Table of Contents: 00:11 - Last topic of Optimization for Vectorizatioin 00:28 - Example for Strip-mining 00:35 - Histogram calculation 00:52 - http://colfaxresearch.com/?p=709 00:55 - Function parameters 01:27 - Histogram explained 01:47 - Code example for Histogram calculation 02:05 - Potential SIMD vectorization 02:31 - Data dependency 02:40 - Loop cannot be vectorized 03:03 - Strip-Mining method 03:28 - Strip-Mined version of the histogram code 03:35 - Outer loop 03:43 - Vectorised inner loop 03:56 - Separate loop with data dependence 04:02 - Why is this implementation better? 04:25 - Result of automatic vectorization by the compiler 04:39 - https://youtu.be/hyZMssi_gZY?t=39m50s 04:45 - Strength reduction optimization technique 05:06 - Performance results
Views: 1324 Vadim Karpusenko
Video lecture about strip-mining optimization technique for vectorization of histogram calculation. Table of Contents: 00:15 - Previous trainings 00:56 - Problem Statement 01:16 - Simple Histogram calculation 02:37 - Data parallelism 04:10 - Optimized code 05:39 - Strength reduction 06:07 - Performance results 07:16 - About next section
Views: 1417 Vadim Karpusenko
Topics Covered: - Vector Processors - Pipelined Functional Units, Lanes, Vector Chaining - Code Examples: Stripmining, Masking, Vector Reductions, Scatter/Gather - Multimedia Extensions (SIMD Extensions) SUBSCRIBE! https://www.youtube.com/channel/UCRZQvLnnlkJ0vHXGwjNsfNw?sub_confirmation=1 Most of lecture material is derived from freely available course i.e. UC Berkeley course CS 152 https://inst.eecs.berkeley.edu/~cs152/sp12/ This course was taught at Abasyn University Islamabad, Summer 2016 http://www.abasynisb.edu.pk/
Views: 2613 Renzym Education
Table of Contents: 00:11 - Problem statement: matrix-vector multiplication 00:36 - Naive implementation of matrix-vector multiplication 01:20 - Why temporal locality is important for vector b? 01:40 - Problem with naive implementation 02:04 - Loop tiled code implementation for matrix-vector multiplication 02:34 - Code explanation 02:52 - Complications of new implementation 03:05 - Parallel reduction 03:20 - Strip-mining, parallel reduction, alignment and compiler hints 03:26 - Next optimization step 04:13 - New implementation - expanding parallel iterations space 04:18 - Double tiling 04:31 - iTile is imperical 04:45 - Orders of the tiled loops 04:56 - Choosing tiling optimization parameter 05:44 - Performance results for matrix-vector multiplication 06:32 - Cache-oblivious method results
Views: 5428 Vadim Karpusenko
Table of Contents: 00:07 - Optimization of Vectorization 00:29 - Please leave you question and comments 00:55 - Unit-stride access to data 01:02 - Load/Store operations on vector registers 01:27 - Scattered data vs contiguous structure 01:48 - Example: Coulomb's Law 01:52 - Problem statement 02:11 - Potential for vectorization 02:30 - Naive code implementation 02:44 - Code explanation 03:01 - Inafficient vectorization 03:37 - SoA vs AoS 03:56 - Advantage of unit-stride access in SoA 04:13 - Performance results 04:19 - Explaining results 04:46 - Baseline performance 04:51 - Optimized performance 05:04 - Performance with relaxed precision 05:17 - Important optimization, but may be difficult to implement 05:43 - New applications: think about data structures beforehand! 05:51 - Final words
Views: 438 Vadim Karpusenko
Learn about arrays, strings, and sorting algorithms and how they work in the C programming language. This course teaches the foundations of computer science. This video is lecture 2 of Harvard University's CS50 2018 course (part 3 since the lectures start at 0). Check out our full CS50 playlist: https://www.youtube.com/playlist?list=PLWKjhJtqVAbmGw5fN5BQlwuug-8bDmabi 🔗Notes: https://cs50.harvard.edu/college/weeks/2/notes/ 🔗Problem Set: https://cs50.harvard.edu/college/psets/2/ 🔗Source Code: https://cdn.cs50.net/2018/fall/lectures/2/src2/ ⭐️Contents⭐️ ⌨️ (00:00:00) Introduction ⌨️ (00:00:54) Week 1 Recap ⌨️ (00:04:47) Preprocessing ⌨️ (00:07:05) Compiling ⌨️ (00:09:01) Assembling ⌨️ (00:09:29) Linking ⌨️ (00:12:36) buggy0.c ⌨️ (00:16:13) buggy2.c ⌨️ (00:25:14) Debugging Tools ⌨️ (00:26:02) RAM ⌨️ (00:29:11) Arrays ⌨️ (00:30:01) scores0.c ⌨️ (00:41:47) scores2.c ⌨️ (00:49:45) scores4.c ⌨️ (00:52:21) string0.c ⌨️ (01:00:42) Null Terminator ⌨️ (01:03:06) strlen.c ⌨️ (01:06:16) ascii0.c ⌨️ (01:09:39) capitalize0.c ⌨️ (01:12:23) capitalize1.c ⌨️ (01:16:38) argv0.c ⌨️ (01:21:25) argv1.c ⌨️ (01:24:52) Ciphering ⌨️ (01:33:15) exit.c ⌨️ (01:36:58) Finding 50 ⌨️ (01:40:38) Sorting on Stage ⌨️ (01:50:27) Bubble Sort ⌨️ (01:51:34) Selection Sort ⌨️ (01:52:23) Computational Complexity ⌨️ (01:57:42) Merge Sort ⌨️ (02:04:29) Comparing Sorts Visually Lecture taught by David J. Malan. Thanks to Harvard's CS50 for giving us permission to post this lecture. Checkout their YouTube channel for more great lectures: https://www.youtube.com/cs50 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 21177 freeCodeCamp.org
