Keep it simple and it works - Simplicity and sticking to fundamentals in the world of big data By Mathangi Sri Head of Data Science at PhonePe
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With the buzz around AI and ML there is an increasing tendency for leaders and data scientists to move towards complex problem-solving. Its important to unlearn the tendency to gravitate towards complexity. In this talk we will see why avoiding complexity in ML solutions is a wiser and a quicker way to solve business problems. We can also visit some thumb rules to build pragmatic and useful models. Simplicity and sticking to fundamentals is the next "big" thing in the world of big data.
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This document contains the notes from a case study meeting discussing cloud computing. It lists the group members and then provides questions and responses about: 1) The business benefits of cloud computing and problems it solves; 2) The disadvantages of cloud computing regarding reliability and security; 3) How concepts of capacity planning, scalability, and total cost of ownership apply to Amazon and its subscribers; 4) What types of businesses most benefit from cloud computing like internet companies and large corporations due to cost savings, sharing information, and improved collaboration.
Introduction to Machine Learning
This document provides an introduction to machine learning. It defines machine learning as systems that take in data to make predictions and decisions about unseen data without being explicitly programmed. Machine learning systems can label or classify data, predict numerical values, cluster similar data, infer patterns in data, and create complex outputs. The document discusses supervised and unsupervised learning and gives examples of machine learning applications in areas like early dementia diagnosis, wildlife surveys, pricing optimization, population tracking, and predicting social media engagement. It prompts the reader to consider scenarios where machine learning could be applied and how models may fail or be improved.
Explore ML with Crowdsource | ML Extended - Session 4
Jargon is an important aspect in the learning process of any new concept. Join us in our fourth session of the Explore ML series to learn more about the terminologies associated with Machine Learning
A strategy for security data analytics - SIRACon 2016
A snag list for 'things that can go wrong' with big data analytics initiatives in security, and ways to think about the problem space to avoid that happening.
Intel Faster Risk Oct08 - Andrew Parry
The document discusses challenges in using models and technology for risk management. It covers complexity, scale, synchronization, information, technology and people. Key points are that decomposition and specification are important to handle complexity; scale requires active design not just hardware; synchronization and information are distinct concepts; and technology alone will not achieve good outcomes without the right people and roles.
Solidifying Vague Requirements & Establishing Unknown User Needs
Every web project begins with some sort of requirements gathering processes. Sometimes it’s done by a technical team, sometimes by a business strategist or marketing department.
Needs typically seem straightforward to the people closest to the project because of personal pain points they’ve experienced. Many stakeholders will talk about how very obvious the need for improvement is without referencing specifics from the user's point of view. Specifications range from the very granular to high level overviews. Which is best for Drupal projects?
This presentation from DrupalCon New Orleans 2016 by Vanessa Turke introduces concepts that she has been incorporating into her project requirements gathering process:
1. Conscious Requirements - Problems that the new system must solve.
2. Unconscious Requirements - Issues already adequately addressed by the current system, and important not to overlook
3. Undreamed of Requirements - Items that would be considered important if it was known they were possible or if they were better understood
ML Session-2
Machine learning systems take input data to make useful predictions and decisions about previously unseen data without being explicitly programmed. Machine learning involves finding patterns in examples to make predictions and can be used to label or classify data, predict values, cluster similar data, infer patterns, and create complex outputs. The document provides examples of using machine learning for early dementia diagnosis, drone-based wildlife surveys, and recommending videos, and discusses supervised and unsupervised learning models.
