The document introduces Julia, a programming language for scientific computing and data science. It begins with an introduction of the speaker and why they chose Julia. Some key advantages of Julia are its readability like Python but performance of C. Julia also avoids the two language problem of Python for development and C for performance. Examples are given showing Julia code and its performance. Several use cases of Julia in fields like economics, astronomy, biology are also mentioned.
The .NET architecture addresses an important need - language interoperability. Instead of generating native code that is specific to one platform, programming languages generate code in CIL (Common Intermediate Language) targeting the Common Language Runtime (CLR) to reap the rich benefits provided by the .NET platform.
Advanced programmers occasionally peek into CIL code when they are in doubt about what is happening under the hood (using the Ildasm tool for .NET or monodis tool for Mono). Therefore, it is essential that developers working in .NET platform understand the essentials of CIL. This presentation uses an example driven approach to help understand bytecodes in CIL.
Julia programming language is a high-level, high-performance dynamic programming language for technical computing. It can be applied for Data Science, Machine Learning tasks, the web, among others. These slides are a brief introduction to this amazing language that facilitates my daily activities as Data Science and Software Engineer. For more information about the language access http://julialang.org/.
Kickstart your data science journey with this Python cheat sheet that contains code examples for strings, lists, importing libraries and NumPy arrays.
Find more cheat sheets and learn data science with Python at www.datacamp.com.
Jose Leiva, data scientist at Ets Asset Management Factory, gives an accurate and simple introduction to Machine Learning. He explains some of the problems that quantitative managers have to get alpha in the markets, and how to face them using Deep Learning.
The .NET architecture addresses an important need - language interoperability. Instead of generating native code that is specific to one platform, programming languages generate code in CIL (Common Intermediate Language) targeting the Common Language Runtime (CLR) to reap the rich benefits provided by the .NET platform.
Advanced programmers occasionally peek into CIL code when they are in doubt about what is happening under the hood (using the Ildasm tool for .NET or monodis tool for Mono). Therefore, it is essential that developers working in .NET platform understand the essentials of CIL. This presentation uses an example driven approach to help understand bytecodes in CIL.
Julia programming language is a high-level, high-performance dynamic programming language for technical computing. It can be applied for Data Science, Machine Learning tasks, the web, among others. These slides are a brief introduction to this amazing language that facilitates my daily activities as Data Science and Software Engineer. For more information about the language access http://julialang.org/.
Kickstart your data science journey with this Python cheat sheet that contains code examples for strings, lists, importing libraries and NumPy arrays.
Find more cheat sheets and learn data science with Python at www.datacamp.com.
Jose Leiva, data scientist at Ets Asset Management Factory, gives an accurate and simple introduction to Machine Learning. He explains some of the problems that quantitative managers have to get alpha in the markets, and how to face them using Deep Learning.
Jay Yagnik at AI Frontiers : A History Lesson on AIAI Frontiers
We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
This presentation was given online in July 2017 and will be given at the NY Java SIG later this year. It progressively builds on Java 8 concepts using puzzles and coding to give students confidence in their Java 8 stream/lambda skills. Handouts and code in https://github.com/boyarsky/java-8-streams-by-puzzles
Software is eating the world. The rate at which we produce new software is astounding. Understanding and preventing potential issues is a growing concern.
Building software security teams is much different than building IT security teams. It requires different backgrounds and focus. Software security groups without an emphasis on software fail.
Join Aaron as he talks about the right way to build and run a software security group. You will walk away with a concrete list of actions that you can take back to your job and start working on right away.
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
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Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
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Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
Here we are going to take a look how to use for loop, foreach loop and while loop. Also we are going to learn how to use and invoke methods and how to define classes in Java programming language.
A Unicorn Seeking Extraterrestrial Life: Analyzing SETI@home's Source CodePVS-Studio
Debates on whether or not we are alone in the Universe have been exciting our minds for many decades. This question is approached seriously by the SETI program whose mission is to search for extraterrestrial civilizations and ways to contact them. It is the analysis of one of this program's projects, SETI@home, that we are going to talk about in this article.
Explanations to the article on Copy-PastePVS-Studio
Many readers liked my article "Consequences of using the Copy-Paste method in C++ programming and how to deal with it" [1]. Scott Meyers [2] noticed it too and asked me how static analysis proper helped us to detect the errors described in the article.
Numerical tour in the Python eco-system: Python, NumPy, scikit-learnArnaud Joly
We first present the Python programming language and the NumPy package for scientific computing. Then, we devise a digit recognition system highlighting the scikit-learn package.
JDD2015: Frege - Introducing purely functional programming on the JVM - Dierk...PROIDEA
FREGE - INTRODUCING PURELY FUNCTIONAL PROGRAMMING ON THE JVM
Frege is a Haskell for the JVM. In Frege you program with pure functions, immutable values,
and isolated effects only. This talk gives you a first impression of what this paradigm means to the programmer and how it makes your code robust under composition, allows refactoring without fear, and becomes safe for parallel execution.