This talk discards hand-wavy pop-science metaphors and answers a simple question: from a computer science perspective, how can a quantum computer outperform a classical computer? Attendees will learn the following: - Representing computation with basic linear algebra (matrices and vectors) - The computational workings of qbits, superposition, and quantum logic gates - Solving the Deutsch oracle problem: the simplest problem where a quantum computer outperforms classical methods - Bonus topics: quantum entanglement and teleportation The talk concludes with a live demonstration of quantum entanglement on a real-world quantum computer, and a demo of the Deutsch oracle problem implemented in Q# with the Microsoft Quantum Development Kit. This talk assumes no prerequisite knowledge, although comfort with basic linear algebra (matrices, vectors, matrix multiplication) will ease understanding. See more at https://www.microsoft.com/en-us/research/video/quantum-computing-computer-scientists/
Views: 172059 Microsoft Research
Download LeFlow at: https://github.com/danielholanda/LeFlow
Views: 1028 Daniel Holanda Noronha
Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 100280 Siraj Raval
Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 93527 Siraj Raval
Space filling curves, turning visual information into audio information, and the connection between infinite and finite math (this is a reupload of an older video which had much worse audio). Supplement with more space-filling curve fun: https://youtu.be/RU0wScIj36o For more information on sight-via sound, this paper involving rewiring a ferret's retinas to its auditory cortex is particularly thought-provoking: http://phy.ucsf.edu/~houde/coleman/sur2.pdf Alternatively, here the NYT summary: https://goo.gl/qNuc14 Also, check out this excellent podcast on Human echolocation: https://goo.gl/23f4Yh For anyone curious to read more about the connections between infinite and finite math, consider this Terry Tao blog post: https://goo.gl/NZ4yrW Lion photo by Kevin Pluck Music by Vincent Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brownm/r/3Blue1Brown
Views: 565195 3Blue1Brown
SIGGRAPH 2015 Technical Paper Video Project Page: http://www.graphics.stanford.edu/~niessner/zollhoefer2015shading.html We present a novel method to obtain fine-scale detail in 3D reconstructions generated with low-budget RGB-D cameras or other commodity scanning devices. As the depth data of these sensors is noisy, truncated signed distance fields are typically used to regularize out the noise, which unfortunately leads to over-smoothed results. In our approach, we leverage RGB data to refine these reconstructions through shading cues, as color input is typically of much higher resolution than the depth data. As a result, we obtain reconstructions with high geometric detail, far beyond the depth resolution of the camera itself. Our core contribution is shading-based refinement directly on the implicit surface representation, which is generated from globally-aligned RGB-D images. We formulate the inverse shading problem on the volumetric distance field, and present a novel objective function which jointly optimizes for fine-scale surface geometry and spatially-varying surface reflectance. In order to enable the efficient reconstruction of sub-millimeter detail, we store and process our surface using a sparse voxel hashing scheme which we augment by introducing a grid hierarchy. A tailored GPU-based Gauss-Newton solver enables us to refine large shape models to previously unseen resolution within only a few seconds.
Views: 5348 Matthias Niessner
Michael Wong (IBM Canada and CEO of OpenMP ARB), presented at Supercomputing 14, November 2014.
Views: 436 OpenMP
Lecture 17 looks at solving language, efficient tree-recursive models SPINN and SNLI, as well as research highlight "Learning to compose for QA." Also covered are interlude pointer/copying models and sub-word and character-based models. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Views: 14592 Stanford University School of Engineering
Views: 34573 The Coding Train
You have no shortage of great ideas. Our goal is to help you bring those ideas to reality as beautifully and easily as possible. At the open MAX Keynote, the Adobe Creative Cloud team will unveil hundreds of new tools, features, and innovations that will accelerate your work, liberate your creativity, and drive new mediums.
