A collection of of creative virel Guerilla advertising samples from around the world. we collected Print, street, ambient and digital works that inspired us during 2011. hope you enjoy!
Here, in this paper we are introducing a dynamic clustering algorithm using fuzzy c-mean clustering algorithm. We will try to process several sets patterns together to find a common structure. The structure is finalized by interchanging prototypes of the given data and by moving the prototypes of the subsequent clusters toward each other. In regular FCM clustering algorithm, fixed numbers of clusters are chosen and those are pre-defined. If, in case, the number of chosen clusters is wrong, then the final result will degrade the purity of the cluster. In our proposed algorithm this drawback will be overcome by using dynamic clustering architecture. Here we will take fixed number of clusters in the beginning but on iterations the algorithm will increase the number of clusters automatically depending on the nature and type of data, which will increase the purity of the result at the end. A detailed clustering algorithm is developed on a basis of the standard FCM method and will be illustrated by means of numeric examples.
Dirty data science machine learning on non-curated dataGael Varoquaux
These slides are a one-hour course on machine learning with non-curated data.
According to industry surveys, the number one hassle of data scientists is cleaning the data to analyze it. Here, I survey what "dirtyness" forces time-consuming cleaning. We will then cover two specific aspects of dirty data: non-normalized entries and missing values. I show how, for these two problems, machine-learning practice can be adapted to work directly on a data table without curation. The normalization problem can be tackled by adapting methods from natural language processing. The missing-values problem will lead us to revisit classic statistical results in the setting of supervised learning.
A collection of of creative virel Guerilla advertising samples from around the world. we collected Print, street, ambient and digital works that inspired us during 2011. hope you enjoy!
Here, in this paper we are introducing a dynamic clustering algorithm using fuzzy c-mean clustering algorithm. We will try to process several sets patterns together to find a common structure. The structure is finalized by interchanging prototypes of the given data and by moving the prototypes of the subsequent clusters toward each other. In regular FCM clustering algorithm, fixed numbers of clusters are chosen and those are pre-defined. If, in case, the number of chosen clusters is wrong, then the final result will degrade the purity of the cluster. In our proposed algorithm this drawback will be overcome by using dynamic clustering architecture. Here we will take fixed number of clusters in the beginning but on iterations the algorithm will increase the number of clusters automatically depending on the nature and type of data, which will increase the purity of the result at the end. A detailed clustering algorithm is developed on a basis of the standard FCM method and will be illustrated by means of numeric examples.
Dirty data science machine learning on non-curated dataGael Varoquaux
These slides are a one-hour course on machine learning with non-curated data.
According to industry surveys, the number one hassle of data scientists is cleaning the data to analyze it. Here, I survey what "dirtyness" forces time-consuming cleaning. We will then cover two specific aspects of dirty data: non-normalized entries and missing values. I show how, for these two problems, machine-learning practice can be adapted to work directly on a data table without curation. The normalization problem can be tackled by adapting methods from natural language processing. The missing-values problem will lead us to revisit classic statistical results in the setting of supervised learning.
PPC Restart 2023: Matouš Ledvina - AI jako klíč pro efektivní marketingTaste
Je evidentní, že se čím dále přesouváme do světa poháněného umělou inteligencí. Pojďme si společně projít jak AI využít ve svůj prospěch v rámci Google produktů a maximalizovat tak výkon vašich marketingových aktivit.
Data Restart 2023: Petra Dolejšová - Práce s daty v roce 2023 – držíte krok?Taste
Pokrok nezastavíš. Ledaže jsi zákonodárce. Pak to můžeš aspoň zkusit. Jaké novinky se letos udály? Co v dohledné době čekat? Mrkneme na TOP5, které by vaší profi pozornosti neměly uniknout. Prakticky, na příkladech a způsobem, který vás bude bavit.
Ordinal Regression and Machine Learning: Applications, Methods, MetricsFrancesco Casalegno
What do movie recommender systems, disease progression evaluation, and sovereign credit ranking have in common?
→ ordinal regression sits between classification and regression
→ target values are categorical and discrete, but ordered
→ many challenges to face when training and evaluating models
What will you find in this presentation?
→ real life, clear examples of ordinal regression you see everyday
→ learning to rank: predict user preferences and items relevance
→ best solution methods: naïve, binary decomposition, threshold
→ how to measure performance: understand & choose metrics
This is the slide deck for my keynote at the Software Architect conference in London, October 2015.
The development and maintenance of monoliths presents organisations with increasing challenges, resulting in high costs and a decreasing time-to-market. More and more organisations are therefore attempting to componentise their applications.
The latest and greatest paradigm “microservices” finally seems to deliver on the promises of service-oriented architecture: shortening time-to-market, scalability, autonomy, and exchangeability of technology and databases. The challenges of delivering microservices however are equally big.
In this keynote presentation, Sander will elaborate on his personal experiences with implementing microservices architectures. He’ll be certain to address the good parts, but he does not shy away from also tackling the bad and ugly parts.
Sadigh Gallery's Spring Savings Event Flyer features huge discounts on a special selection of authentic ancient artifacts, coins and jewelry from various cultures. All orders are by phone only. Call us Toll Free at 1(800)426-2007 or 1(212)725-7537 to place your order.