Similar to Keep it simple and it works - Simplicity and sticking to fundamentals in the world of big data By Mathangi Sri Head of Data Science at PhonePe (20)
Doing Analytics Right - Designing and Automating Analytics
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Explore ML with Crowdsource | ML Extended - Session 4
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A strategy for security data analytics - SIRACon 2016
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Deep Learning in Search for E-Commerce
The agenda of the talk has been broken down into two parts: 1. Query Understanding [30mins]: [Sonu Sharma] • NLP based Deep Learning Models for finding the intent of a Query in a particular taxonomy/categories: Description and Jupyter-notebook demonstration [20mins]: o Multi-Label/Multi-Class Classification Model from scratch in Keras o Feature Engineering in Spark Scala and pandas o Keras Functional APIs details in TF 2.0 o ImageNet moment of NLP - Latest invention in Word embeddings – ELMO and BeRT o Understanding Deep Neural Networks like Bi-directional Long Short-term Memory (BiLSTM) and character embeddings for language modeling • NLP based Deep Learning Models for Query Tagging with entities like Brand, Color, Nutrition, product quantity, etc. using Named Entity Recognition: [10mins]: o Building custom model in Tensorflow Estimator API o Traditional Word Embeddings like Glove, Fasttext etc. o Query (Text) Preprocessing o Sequence modeling using Convolutional Random Fields (CRF) o Saving and Restore heavy model in TF using SavedModel concept 2. Related Searches [20mins]: [Atul Agarwal] • NLP based Deep Learning Model for predicting Next Search Keyword – Model Description and Jupyter Notebook demonstration[20mins]: o Building Sequence to Sequence (Seq2Seq) model using Long Short Term Memory (LSTM) concept of Deep Neural Network in Keras o comparing different word Embeddings e.g. word2vec, fasttext, glove, etc. in popular AI framework such as gensim. o Keras Sequential APIs details in Keras o Similarity Search based on Facebook AI Research aka FAISS.
[Paper Presentation] EMOTIONAL STRESS DETECTION USING DEEP LEARNING
This document discusses using a single channel EEG device to recognize emotions from EEG data. It collected data from 10 individuals labeled as stressed or relaxed. It preprocessed the raw EEG data using filters to isolate brain signals. It then used deep learning models including an LSTM network with and without attention to classify emotions. The LSTM with attention achieved 85% accuracy, which was an improvement over the LSTM without attention. Potential applications discussed include using EEG for stress reduction by customizing music, and emotion or word prediction. The document also discusses opportunities for future enhancements such as using convolutional layers or multi-modal networks incorporating additional physiological sensors.
Flood & Other Disaster forecasting using Predictive Modelling and Artificial ...
Flood & Other Disaster forecasting using Predictive Modelling and Artificial ...Analytics India Magazine
Over 2.3 Billion people are affected due to floods in last 20 years and causing countless death , More than 92,million cattle are lost every year, seven million hectares of land is affected, and damage is over trillions dollars when taken globally in last 5 years. Floods are complicated natural events. It depends on several parameters, so it is very difficult to model analytically. The floods in a catchment depends on the characteristics of the catchment, rainfall and antecedent conditions. So the estimation of the flood peak is a very complex problem. Its due to the lack of Flood Prediction System which can predict the situation accurately. To Overcome this challenge we are building a Flood Prediction System using Predictive modelling. However we have divided our idea into small fragments but enough to be used globally. We have considered most flooded state of India, but can be used widely for all the low lying geographical regions. •The plains of Bihar, adjoining Nepal, are drained by a number of rivers that have their catchments in the steep and geologically nascent Himalayas. Kosi, Gandak,Burhi Gandak, Bagmati, Kamla Balan, Mahananda and Adhwara Group of rivers originates in Nepal, carry high discharge and very high sediment load and drops it down in the plains of Bihar. · About 65% of catchments area of these rivers falls in Nepal/Tibet and only 35% of catchments area lies in Bihar. · Bihar is India’s most flood-prone State, with 76 percent of the population, in the north Bihar living under the recurring threat of flood devastation. About 68800 sq Km out of total geographical area of 94163 sq Km comprising 73.06 percent is flood affected. · According to some historical data, 16.5% of the total flood affected area in India is located in Bihar while 22.1% of the flood affected population in India lives in Bihar. · From 1979 to Present day more than 8,873 Humans & 27,573 animals have lost their life due to flood. Some of Tools & Technology which is being used & can be used for Flood Prediction: •IBM. Watson Studio democratizes machine learning and deep learning to accelerate infusion of AI in to drive innovation. •An Intelligent Hydro-informatics Integration Platform for Regional Flood Inundation Warning Systems. •Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm. · Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation
AI for Enterprises-The Value Paradigm By Venkat Subramanian VP Marketing at B...
