Hands-On Machine Learning with Scikit-Learn and TensorFlow - Chapter8Hakky St
This is the documentation of the study-meeting in lab.
Tha book title is "Hands-On Machine Learning with Scikit-Learn and TensorFlow" and this is the chapter 8.
File Replication : High availability is a desirable feature of a good distributed file system and file replication is the primary mechanism for improving file availability. Replication is a key strategy for improving reliability, fault tolerance and availability. Therefore duplicating files on multiple machines improves availability and performance.
Replicated file : A replicated file is a file that has multiple copies, with each copy located on a separate file server. Each copy of the set of copies that comprises a replicated file is referred to as replica of the replicated file.
Replication is often confused with caching, probably because they both deal with multiple copies of data. The two concepts has the following basic differences:
A replica is associated with server, whereas a cached copy is associated with a client.
The existence of cached copy is primarily dependent on the locality in file access patterns, whereas the existence of a replica normally depends on availability and performance requirements.
Satynarayanana [1992] distinguishes a replicated copy from a cached copy by calling the first-class replicas and second-class replicas respectively
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Chapter8Hakky St
This is the documentation of the study-meeting in lab.
Tha book title is "Hands-On Machine Learning with Scikit-Learn and TensorFlow" and this is the chapter 8.
File Replication : High availability is a desirable feature of a good distributed file system and file replication is the primary mechanism for improving file availability. Replication is a key strategy for improving reliability, fault tolerance and availability. Therefore duplicating files on multiple machines improves availability and performance.
Replicated file : A replicated file is a file that has multiple copies, with each copy located on a separate file server. Each copy of the set of copies that comprises a replicated file is referred to as replica of the replicated file.
Replication is often confused with caching, probably because they both deal with multiple copies of data. The two concepts has the following basic differences:
A replica is associated with server, whereas a cached copy is associated with a client.
The existence of cached copy is primarily dependent on the locality in file access patterns, whereas the existence of a replica normally depends on availability and performance requirements.
Satynarayanana [1992] distinguishes a replicated copy from a cached copy by calling the first-class replicas and second-class replicas respectively
In this Spark session Ravi Saraogi talks about why estimating default risk in fund structures can be a challenging task. He presents on how this process has evolved over the years and the current methodologies for assessing such risks.
Valencian Summer School 2015
Day 2
Lecture 15
Machine Learning - Black Art
Charles Parker (Alston Trading)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Influx/Days 2017 San Francisco | Baron SchwartzInfluxData
WHAT GOOD IS ANOMALY DETECTION?
Static thresholds on metrics have been falling out of fashion for a while, and for good reason. Modern tooling lets you analyze and monitor a lot more data points than you used to be able to, resulting in lots more noise. The hope is that anomaly detection answers some of this, by replacing static thresholds (anomalies) with dynamic ones. But it doesn’t work as well as most people think it will. In this talk I’ll explain how anomaly detection works, so you can understand why it isn’t a good general-purpose solution, and which specific cases it’s good at.
Understanding complexity is hard. That’s why demagogy often wins with logic. “Agile” estimating and planning is full of inspiring stories - if you try hard you may even convince yourself that they are real. Is your estimating and planning aligned with reality, or you're still dreaming?
This presentation is an extended version of my presentation during Agile by Example 2013. The slides has been improved and more structure has been added to assist online reading.
A Digital Conversation Meetup, June 2014. The closing presentation of the evening was shared by Adam Sefton.
Talking on the subject of complexity of the Next Web, he suggested instead of worrying about trying to organise and control this world, both digital and offline, we should embrace complexity (unicorns and all) and allow solutions to evolve and emerge naturally.
Adam Sefton is Global Executive Creative Director at Reading Room. He's been working in digital on a variety of levels for over 10 years, the last 6 in senior agency positions. He is excitable, energetic and enthusiastic about the internet, how people like to use it and what might happen to it in the future. He is returning to discuss how the emergent principles and technologies underlying the next iteration of the web should influence organisations digital strategies. What are the challenges and opportunities facing digital decision makers.
Think about the most boring eLearning you've ever created. Could you have done something different? Explore some simple and low-cost ways to incorporate scenarios into your programs to add context and relevance. Presentation by Cammy Bean, presented on February 2, 2011 at ASTD's TechKnowledge conference in San Jose.
DoWhy Python library for causal inference: An End-to-End toolAmit Sharma
As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education, and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning.
Much like machine learning libraries have done for prediction, "DoWhy" is a Python library that aims to spark causal thinking and analysis. DoWhy provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts.
For a quick introduction to causal inference, check out amit-sharma/causal-inference-tutorial. We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (KDD 2018) conference: causalinference.gitlab.io/kdd-tutorial.
Five Things I Learned While Building Anomaly Detection Tools - Toufic Boubez ...tboubez
This is my presentation from LISA 2014 in Seattle on November 14, 2014.
Most IT Ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this haystack of data and extracting signal from the noise is not easy and generates too many false positives.
In this talk I will show some of the types of anomalies commonly found in dynamic data center environments and discuss the top 5 things I learned while building algorithms to find them. You will see how various Gaussian based techniques work (and why they don’t!), and we will go into some non-parametric methods that you can use to great advantage.
Machine Learning in NutShell. Machine learning is essentially a subfield of artificial intelligence (AI). In a nutshell, the goal of machine learning is to learn from data and make accurate outcome predictions, without being explicitly programmed.
This is the presentation I gave at VizSec 2014 on our information-theoretic method for anomaly detection. The conference was held in Paris in November 2014.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
3. Slide 11- 3
It’s Not Easy Being Random (cont.)
• It’s surprisingly difficult to generate random values even
when they’re equally likely.
• Computers have become a popular way to generate
random numbers.
• Even though they often do much better than humans,
computers can’t generate truly random numbers either.
• Since computers follow programs, the “random”
numbers we get from computers are really
pseudorandom.
• Fortunately, pseudorandom values are good enough for
most purposes.
4. Slide 11- 4
It’s Not Easy Being Random (cont.)
• There are ways to generate random numbers so that they are both
equally likely and truly random.
• The best ways we know to generate data that give a fair and accurate
picture of the world rely on randomness, and the ways in which we
draw conclusions from those data depend on the randomness, too.
5. Slide 11- 5
Practical Randomness
• We need an imitation of a real process so we can manipulate and control
it.
• In short, we are going to simulate reality.
6. Slide 11- 6
A Simulation
• The sequence of events we want to investigate is called a trial.
• The basic building block of a simulation is called a component.
• There are seven steps to a simulation…
7. Slide 11- 7
Simulation Steps
1. Identify the component to be repeated.
2. Explain how you will model the component’s
outcome.
3. State clearly what the response variable is.
4. Explain how you will combine the components into
a trial to model the response variable.
5. Run several trials.
6. Collect and summarize the results of all the trials.
7. State your conclusion.
8. Slide 11- 8
What Can Go Wrong?
• Don’t overstate your case.
• Beware of confusing what really happens with what a simulation suggests
might happen.
• Model outcome chances accurately.
• A common mistake in constructing a simulation is to adopt a strategy that
may appear to produce the right kind of results.
• Run enough trials.
• Simulation is cheap and fairly easy to do.
9. Slide 11- 9
What Have We Learned?
• How to harness the power of randomness.
• A simulation model can help us investigate a question when we can’t
(or don’t want to) collect data, and a mathematical answer is hard to
calculate.
• How to base our simulation on random values generated by a
computer, generated by a randomizing device, or found on the
Internet.
• Simulations can provide us with useful insights about the real world.