Update version of the SMBE/SESBE Lecture on ENCODE & junk DNA (Graur, Decembe...Dan Graur
How to Assemble a Human Genome? Mix generous amounts of Junk DNA and Indifferent DNA, add a dollop of Garbage DNA and a sprinkling of Functional DNA (Lazarus DNA optional)
French establishments continue to grow in India. linked to 394 major French conglomerates, there are now 1051 establishments or French entities in India, which are subsidiaries of either the companies or the Groups based in France. These numbers are undergoing a constant increase as the research data is from the year 2013, which actually reflects the data of 2012, and a list of 750 establishments linked to 350 parent companies. According to the current estimates, the French companies in India today employ around 300 000 people (240 000 in the year 2013), have a turnover of more than 20 billion USD (18.5 billion USD in the year 2013) and have a minimum stock investment portfolio of 19 billion USD (17 billion USD in 2013).
PPC Restart 2023: Matouš Ledvina - AI jako klíč pro efektivní marketingTaste
Je evidentní, že se čím dále přesouváme do světa poháněného umělou inteligencí. Pojďme si společně projít jak AI využít ve svůj prospěch v rámci Google produktů a maximalizovat tak výkon vašich marketingových aktivit.
Data Restart 2023: Petra Dolejšová - Práce s daty v roce 2023 – držíte krok?Taste
Pokrok nezastavíš. Ledaže jsi zákonodárce. Pak to můžeš aspoň zkusit. Jaké novinky se letos udály? Co v dohledné době čekat? Mrkneme na TOP5, které by vaší profi pozornosti neměly uniknout. Prakticky, na příkladech a způsobem, který vás bude bavit.
Ordinal Regression and Machine Learning: Applications, Methods, MetricsFrancesco Casalegno
What do movie recommender systems, disease progression evaluation, and sovereign credit ranking have in common?
→ ordinal regression sits between classification and regression
→ target values are categorical and discrete, but ordered
→ many challenges to face when training and evaluating models
What will you find in this presentation?
→ real life, clear examples of ordinal regression you see everyday
→ learning to rank: predict user preferences and items relevance
→ best solution methods: naïve, binary decomposition, threshold
→ how to measure performance: understand & choose metrics
This is the slide deck for my keynote at the Software Architect conference in London, October 2015.
The development and maintenance of monoliths presents organisations with increasing challenges, resulting in high costs and a decreasing time-to-market. More and more organisations are therefore attempting to componentise their applications.
The latest and greatest paradigm “microservices” finally seems to deliver on the promises of service-oriented architecture: shortening time-to-market, scalability, autonomy, and exchangeability of technology and databases. The challenges of delivering microservices however are equally big.
In this keynote presentation, Sander will elaborate on his personal experiences with implementing microservices architectures. He’ll be certain to address the good parts, but he does not shy away from also tackling the bad and ugly parts.
Sadigh Gallery's Spring Savings Event Flyer features huge discounts on a special selection of authentic ancient artifacts, coins and jewelry from various cultures. All orders are by phone only. Call us Toll Free at 1(800)426-2007 or 1(212)725-7537 to place your order.
Update version of the SMBE/SESBE Lecture on ENCODE & junk DNA (Graur, Decembe...Dan Graur
How to Assemble a Human Genome? Mix generous amounts of Junk DNA and Indifferent DNA, add a dollop of Garbage DNA and a sprinkling of Functional DNA (Lazarus DNA optional)
French establishments continue to grow in India. linked to 394 major French conglomerates, there are now 1051 establishments or French entities in India, which are subsidiaries of either the companies or the Groups based in France. These numbers are undergoing a constant increase as the research data is from the year 2013, which actually reflects the data of 2012, and a list of 750 establishments linked to 350 parent companies. According to the current estimates, the French companies in India today employ around 300 000 people (240 000 in the year 2013), have a turnover of more than 20 billion USD (18.5 billion USD in the year 2013) and have a minimum stock investment portfolio of 19 billion USD (17 billion USD in 2013).
Making Story Apps - The Art of Little Red Riding Hoodedatnosycrow
In March 2013 I gave a short talk about how we make story apps at Nosy Crow. This really focused on the illustration and how we bring it to life with Maya. The original presentation didn't have any text as I was there to talk about it all. To make up for my absence here, I've added comments all the way through, so hopefully it makes sense and helps explain how we go about things.
Tell your own story : how can you build human values for innovation? (preview)WeAreInnovation
Through recent discussions engaging social media and technology experts, innovation leaders and entrepreneurs have outlined a central question that could help generate long term value for their projects: what human values could we define for innovation?
You could see this presentation as a "thinkathon" collating customers, finance stakeholders and business experts views and processing them through methodologies defined as the discussion goes along. We created a tool to capture requirements, a thinking framework to compile user stories into user solutions, and a management tool to monitor costs and results. Finally, we have consolidated results into an end-to-end plan which you can use to tell your own story and build human values for innovation.
why standard valuation matrix is not the best way to value great businessesperfectresearch
The presentation is an attempt to collate thoughts on the investment process we follow from the Gurus, Mentors and Friends we follow along with our own experience in this field.
*Disclaimer*
1. We are not SEBI registered analysts
2. Educational post only and not a stock recommendation
3. We take no responsibility to keep updating about the business being discussed
4. We may or may not own a position in any of the businesses being discussed and even if we do own a position, we may change our mind due to change in any facts or circumstances
5. Plz consider this post only as a framework to keep tracking businesses and understanding them
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