Views: 144264 Adobe Creative Cloud
How do you read 100,000 documents? The connection between the words we use and things and ideas that they represent can be represented as a structure. Using Neo4j this linguistic and semantic structure is developed to facilitate the large-scale analysis of text for meaning representation and automatic reading at scale. Learn how natural language processing can be implemented within Neo4j at scale to reveal actionable insights. Also, see how these structures are visualized in virtual reality. Speaker: Ryan Chandler Location: GraphConnect NYC 2017
Views: 1312 Neo4j
Buy MATLAB Books (affiliate): Digital Image Processing Using MATLAB https://amzn.to/2oH4Xkd A Guide To Matlab: For Beginners And Experienced Users https://amzn.to/2CmdUJr MATLAB and its Applications in Engineering https://amzn.to/2MQi294 Understanding MATLAB: A Textbook for Beginners https://amzn.to/2NfjNMv Essential MATLAB for Engineers and Scientists https://amzn.to/2LXFfB9 MATLAB: An Introduction with Applications https://amzn.to/2M0FxqS Matlab Essentials for Problem Solving https://amzn.to/2ML8iNs Matrix and Linear Algebra Aided with MATLAB https://amzn.to/2wLIIhb Getting Started with MATLAB: A Quick Introduction for Scientists & Engineers https://amzn.to/2M0ahrS Modeling and Simulation using MATLAB - Simulink https://amzn.to/2LYx8nY ------------------------------------- Buy MATLAB Books (affiliate): Digital Image Processing Using MATLAB https://amzn.to/2oH4Xkd A Guide To Matlab: For Beginners And Experienced Users https://amzn.to/2CmdUJr MATLAB and its Applications in Engineering https://amzn.to/2MQi294 Understanding MATLAB: A Textbook for Beginners https://amzn.to/2NfjNMv Essential MATLAB for Engineers and Scientists https://amzn.to/2LXFfB9 MATLAB: An Introduction with Applications https://amzn.to/2M0FxqS Matlab Essentials for Problem Solving https://amzn.to/2ML8iNs Matrix and Linear Algebra Aided with MATLAB https://amzn.to/2wLIIhb Getting Started with MATLAB: A Quick Introduction for Scientists & Engineers https://amzn.to/2M0ahrS Modeling and Simulation using MATLAB - Simulink https://amzn.to/2LYx8nY ------------------------------------- Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- for PDFs and notes-https://viden.io/search/knowledge?query=matlab search and find all notes/PDFs go to-https://viden.io
Views: 1689 LearnEveryone
NASA’s Jet Propulsion Laboratory is chartered to conceive and execute robotic spacecraft that explore other worlds. These craft are sent to the planets to orbit and sense their atmospheres, surfaces and interiors, and to asteroids, comets and moons to image and discover their composition. Other spacecraft land on the surface of Mars and rove the planet acting as field geologists. The sensors and spacecraft used for planetary exploration are also applied to remote sensing of the Earth, producing datasets that not only characterize our planet but also allow predictive modeling. Similarly, a range of observational systems for astronomy produces datasets of the heavens that are categorized and then drive astrophysical modeling. Large-scale computation is required in a number of areas to achieve the goals described above. The design and simulation of engineered systems requires integrating high-fidelity physics for accurate model-based design. For example, planetary landing, like the recent Curiosity mission, requires simulations of the spacecraft in the presence of a range of uncertainties such as the knowledge of the Mars atmosphere. Similarly, a long-term goal will be the real-time ingestion and analysis of multiple data sets that are used to autonomously guide a landing craft to a scientifically valuable location. In the area of remote sensing, models that can extract the thickness and composition of the icy shell and subsurface ocean on icy moons, such as Jupiter’s moon Europa, requires the development of electromagnetic models and large-scale computation to retrieve the information from radar signals. Finally, spacecraft that capture Earth and astrophysical data sets with many 10’s of terabytes of data per day, down-linked from the observatories, will require state-of-the-art technologies in big data and analytics. This talk will describe a range of problems requiring large-scale computation and data science as described above, intermingled with recent results from JPL missions.