This introduction leads you through the benefits that make Frege unique between the JVM languages. It is followed up by the Frege tutorial that provides more detail and examples.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
6. Avoid two language problem
One language for rapid development
The other for performance
Example:
Python for rapid development
C for performance
6
13. Nobel prize in economic sciences
The founder of QuantEcon
“His team at NYU uses Julia for macroeconomic modeling and contributes
to the Julia ecosystem.”
https://juliacomputing.com/case-studies/thomas-sargent.html
13
14. In 2015, economists at the Federal Reserve Bank of New York (FRBNY)
published FRBNY’s most comprehensive and complex macroeconomic
models, known as Dynamic Stochastic General Equilibrium, or DSGE
models, in Julia.
https://juliacomputing.com/case-studies/ny-fed.html
14
15. UK cancer researchers turned to Julia to run simulations of tumor growth.
Nature Genetics, 2016
Approximate Bayesian Computation (ABC) algorithms require potentially millions of
simulations - must be fast
BioJulia project for analyzing biological data in Julia
Bayesian MCMC methods Lora.jl and Mamba.jl
https://juliacomputing.com/case-studies/nature.html
15
16. IBM and Julia Computing analyzed eye fundus images provided by Drishti
Eye Hospitals.
Timely screening for changes in the retina can help get them to treatment
and prevent vision loss. Julia Computing’s work using deep learning
makes retinal screening an activity that can be performed by a trained
technician using a low cost fundus camera.
https://juliacomputing.com/case-studies/ibm.html
16
17. Path BioAnalytics is a computational biotech company developing novel
precision medicine assays to support drug discovery and development,
and treatment of disease.
https://juliacomputing.com/case-studies/pathbio.html
17
18. The Sloan Digital Sky Survey contains nearly 5 million telescopic images of
12 megabytes each – a dataset of 55 terabytes.
In order to analyze this massive dataset, researchers at UC Berkeley and
Lawrence Berkeley National Laboratory created a new code named
Celeste.
https://juliacomputing.com/case-studies/intel-astro.html
18
29. 躺著玩、坐著玩、趴著玩,還是運算子好
玩
+x: 就是x本身
-x: 變號
x + y, x - y, x * y, x / y: 一般四則運算
div(x, y): 商
x % y: 餘數,也可以用rem(x, y)
x y: 反除,等價於y / x
x ^ y: 次方
29
30. 操縱數字的機械核心
~x: bitwise not
x & y: bitwise and
x | y: bitwise or
x $ y: bitwise xor
x >>> y:無正負號,將x的位元右移y個位數
x >> y:保留正負號,將x的位元右移y個位數
x << y: 將x的位元左移y個位數
https://www.technologyuk.net/mathematics/number-systems/images/binary_number.gif
30
39. 組織起來
if x == paper
println("你出布")
elseif x == scissor
println("你出剪刀")
elseif x == stone
println("你出石頭")
end
if <判斷式1>
<程式碼1>
elseif <判斷式2>
<程式碼2>
else
<程式碼3>
end
39
41. 巢狀比較
if x == y
println("平手")
elseif x == paper
println("你出布")
if y == scissor
println("電腦出剪刀")
println("電腦贏了")
elseif y == stone
println("電腦出石頭")
println("你贏了")
end
... 41
42. 我的義大利麵條
elseif x == scissor
println("你出剪刀")
if y == paper
println("電腦出布")
println("你贏了")
elseif y == stone
println("電腦出石頭")
println("電腦贏了")
endelseif x == stone
println("你出石頭")
if y == scissor
println("電腦出剪刀")
println("你贏了")
elseif y == paper
println("電腦出布")
println("電腦贏了")
end
end
if x == y
println("平手")
elseif x == paper
println("你出布")
if y == scissor
println("電腦出剪刀")
println("電腦贏了")
elseif y == stone
println("電腦出石頭")
println("你贏了")
end 42
65. Easy to optimize
Allow generalization and flexibility, and enable to optimize.
Hints:
Avoid global variables
Add type declarations
Measure performance with @time and pay attention to memory
allocation
……
65
94. Probability
JuliaStats
JuliaOpt
JuMP.jl
Convex.jl
JuliaML
LearnBase.jl
LossFunctions.jl
ObjectiveFunctions.jl
PenaltyFunctions.jl
Klara.jl: MCMC inference in Julia
Mamba.jl: Markov chain Monte
Carlo (MCMC) for Bayesian
analysis in julia
94
101. Jobs
Apple, Amazon, Facebook, BlackRock, Ford, Oracle
Comcast, Massachusetts General Hospital
Farmers Insurance
Los Alamos National Laboratory and the National
Renewable Energy Laboratory
101
https://juliacomputing.com/press/2017/01/18/jobs.html
the next generation of macroeconomic models is very computationally intensive with large datasets and large numbers of variables
First, as free software
Second, as the models that we use for forecasting and policy analysis grow more complicated, we need a language that can perform computations at a high speed