://youtu.be/lyaIDHyOlFc Part 2 – 预处理数据: https://youtu.be/ais_Oj6Fx-E Part 3 – 分析数据: https://youtu.be/I1EQx3O2qCk Part 4 -可视化数据: https://youtu.be/rLeMOEWkoa8 Part 5 – 模型预测: https://youtu.be/BBtnVjVYI2k Part 6 – 大数据扩展: https://youtu.be/xGzMMkFURb8 Part 7
From playlist MATLAB 数据科学系列视频
Many animations used in this video came from Jonathan Barron [1, 2]. Give this researcher a like for his hard work! SUBSCRIBE FOR MORE CONTENT! RESEOURCES [1] Paper on adaptive loss function: https://arxiv.org/abs/1701.03077 [2] CVPR paper presentation: https://www.youtube.com/watch?v=Bm
From playlist Deep Learning 101
The KL Divergence : Data Science Basics
understanding how to measure the difference between two distributions Proof that KL Divergence is non-negative : https://www.youtube.com/watch?v=LOwj7UxQwJ0&t=520s My Patreon : https://www.patreon.com/user?u=49277905 0:00 How to Learn Math 1:57 Motivation for P(x) / Q(x) 7:21 Motivation
From playlist Data Science Basics
Linear equation using segment | Geometry | 8th grade | Khan Academy
Well use some algebraic skills to add segments in this example. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-eighth-grade-math/cc-8th-geometry/line-segment-algebra/e/segment_addition?utm_source=YT&utm_medium=Desc&utm_campaign=8thgrade Wa
From playlist Geometry | 8th Grade | Khan Academy
Identifying similar triangles in the coordinate plane | Analytic geometry | Geometry | Khan Academy
Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/geometry/analytic-geometry-topic/geometry-problems-coordinate-pla/e/geometry-problems-on-the-coordinate-plane?utm_source=YT&utm_medium=Desc&utm_campaign=Geometry Watch the next lesson: https://ww
From playlist Analytic geometry | Geometry | Khan Academy
VQ-VAEs: Neural Discrete Representation Learning | Paper + PyTorch Code Explained
❤️ Become The AI Epiphany Patreon ❤️ ► https://www.patreon.com/theaiepiphany In this video I cover VQ-VAEs papers: 1) Neural Discrete Representation Learning 2) Generating Diverse High-Fidelity Images with VQ-VAE-2 (the only difference is the existence of a hierarchical structure of laten
From playlist VAEs
Deep InfoMax: Learning deep representations by mutual information estimation and maximization | AISC
For more details including paper and slides, visit https://aisc.a-i.science/events/2019-04-11/ Discussion lead/coauthor: Karan Grewal Abstract Building agents to interact with the web would allow for significant improvements in knowledge understanding and representation learning. Howev
From playlist Natural Language Processing
Donald Cartwright : Construction of lattices defining fake projective planes - lecture 1
Recording during the meeting "Ball Quotient Surfaces and Lattices " the February 25, 2019 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Ma
From playlist Algebraic and Complex Geometry
Data around us, like images and documents, are very high dimensional. Autoencoders can learn a simpler representation of it. This representation can be used in many ways: - fast data transfers across a network - Self driving cars (Semantic Segmentation) - Neural Inpainting: Completing sect
From playlist Algorithms and Concepts
Lecture 20: Periodic Lattices Part 1
MIT 8.04 Quantum Physics I, Spring 2013 View the complete course: http://ocw.mit.edu/8-04S13 Instructor: Allan Adams In this lecture, Prof. Adams discusses the energy structure and wave functions under a periodic potential. The energy band structure is derived for a periodic delta potenti
From playlist 8.04 Quantum Physics I - Prof. Allan Adams
V8-11: Eigenvalue problems example with Neumann Boundary Conditions; Elementary Differential eqns
Eigenvalue problems Second example with Neumann Boundary Conditions; Elementary Differential equations Course playlist: https://www.youtube.com/playlist?list=PLbxFfU5GKZz0GbSSFMjZQyZtCq-0ol_jD This project was created with Explain Everything™ Interactive Whiteboard for iPad. 00:00 Sl
From playlist Elementary Differential Equations
卡尔曼滤波器是一种优化估算算法，在不确定和间接测量的情况下估算系统状态。 观看视频示例，了解卡尔曼滤波器背后的工作原理。本视频介绍卡尔曼滤波器结合两个信息源，预测状态和噪声测量，以产生最佳的，无偏的状态估计。 使用 MATLAB 和 Simulink 设计和使用卡尔曼滤波器：https://bit.ly/2GXwjxG 了解 Control System Toolbox：https://bit.ly/2BWJECb 获取免费试用版，30 天探索触手可及：https://bit.ly/2IPvqcc 观看更多 MATLAB 和 Simulink 入门视频：https://
From playlist 卡尔曼滤波器（Kalman Filters）