Views: 115 NCSAatIllinois
http://CppCon.org — Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2018 — The discovery of speculative execution side-channel attacks (called "Spectre") fundamentally changes the security model of every modern superscalar microprocessor. Extracting secret data (credit cards, cryptographic keys) through side-channels is not new and has challenged the cryptographic community for decades. Despite this, the industry has often been complacent in our response, viewing these attacks as impacting a tiny amount of code and being nearly impossible to weaponize. But speculative execution attack techniques have fundamentally altered the ease and applicability of side-channels, making them a serious threat to computer security. Responding to these issues has impacted CPU design, compiler design, library design, sandbox techniques and even the C++ programming language and standard. This talk will explain how these kinds of attacks work at a high level and provide a clear set of terminology to describe these classes of vulnerabilities and attacks. It will show how the different variants work at the low level of modern hardware to give a detailed and precise understanding of the mechanics involved on CPUs today. It will also provide guidance about what makes applications and services vulnerable and how to analyze your software to understand the degree of its exposure. It will include an overview of the numerous different mitigation techniques available, how to deploy them, and what tradeoffs come with them. Some of these mitigations will be covered in detail: how they work at a hardware level, where they don't work, and what attack vectors remain. Finally, the talk will show how traditional side-channel risks are made substantially easier to exploit due to speculative execution. This will cover how cryptographic and other libraries dealing in high-value secrets need to be adapted to correctly defend against these attacks. Further, it will introduce general problems of sandboxing untrusted code from secret data and the current best techniques in those circumstances. This talk will be accessible to most C and C++ programmers. No deep background on CPUs, assembly, hardware instructions, Spectre, side-channels, or security is needed. — Chandler Carruth, Google Software Engineer Chandler Carruth leads the Clang team at Google, building better diagnostics, tools, and more. Previously, he worked on several pieces of Google’s distributed build system. He makes guest appearances helping to maintain a few core C++ libraries across Google’s codebase, and is active in the LLVM and Clang open source communities. He received his M.S. and B.S. in Computer Science from Wake Forest University, but disavows all knowledge of the contents of his Master’s thesis. He is regularly found drinking Cherry Coke Zero in the daytime and pontificating over a single malt scotch in the evening. — Videos Filmed & Edited by Bash Films: http://www.BashFilms.com
Views: 22951 CppCon
By leveraging the latest advances in natural language processing, you can drastically improve customer service experience. A Tensorflow based CNN model offers a whole new level of predictive accuracy in routing cases to the correct teams, 24/7. The Google Language API makes building real-time customer support dashboards with contextual and situational advice a cinch. By combining these powerful and cutting-edge technologies, we show how you can power fast and accurate customer support pipelines to navigate routine and sensitive cases, and positively impact customers and the support agents alike. MLAI206 Event schedule → http://g.co/next18 Watch more Machine Learning & AI sessions here → http://bit.ly/2zGKfcg Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Views: 896 Google Cloud Platform
Join us for Flutter Live on December 4th to experience the latest from Flutter, Google’s free and open source SDK for building high-quality native iOS and Android apps from a single codebase. Presented from the Science Museum in London, we are optimizing Flutter Live to reach the largest audience of mobile developers and provide all the rigor and excitement of an in-person experience to our global online audience. More information on the event website here → https://g.co/FlutterLive Subscribe to the Google Developers channel! → http://bit.ly/googledevs Music by Terra Monk → https://bit.ly/2Rst3f6 #FlutterLive
Views: 118231 Google Developers
An updated deep learning introduction using Python, TensorFlow, and Keras. Text-tutorial and notes: https://pythonprogramming.net/introduction-deep-learning-python-tensorflow-keras/ TensorFlow Docs: https://www.tensorflow.org/api_docs/python/ Keras Docs: https://keras.io/layers/about-keras-layers/ Discord: https://discord.gg/sentdex
Views: 235497 sentdex
This week we'll be joined by Lead Technical Artist Wyeth Johnson. We'll be taking a look at some of the latest additions to the Niagara plugin, including template emitters, 2d and 3d texture sampling, distance fields, mass and forces, collision refactors, and some thoughts on where we're headed next. The Winter #ue4jam theme will be announced at 3:00PM ET! NEWS Shortlist chosen for “Unreal Awards: Experience Design” competition https://www.unrealengine.com/en-US/blog/unreal-awards-experience-design-competition-winners-announced Unreal Engine 4.22 Preview 1 now available https://www.unrealengine.com/en-US/blog/4-22-preview-1-now-available How indie developer Bit Dragon tackled cross-platform play in Hyper Jam https://www.unrealengine.com/en-US/tech-blog/how-indie-developer-bit-dragon-tackled-cross-platform-play-in-hyper-jam Digital humans: 3Lateral cracks the code for real-time facial performance https://www.unrealengine.com/en-US/blog/digital-humans-3lateral-cracks-the-code-for-real-time-facial-performance COMMUNITY SPOTLIGHT Snacko https://forums.unrealengine.com/community/work-in-progress/1583746 Mr Boom's Firework Factory https://forums.unrealengine.com/community/work-in-progress/1581883 The Forge https://www.artstation.com/artwork/0XzRe8 ANNOUNCEMENT POST https://forums.unrealengine.com/unreal-engine/events/1576257
Views: 13711 UnrealEngine