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AI is here, call it buzz, cause it a bubble, we are smack in the middle of an AI revolution. While there is a strong view building about consumer AI applications, there still seems to be some scepticism about AI for enterprises, primarily due to the lack of clarity and focus on how AI can actually deliver value for enterprises. At BRIDGEi2i, we believe it is important to have a non-fragmented view of the AI ecosystem and a “Value Roadmap” for AI in the enterprise context. As CxOs, it is important to understand where the enterprise is in the transformation journey and define value accordingly. This talk will throw light on how to look at the enterprise AI ecosystem and build the right roadmap for value.
Feature Based Opinion Mining By Gourab Nath Core Faculty – Data Science at Pr...
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Suppose that a customer who has given a high rating about a mobile phone writes the following review about the product: The front camera of the phone is excellent! Truly speaking, this is the best front camera I have experienced so far. From this review, we can understand two things. First, the customer holds a positive opinion about the phone. Secondly, the front camera of the phone is the targeted feature on which the opinions have been expressed in the review. In this workshop, we will be particularly interested in discovering patterns as indicated in the second case. We will discuss a framework that enables us to first discover the targets on which the opinions have been expressed in a review and then determine the polarity of the opinions. This kind of detailed analysis helps us to discover the components or features of the products which the customers have liked or disliked and thus help us to better summarize the information.
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...
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Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Getting your first job in Data Science By Imaad Mohamed Khan Founder-in-Resid...
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Getting your first job in Data Science is difficult. You’ve been applying to jobs, but they keep rejecting you. You don’t know what to do and how you could differentiate yourselves amidst the pool of candidates? In this talk, we’ll be going through different tips and techniques you could use to find that elusive Data Science jobs. They’ve worked for me and probably will work for you too!
10 data science & AI trends in india to watch out for in 2019
With enterprises putting digital at the core of their transformation, our annual Data Science & AI Trends Report explores the key strategic shifts enterprises will make to stay intelligent and agile going into 2019. The year was marked by a series of technological advances, including advances in AI, deep learning, machine learning, hybrid cloud architecture, edge computing (with data moving away to edge data centres), robotic process automation, a spurt of virtual assistants, advancements in autonomous tech and IoT.
The hitchhiker's guide to artificial intelligence 2018-19
Everyone is talking about Artificial Intelligence — the new normal, which has entered almost every work process across industries. Enterprises are rethinking and strengthening their AI capabilities, using it as a tool to improve products and services. With AI becoming crucial to enterprise success, upskilling has become the new mantra among Indian IT professionals, who are keen to make an impact in their careers with Machine Learning and AI.
In our annual AI Study with Great Learning, we take a look at key AI trends dominating the Indian AI market-leading companies, professionals, salaries, jobs broken down by cities and how AI’s potential for industry growth has risen over the last few years. In the second half of the study, we cover AI literacy in India through Great Learning’s comprehensive AI/ML programs that are bridging the current skill gap and consequently boosting workforce transitions.
Data Science Skills Study 2018 by AIM & Great Learning
The samples were collected by asking respondents to fill in a survey created by AIM about what tools and techniques data scientists use at work. This included various sub-topics such as data visualisation tools, preferred operating systems and programming languages, among others. We took opinions from all those who practice data science — from professionals with less than two years of experience to CXOs — to get a thorough idea of the working environment in this growing field.
Our survey was met with much enthusiasm — and we got some great insights from it. Some of them were expected, and many of them were real eye-openers.
Emerging engineering issues for building large scale AI systems By Srinivas P...
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The document discusses an online 6-month certificate program in artificial intelligence and deep learning from Manipal Prolearn. It provides awarding from MAHE, hands-on training using real-world data from different domains, and instruction from industry experts. The program teaches skills for developing end-to-end AI/ML systems and covers topics like data acquisition, modeling, evaluation, and deployment.Predicting outcome of legal case using machine learning algorithms By Ankita ...
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This document summarizes a presentation on predicting the outcomes of legal cases using machine learning models. It discusses extracting data from judgment documents and identifying key features for analysis. Exploratory data analysis was conducted on 202 observations to understand patterns. Logistic regression, KNN, random forest, and support vector machine models were developed. The tuned support vector machine model achieved the highest accuracy of 95% based on 10-fold cross-validation. Overall, support vector machine provided the best performance. The models tended to predict non-guilty outcomes more frequently due to the skewed data. Future work involves developing a mobile application for these predictive capabilities.Bringing AI into the Enterprise - A Practitioner's view By Piyush Chowhan CIO...
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Artificial Intelligence is slowly getting its way into organisations. AI journey can be quite complex and a proper roadmap would be needed to realize the benefits. This presentation will talk about common myth and a high level approach for bringing AI into the enterprise.
www.analyticsindiasummit.comExplainable deep learning with applications in Healthcare By Sunil Kumar Vupp...
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We started relying on the decisions made by deep learning models, however why it works and how it works are still big questions for most of us. We shall try to open that black box of deep learning which is essential to build trust for wide spread adoption. The speaker shall address the importance of feature visualization and localization in deep learning models esp. convolutional neural networks. He shares the results of applying methods such as activation map, deconvolution and Grad-CAM in healthcare.
Getting started with text mining By Mathangi Sri Head of Data Science at Phon...
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The workshop will empower you and get started with analyzing text data, discover patterns and what are the best ways to convert unstructured to structured data. We will also build a quick classification model and understand techniques to improve model performance. Towards the end lets quickly do a sentiment analysis on data corpus and discuss the next steps to improve model accuracy. Please come prepared with a working laptop with Jupyter Notebook and Python 2.7. Participants who have a minimum working knowledge of supervised models is encouraged.“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...Analytics India Magazine
The “Sexiest job of the 21st century” is often surveyed to be poorly defined, intermittently satisfying and vaguely understood in most board rooms. As success stories are widely publicized, senior business leaders’ expectations from analytics are rising quickly. And the field itself is changing rapidly - with speciality skills becoming self-service in no time. In that context, the talk explores how the various analytics roles across the spectrum are changing. And what it takes for analytics professionals to stay relevant, contribute meaningfully to business results and play a critical role in shaping business strategy.
www.analyticsindiasummit.com"Route risks using driving data on road segments" By Jayanta Kumar Pal Staff ...
"Route risks using driving data on road segments" By Jayanta Kumar Pal Staff ...Analytics India Magazine
Going out for dinner in Mumbai during an extended stay, or planning for a long road-trip across the wild west of Rajasthan, the first thing one looks at is Maps, that informs the relative distance, estimated time and congestion areas of different routes for the drive. Zendrive built state-of-the-art technologies on its huge cache of driving data from smartphones and OBD, to add a significant dimension to the route mapping of Google, that is safety risk of the route. Essentially the technology is built on millions of drivers zipping through the route or segments thereof. Automobile Insurance expands in UBI- where it has been established that tracking a driver’s behavior behind the wheels (like Hard Brake, Speeding etc) can predict significant differences between their chances of collisions. Looking at the same event data from the road perspective, aggregating the relative event density on road stretches also predict the relative chances of collision on that segment. We have used map matching using GIS techniques, parametric density estimation and rare event modeling using quasi-Poisson GLM to analyze our data, build the models and finally implement the scoring system across the GIS route maps. Key learnings : Relation between dangerous driving events and collisions Route risk as an aggregate of all the drivers (or sample thereof) and their driving risk. The route you take for commute may determine your auto insurance. Outline : Usage Based Insurance : relation between collision rates and dangerous driving. Driving events : aggressive acceleration, hard brake, speeding, phone use, aggressive turns Poisson GLM modeling to predict collision rates using driving data Events on a road segment : map-matching using GIS techniques to split trips along road stretches, and aggregate such events along the spatio-temporal dimension across all drivers. Route risk of the road segment and any route comprising such segments. Driving risk along such routes and corresponding collision risks using transfer of the GLM model. Assignment of risks to drivers on their daily route of commute, to be used in UBI.
www.analyticsindiasummit.com“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...Analytics India Magazine
By Phani Mitra VP Analytics & Strategy at Dr. Reddy’s
The “Sexiest job of the 21st century” is often surveyed to be poorly defined, intermittently satisfying and vaguely understood in most board rooms. As success stories are widely publicized, senior business leaders’ expectations from analytics are rising quickly. And the field itself is changing rapidly - with speciality skills becoming self-service in no time. In that context, the talk explores how the various analytics roles across the spectrum are changing. And what it takes for analytics professionals to stay relevant, contribute meaningfully to business results and play a critical role in shaping business strategy.
www.analyticsindiasummit.comAnalytics Education — A Primer & Learning Path
The analytics education market in India is exploding with analytics institutes providing a slew of instructional courses that are in line with the industry demand. This study aims to provide an overview of the analytics education landscape in India, the type of learning models offered and how online learning has become an inherent part of the analytics ecosystem in India.