In this Google Cloud AI Huddle, Technical Lead for Big Data and Machine Learning on GCP, Lak Lakshmanan, walks you through the process of training a state-of-the-art image and text classification model on your own data using TPUs and how to adapt your own model for TPU training. Google AI Huddle is an open, collaborative and developer-first AI forum driven by Google AI expertise. It’s a monthly in-person engagement where Googlers engage with developers to speak on ML topics, deliver workshops / tutorials, and hands-on labs. AI Huddle is open to all GCP customers, startups and developers interested in learning about Google AI. The Huddle provides: • Direct avenue to speak with Google experts on real problems they face in their ML and AI projects • Opportunity to hear about the latest developments from experts and peers in the industry and community • Engaging technical content and discussions to help address real development problems in ML Watch other videos in playlist here → http://bit.ly/2o2TQle Subscribe to the GCP channel → http://bit.ly/GCloudPlatform
Views: 2846 Google Cloud Platform
http://CppCon.org — Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2018 — For decades C++ developers have built software around OOP concepts that ultimately failed us - we didn’t see the promises of code reuse, maintenance or simplicity fulfilled, and performance suffers significantly. Data-oriented design can be a better paradigm in fields where C++ is most important - game development, high-performance computing, and real-time systems. The talk will briefly introduce data-oriented design and focus on practical real-world examples of applying DoD where previously OOP constructs were widely employed. Examples will be shown from modern web browsers. They are overwhelmingly written in C++ with OOP - that’s why most of them are slow memory hogs. In the talk I’ll draw parallels between the design of systems in Chrome and their counterparts in the HTML renderer Hummingbird. As we’ll see, Hummingbird is multiple times faster because it ditches OOP for good in all performance-critical areas. We will see how real-world C++ OOP systems can be re-designed in a C++ data-oriented way for better performance, scalability, maintainability and testability. — Stoyan Nikolov, Coherent Labs AD Chief Software Architect Stoyan Nikolov is the Chief Software Architect and Co-Founder of Coherent Labs. He designed the architecture of all products of the company. Stoyan has more than 10 years experience in games. Currently he heads the development of Hummingbird - the fastest HTML rendering engine in the industry and of LensVR, the first VR-centric web browser. Previously he worked on multiple graphics & core engine systems and on large-scale ERP solutions. Stoyan has degrees in Applied Mathematics and Computer Graphics. He is interested in high-performance computing, graphics, multithreading, VR and browser development. Coherent Labs AD Coherent Labs is a leading game middleware company that develops cross-platform game user interface products. It aims to solve complex problems for major gaming companies such as Arena Net, NCSoft, Bluehole, and hundreds of others, and to help them create stunning and high-performance UI. Using its experience in web, game technologies, and user interface, the company is developing a Virtual Reality browser. — Videos Filmed & Edited by Bash Films: http://www.BashFilms.com
Views: 44963 CppCon
Intervento di Andrea L. Bertozzi (Director of Applied Mathematics University of California Los Angeles) nel quadro del convegno "Mathematics in a Complex World" organizzato per il 150° anniversario della Fondazione del Politecnico di Milano. In lingua inglese.
Views: 1256 PoliMi
Lecture11 dynamic l1 recovery This lecture introduces a dynamical framework for sparse recovery, which is a recent research of Prof. Romberg and his collaborators. First, it talks about fast updating of solutions of l1 optimization programs in various scenarios, including the underlying signal changes slightly, add and remove measurements, the weights change, and have streaming measurements for an evolving signal. Second, it also introduces systems of nonlinear differential equations that solve l1 and related optimization programs, and it is implemented as continuous-time neural nets. Compressive Sensing and Sparse Recovery is a short course taught by Professor Justin Romberg during his visit to Tsinghua University from Oct. 14h to Oct. 18th, 2013. Professor Justin Romberg is an Associate Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. More details about the course: http://gu.ee.tsinghua.edu.cn/index.php/justin-romberg/justin-class
Views: 1606 谷实验室
PG Embedded Systems #197 B, Surandaia Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2013 ieee projects, 2013 ieee java projects, 2013 ieee dotnet projects, 2013 ieee android projects, 2013 ieee matlab projects, 2013 ieee embedded projects, 2013 ieee robotics projects, 2013 IEEE EEE PROJECTS, 2013 IEEE POWER ELECTRONICS PROJECTS, ieee 2013 android projects, ieee 2013 java projects, ieee 2013 dotnet projects, 2013 ieee mtech projects, 2013 ieee btech projects, 2013 ieee be projects, ieee 2013 projects for cse, 2013 ieee cse projects, 2013 ieee it projects, 2013 ieee ece projects, 2013 ieee mca projects, 2013 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2013 mtech projects, 2013 mphil projects, 2013 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2013 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2013 ieee omnet++ projects, ieee 2013 oment++ project, innovative ieee projects, latest ieee projects, 2013 latest ieee projects, ieee cloud computing projects, 2013 ieee cloud computing projects, 2013 ieee networking projects, ieee networking projects, 2013 ieee data mining projects, ieee data mining projects, 2013 ieee network security projects, ieee network security projects, 2013 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2013 wireless networking projects ieee, 2013 ieee web service projects, 2013 ieee soa projects, ieee 2013 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2013 IEEE java projects,2013 ieee Project Titles, 2013 IEEE cse Project Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2013 - 2013 ... Image Processing. IEEE 2013 - 2013 Projects | IEEE Latest Projects 2013 - 2013 | IEEE ECE Projects2013 - 2013, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2013 IEEE C#, C Sharp Project Titles, 2013 IEEE EmbeddedProject Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE Android Project Titles. 2013 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2013, IEEE 2013 PROJECT TITLES, M.TECH. PROJECTS 2013, IEEE 2013 ME PROJECTS.
Views: 157 PG Embedded Systems
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This Edureka video on "Data Science" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI. For Data-sets and Codes discussed in this video, drop a comment. This video will be covering the following topics: 1:23 Evolution of Data 2:14 What is Data Science? 3:02 Data Science Careers 3:36 Who is a Data Analyst 4:20 Who is a Data Scientist 5:14 Who is a Machine Learning Engineer 5:44 Salary Trends 6:37 Road Map 9:06 Data Analyst Skills 10:41 Data Scientist Skills 11:47 ML Engineer Skills 12:53 Data Science Peripherals 13:17 What is Data ? 15:23 Variables & Research 17:28 Population & Sampling 20:18 Measures of Center 20:29 Measures of Spread 21:28 Skewness 21:52 Confusion Matrix 22:56 Probability 25:12 What is Machine Learning? 25:45 Features of Machine Learning 26:22 How Machine Learning works? 27:11 Applications of Machine Learning 34:57 Machine Learning Market Trends 36:05 Machine Learning Life Cycle 39:01 Important Python Libraries 40:56 Types of Machine Learning 41:07 Supervised Learning 42:27 Unsupervised Learning 43:27 Reinforcement Learning 46:27 Supervised Learning Algorithms 48:01 Linear Regression 58:12 What is Logistic Regression? 1:01:22 What is Decision Tree? 1:11:10 What is Random Forest? 1:18:48 What is Naïve Bayes? 1:30:51 Unsupervised Learning Algorithms 1:31:55 What is Clustering? 1:34:02 Types of Clustering 1:35:00 What is K-Means Clustering? 1:47:31 Market Basket Analysis 1:48:35 Association Rule Mining 1:51:22 Apriori Algorithm 2:00:46 Reinforcement Learning Algorithms 2:03:22 Reward Maximization 2:06:35 Markov Decision Process 2:08:50 Q-Learning 2:18:19 Relationship Between AI and ML and DL 2:20:10 Limitations of Machine Learning 2:21:19 What is Deep Learning ? 2:22:04 Applications of Deep Learning 2:23:35 How Neuron Works? 2:24:17 Perceptron 2:25:12 Waits and Bias 2:25:36 Activation Functions 2:29:56 Perceptron Example 2:31:48 What is TensorFlow? 2:37:05 Perceptron Problems 2:38:15 Deep Neural Network 2:39:35 Training Network Weights 2:41:04 MNIST Data set 2:41:19 Creating a Neural Network 2:50:30 Data Science Course Masters Program Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencetutorial #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 17447 edureka!
In this lesson we're going to talk about that how to convert all text to lowercase in Python programming language by using string method lower().
Views: 1071 nevsky.programming
Join Gordon, Alaina, and Adam while we watch Nvidia's live stream introducing the GeForce RTX 2070, 2080, and 2080 Ti. We also have a fun bingo game to play! Follow PCWorld for all things PC! ---------------------------------- SUBSCRIBE: http://www.youtube.com/subscription_center?add_user=PCWorldVideos FACEBOOK: https://www.facebook.com/PCWorld/ TWITCH: https://www.twitch.tv/PCWorldUS TWITTER: https://www.twitter.com/pcworld WEBSITE: http://www.pcworld.com
Views: 43877 PCWorld
FINAL YEAR STUDENTS PROJECT www.finalyearstudentsproject.in Phone: +91-8903410319 Tamil Nadu India General Information and Enquiries: [email protected] PROJECTS FROM Final Year Students Project 2013 ieee projects, 2013 ieee java projects, 2013 ieee dotnet projects, 2013 ieee android projects, 2013 ieee matlab projects, 2013 ieee embedded projects, 2013 ieee robotics projects, 2013 IEEE EEE PROJECTS, 2013 IEEE POWER ELECTRONICS PROJECTS, ieee 2013 android projects, ieee 2013 java projects, ieee 2013 dotnet projects, 2013 ieee mtech projects, 2013 ieee btech projects, 2013 ieee be projects, ieee 2013 projects for cse, 2013 ieee cse projects, 2013 ieee it projects, 2013 ieee ece projects, 2013 ieee mca projects, 2013 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2013 mtech projects, 2013 mphil projects, 2013 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2013 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2013 ieee omnet++ projects, ieee 2013 oment++ project, innovative ieee projects, latest ieee projects, 2013 latest ieee projects, ieee cloud computing projects, 2013 ieee cloud computing projects, 2013 ieee networking projects, ieee networking projects, 2013 ieee data mining projects, ieee data mining projects, 2013 ieee network security projects, ieee network security projects, 2013 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2013 wireless networking projects ieee, 2013 ieee web service projects, 2013 ieee soa projects, ieee 2013 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2013 IEEE java projects,2013 ieee Project Titles, 2013 IEEE cse Project Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2013 - 2013 ... Image Processing.IEEE 2013 - 2013 Projects | IEEE Latest Projects 2013- 2013 | IEEE ECE Projects2013 - 2013, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2013 IEEE C#, C Sharp Project Titles, 2013 IEEE EmbeddedProject Titles, 2013 IEEE NS2 Project Titles, 2013 IEEE Android Project Titles. 2013 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2013, IEEE 2013 PROJECT TITLES, M.TECH. PROJECTS 2013, IEEE 2013 ME PROJECTS.