Analytics & Data Science Industry In India: Study 2018 - by AnalytixLabs & AIM
Analytics & Data Science Industry In India: Study 2018 - by AnalytixLabs & AIMAnalytics India Magazine
The data analytics market in India is growing at a fast pace, with companies and startups offering analytics services and products catering to various industries. Different sectors have seen different penetration and adoption of analytics, and so is the revenue generation from these sectors.
The Analytics and Data Science Industry Study 2018 takes into account various trends that analytics industry in India is witnessing, revenue generated through various geographies, analytics market size by sector, across cities etc. It also takes into consideration analytics professionals in India across work experience and education.
This year’s study is brought to you in association with AnalytixLabs, a pioneer and one of the first analytics training institutes in India. The study is a result of extensive primary and secondary research conducted over a duration of two months, where we got in touch with analytics companies and professionals across various industries such as banking, finance, ecommerce, retail, pharma, healthcare and others.More from Analytics India Magazine (20)
[Paper Presentation] EMOTIONAL STRESS DETECTION USING DEEP LEARNING
[Paper Presentation] EMOTIONAL STRESS DETECTION USING DEEP LEARNING
Flood & Other Disaster forecasting using Predictive Modelling and Artificial ...
Flood & Other Disaster forecasting using Predictive Modelling and Artificial ...
AI for Enterprises-The Value Paradigm By Venkat Subramanian VP Marketing at B...
AI for Enterprises-The Value Paradigm By Venkat Subramanian VP Marketing at B...
Feature Based Opinion Mining By Gourab Nath Core Faculty – Data Science at Pr...
Feature Based Opinion Mining By Gourab Nath Core Faculty – Data Science at Pr...
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...
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Getting your first job in Data Science By Imaad Mohamed Khan Founder-in-Resid...
10 data science & AI trends in india to watch out for in 2019
10 data science & AI trends in india to watch out for in 2019
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The hitchhiker's guide to artificial intelligence 2018-19
Data Science Skills Study 2018 by AIM & Great Learning
Data Science Skills Study 2018 by AIM & Great Learning
Emerging engineering issues for building large scale AI systems By Srinivas P...
Emerging engineering issues for building large scale AI systems By Srinivas P...
Predicting outcome of legal case using machine learning algorithms By Ankita ...
Predicting outcome of legal case using machine learning algorithms By Ankita ...
Bringing AI into the Enterprise - A Practitioner's view By Piyush Chowhan CIO...
Bringing AI into the Enterprise - A Practitioner's view By Piyush Chowhan CIO...
Explainable deep learning with applications in Healthcare By Sunil Kumar Vupp...
Explainable deep learning with applications in Healthcare By Sunil Kumar Vupp...
Getting started with text mining By Mathangi Sri Head of Data Science at Phon...
Getting started with text mining By Mathangi Sri Head of Data Science at Phon...
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...
"Route risks using driving data on road segments" By Jayanta Kumar Pal Staff ...
"Route risks using driving data on road segments" By Jayanta Kumar Pal Staff ...
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...
“Who Moved My Cheese?” – Sniff the changes and stay relevant as an analytics ...