Views: 47 HARISH G
Developmental Origins of Brain Circuit Architecture and Psychiatric Disorders (Day 2) Air date: Friday, November 30, 2018, 8:30:00 AM Category: Conferences Runtime: 07:35:37 Description: Developmental neurobiology is a critical ingredient for understanding the structure and function of the human brain. Classical anatomists recognized that the brain’s component architecture is most accurately seen through the lens of neurodevelopment. This principle has been further applied to understand the brain’s internal connections, capacity for learning, and functional maturation. By drawing together researchers from different fields, this symposium aims to generate healthy discussion and debate on how advances in neurodevelopment have shaped and will continue to shape, our understanding of brain architecture and function, both in health and in psychiatric disorders. For more information go to https://nimhbraincircuit.com Author: National Institute of Mental Health, NIH Permanent link: https://videocast.nih.gov/launch.asp?27216
Views: 599 nihvcast
PG Embedded Systems www.pgembeddedsystems.com #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2015 ieee projects, 2015 ieee java projects, 2015 ieee dotnet projects, 2015 ieee android projects, 2015 ieee matlab projects, 2015 ieee embedded projects, 2015 ieee robotics projects, 2015 IEEE EEE PROJECTS, 2015 IEEE POWER ELECTRONICS PROJECTS, ieee 2015 android projects, ieee 2015 java projects, ieee 2015 dotnet projects, 2015 ieee mtech projects, 2015 ieee btech projects, 2015 ieee be projects, ieee 2015 projects for cse, 2015 ieee cse projects, 2015 ieee it projects, 2015 ieee ece projects, 2015 ieee mca projects, 2015 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2015 mtech projects, 2015 mphil projects, 2015 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2015 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2015 ieee omnet++ projects, ieee 2015 oment++ project, innovative ieee projects, latest ieee projects, 2015 latest ieee projects, ieee cloud computing projects, 2015 ieee cloud computing projects, 2015 ieee networking projects, ieee networking projects, 2015 ieee data mining projects, ieee data mining projects, 2015 ieee network security projects, ieee network security projects, 2015 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2015 wireless networking projects ieee, 2015 ieee web service projects, 2015 ieee soa projects, ieee 2015 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2015 IEEE java projects,2015 ieee Project Titles, 2015 IEEE cse Project Titles, 2015 IEEE NS2 Project Titles, 2015 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2015 - 2015 ... Image Processing. IEEE 2015 - 2015 Projects | IEEE Latest Projects 2015 - 2015 | IEEE ECE Projects2015 - 2015, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2015 IEEE C#, C Sharp Project Titles, 2015 IEEE EmbeddedProject Titles, 2015 IEEE NS2 Project Titles, 2015 IEEE Android Project Titles. 2015 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2015, IEEE 2015 PROJECT TITLES, M.TECH. PROJECTS 2015, IEEE 2015 ME PROJECTS.
Views: 283 PG Embedded Systems
Many hackers today are using process memory infections to maintain stealth residence inside of a compromised system. The current state of forensics tools in Linux, lack the sophistication used by the infection methods found in real world hacks. ECFS (Extended core file snapshot) technology, https://github.com/elfmaster/ecfs is an innovative extension to regular ELF core files, designed to be used as forensics-friendly snapshots of process memory. A brief showcasing of the ECFS technology was featured in POC||GTFO 0x7 (Innovations with core files). However this talk will reveal deeper insight on the many features of this technology, such as full symbol table reconstruction, builtin detection heuristics, and how common binutils such as objdump, and readelf can be used to quickly identify complex infections such as PLT/GOT hooks and shared library injection. We will also cover the libecfs API that was created specifically for malware and forensics analysts who aim to implement support for ECFS snapshots into new or existing malware detection software. While the ECFS core format was initially designed for runtime malware and forensics purposes, another very neat aspect to this technology was quickly extrapolated on; the ECFS snapshots can also be reloaded into memory and executed. Very similar to VM snapshots, which opens many more doors for research and exploration in a vast array of areas from dynamic analysis to migrating live processes across systems. ECFS is still a work in progress, but for those who understand the arduous nature of dissecting a process and identifying anomalies, will surely acquire a quick respect for the new technology that makes all of this so much easier. Speaker Bio: Ryan 'elfmaster' O'Neill is a computer security researcher at Leviathan Security and the maintainer of Bitlackeys.org, a hub for much of his independent research. He is a Reverse engineer, and a Software engineer, who also specializes in the ELF binary format, and delivers on going workshops in this area to interested parties, including the US government. Ryan has worked on many security technologies including but not limited to: Maya's Veil : anti-tamper / anti-exploitation protection for Linux ELF binaries VMA Vudu : automated forensics analysis of process runtime infections in Linux kernelDetective : Linux kernel forensics software Ryan has produced alot of research and publications in areas pertaining to Linux kernel and userland malware, such as "Linux kprobe instrumentation from phrack 66", and is author of soon to be released book "The art of Linux binary analysis" which focuses on everything from ELF internals to Linux Viruses, and Binary protection techniques. Ryan has been involved in the computer security scene since 1999.
Views: 4174 DEFCONConference
High throughput platforms of interactomics: Protein arrays
Views: 377 NOC16 July-Sep BT06
In this Supercharged Live Code Session, Surma is joined by Firebase specialist David East. Together they are creating a web-component using, server-side rendered, flaky network resilient, comments section! Code is here: https://github.com/GoogleChromeLabs/ui-element-samples/tree/gh-pages/firebase-firestore-comments Whether you are watching live or not, please send in your questions and comments to the guys as they will read them and if they can, answer them for you. For more Firebase subscribe to the Firebase channel: https://www.youtube.com/firebase Follow David on Twitter: https://twitter.com/_davideast Follow Surma on Twitter: https://twitter.com/dassurma
Views: 16301 Google Chrome Developers
http://CppCon.org Niek J. Bouman “Multi-Precision Arithmetic for Cryptology in C++, at Run-Time and at Compile-Time” — Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2018 — In the talk, I will present a new C++17 library for multi-precision arithmetic for integers in the order of 100--500 bits. Many cryptographic schemes and applications, like elliptic-curve encryption schemes and secure multiparty computation frameworks require multiprecision arithmetic with integers whose bit-lengths lie in that range. The library is written in “optimizing-compiler-friendly” C++, with an emphasis on the use of fixed-size arrays and particular function-argument-passing styles (including the avoidance of naked pointers) to allow the limbs to be allocated on the stack or even in registers. Depending on the particular functionality, we get close to, or significantly beat the performance of existing libraries for multiprecision arithmetic that employ hand-optimized assembly code. Beyond the favorable runtime performance, our library is, to the best of the author’s knowledge, the first library that offers big-integer computations during compile-time. For example, when implementing finite-field arithmetic with a fixed modulus, this feature enables the automatic precomputation (at compile time) of the special modulus- dependent constants required for Barrett and Montgomery reduction. Another application is to parse (at compile-time) a base-10-encoded big-integer literal. In this talk, I will focus on some Modern C++ language features that I've used to write the library and design its API (e.g., std::array, variadic templates, std::integer_sequence, constexpr, user-defined literals, using-declarations and decltype, and combinations thereof). Also, I will show some benchmarks, and will argue that the integer types offered by the library compose well with STL containers or other libraries (like Eigen for matrix/linear algebra operations). I will also present some results on formal verification of correctness and the "constant-time" property: - Correctness is verified using a tool named SAW (Software Analysis Workbench), which tries to prove equivalence between the compiled C++ code (represented as LLVM bitcode) and a behavioral specification given in a high-level functional language; - "Constant-timeness" is a property that is crucial for implementations of cryptographic protocols to prevent timing attacks. In particular, I succeeded to verify my C++ code with "ct-verif", a tool for verifying the constant-time property for C programs (which was, in its original form, incompatible with C++ due to usage of non-ANSI C in one of its header files) The library is on Github (Apache 2 licensed) https://github.com/niekbouman/ctbignum — Niek J. Bouman, Eindhoven University of Technology Researcher Secure Multiparty Computation 2017 - now Postdoc TU/e SODA (Scalable Oblivious Data Mining) project, Eindhoven University of Technology, the Netherlands 2016-2017 Senior Researcher Fraud Detection @ ABN AMRO Bank, Amsterdam, the Netherlands 2014-2016 Postdoc at Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland PhD (2012) Quantum Cryptography/Quantum Information Theory from CWI/Universiteit Leiden, the Netherlands BS'05 MS’07 Electrical Engineering from Universiteit Twente, Enschede, the Netherlands — Videos Filmed & Edited by Bash Films: http://www.BashFilms.com
Views: 1528 CppCon
Views: 2564 MrMcsoftware
Join CS50's Colton Ogden for a look at Space Invaders, an arcade classic, in part 1 of (most likely!) 2 streams where we implement our own version of the game from scratching using LÖVE and Lua. Tune in live on twitch.tv/cs50tv every week and be a part of the live chat. This is CS50 on Twitch.
Views: 1591 CS50