Analytics & Data Science Industry In India: Study 2018 - by AnalytixLabs & AIM
Analytics & Data Science Industry In India: Study 2018 - by AnalytixLabs & AIM
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一比一原版南昆士兰大学毕业证如何办理
原版一模一样【微信：741003700 】【南昆士兰大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理南昆士兰大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理南昆士兰大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理南昆士兰大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理南昆士兰大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Salesforce AI + Data Community Tour Slides - Canarias
Slides for the Salesforce AI + Data Community Tour in Las Palmas de Gran Canaria
一比一原版英国赫特福德大学毕业证（hertfordshire毕业证书）如何办理
原版一模一样【微信：741003700 】【英国赫特福德大学毕业证（hertfordshire毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Bangalore ℂall Girl 000000 Bangalore Escorts Service
Bangalore ℂall Girl 000000 Bangalore Escorts Service
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
一比一原版莱斯大学毕业证（rice毕业证）如何办理
原版一模一样【微信：741003700 】【莱斯大学毕业证（rice毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理莱斯大学毕业证（rice毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
原版办【微信号:BYZS866】【兰加拉学院毕业证(Langara毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版卡尔加里大学毕业证（uc毕业证）如何办理
原版一模一样【微信：741003700 】【卡尔加里大学毕业证（uc毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理卡尔加里大学毕业证（uc毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
How To Control IO Usage using Resource Manager
How To Control IO Usage using Resource Manager
In Multitenant and Exadata architecture
PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)
To honor ten years of PyData London, join Dr. Rebecca Bilbro as she takes us back in time to reflect on a little over ten years working as a data scientist. One of the many renegade PhDs who joined the fledgling field of data science of the 2010's, Rebecca will share lessons learned the hard way, often from watching data science projects go sideways and learning to fix broken things. Through the lens of these canon events, she'll identify some of the anti-patterns and red flags she's learned to steer around.
一比一原版斯威本理工大学毕业证（swinburne毕业证）如何办理
原版一模一样【微信：741003700 】【斯威本理工大学毕业证（swinburne毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理斯威本理工大学毕业证（swinburne毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理斯威本理工大学毕业证（swinburne毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理斯威本理工大学毕业证（swinburne毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理斯威本理工大学毕业证（swinburne毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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Keep it simple and it works - Simplicity and sticking to fundamentals in the world of big data By Mathangi Sri Head of Data Science at PhonePe
- 1. Keep it Simple And it works!
- 2. Keep it simple, stupid (KISS Principle)
- 3. OCcam’s principle of parsimony Any hypothesis that is more probable is possibly the right one to explain it
- 4. simple data science - but why?
- 5. Complexity depletes robustness Mathematical reason 1. Curse of dimensionality 2. Sparse Matrix 3. Tough to optimize 4. Overfitting Pragmatic reason 1. Danger of unstable data pipelines 2. Compute time
- 6. Simple solutions sell faster Humans need not explain how they think - but machines need to!
- 7. Use the time more to analyze the data Explore the data Create Features
- 8. You get a high return on effort Defining the right business problem Choosing the right metric Choosing right dependent variable,timeframe & data Feature creation Algorithm Hyper Parameter Tuning ML optimization funnel
- 9. Time in hand for adequate validations 1. Validations on business accuracy 2. Out of time validations 3. Thinking about any “Leakage” variables 4. Tossing to business stakeholders
- 10. simple data science - and how?
- 11. Understand status quo 1. What does the current system do today?. How is success measured
- 12. Does the problem warrant a ml solution 1. Some problems can be solved by just business rules a. For ex a hair shampoo company wants to send offers for all customers who have bought the product earlier b. Company is not averse to spend on all the people who qualify nor is it concerned by response rate 1. Sometimes even freq distribution can solve problems a. We want to predict the top 3 reasons for calling from available 10 reasons b. Top 3 reasons lets say covers 95% of the use cases c. Ordering of the reason within top 3 does not matter
- 13. Prefer supervised over unsupervised 1. Label data whenever possible 2. Instrument dependent variables if possible 3. Accuracy trade-off Vs cost of collecting samples
- 14. Prefer batch models over real time models Vs
Editor's Notes
- Kelly Johnson
- Henny-Penny the sky is falling
- Prof from George Tech
- Stakeholders find it far difficult to digest a highly complex black box models NLP & CV - people dont question Personalization or Targeting or risk based selection kind of problems people do need it to be explained Even if not the mathematical model - atleast the input features need to be explainable
- Less Labels Good number of samples per label Historic data is representative
- Most companies have systems well oiled for batch Lot of targeting/personalization/fraud/risk etc can well be solved by batch Trade off analysis - accuracy Vs complexity Keep a offset period in models