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Our Team
We believe our teamwork approach adds a broader perspective to all we do and provides increased benefits to our clients.
Our team of financial professionals can help you address:
- Investing principles and strategies
- Retirement investing and distribution strategies
- Estate conservation issues
- Risk management
We also can answer your questions, including:
- Can I retire early?
- Are my investments working hard enough?
- What's the best approach for college savings?
- What are the elements of a sound estate strategy?
- Do I have enough life insurance for my family?
Our experienced professionals have helped many people just like you with similar issues and concerns. We specialize in creating an approach that is designed to address your individual situation.
Chad Schut
Principal, Certified Financial Planner
Chad is co-owner of Kuipers Advisors and is a CFP professional with the investment team. Chad holds his Series 7 and 63 registratons as well as licenses for insurance products. Chad has his undergraduate degree from Calvin College, his...
W. Douglas Koning
Licensed Insurance Agent
Doug has been a licensed Insurance Agent with our office for over seventeen years working with local employers and individuals to set up insurance programs that fit their unique needs. Doug specializes in employee benefits, individual...
Mitchell Wassink
Financial Advisor
Mitch is an important addition to the investment advisor team, with over 10 years of experience in financial services. Mitch brings important banking experience. He is investment registered with Series 7 and 66 registrations, as well...
Darla Tiffany
Employee Benefit Specialist
Darla has been working with group, individual, and ancillary insurance benefits since 1993. She has worked in all aspects of group benefits including; Sales, Marketing, Claims, Agent Services and Account Management. She has also worked...
Shannan Frank
Financial Assistant
Shannan has been with our office for over twenty years. She holds her Life, Health and Accident Agent License in the state of Michigan. She works with our investment team and is one of our Medicare insurance specialists.
Shannon Potgiesser
Financial Assistant
Shannon Potgiesser works with our Investment team. She brings her accounting experience and an education background of a Masters Degree in Management and a Bachelors Degree in Finance.
Chris Harper
Chris Harper has been with our office for over twenty five years. First working as the receptionist and handling individual health insurance. Over the years, she has added responsibilities and is now the Office Manager working mostly...
Carrie Betz
Employee Benefits Specialist
Jack Kuipers
Certified Financial Planner
*Jack Kuipers is not an employee of Cetera Advisors LLC.


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Works on strengthening the temporary bund around the damaged portion of the old regulator across the Kollidam at Mukkombu will be completed by August 20, according to the Public Works department (PWD).
Completion of the works is essential to ensure flow of water in the Cauvery once the Mettur dam is opened for irrigation.
The PWD built a temporary bund after nine of the 45 shutters of the regulator across the Kollidam, at its originating point, collapsed on August 22 last year. It built a ring bund using sand bags, boulders and wooden logs to block the flow of water through the damaged shutters and regulate release of water for irrigation in the delta in the adjoining Cauvery.
The State government decided to strengthen the temporary bund using sheet piles and sanctioned ₹38.85 crore for the purpose. This apart, a new regulator was sanctioned at a cost of ₹387.60 crore.
Works on strengthening the temporary bund and construction of the new regulator are progressing simultaneously at Mukkombu now. The contract for construction of the regulator has been awarded to Larsen and Toubro and erection of the pile foundation for the new structure is currently underway.
PWD officials say 484 piles will be erected for building the regulator and each will run for a depth of 15 to 18 metres. So far, 71 piles have been erected and all of them will be completed within the next couple of months. The regulator will run for a length of about 630 metres and have 45 vents as the old structure. It is expected to be ready by March 2021, although PWD is hopes to the complete the project well ahead of schedule.
On Tuesday, PWD Secretary S.K. Prabhakar, accompanied by Collector S. Sivarasu, S. Ramamurthy, Chief Engineer, PWD, Tiruchi region, N. Radhakrishnan, Superintending Engineer and R. Baskar, Executive Engineer, PWD, River Conservation Division, inspected the progress of work at the site.

subset: commoncrawl


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Jan 9, 2014 ... ... Appeal No.86 of 2013) In the matter of M/s Dhillon Stone Crushers, Village Mandewala ... Jai Mata Di Stone Crushers Village Doiwala, Yamuna Nagar (Haryana) . ..... Hongo India Private Limited and Anr. (2009) 5 SCC 791,...Chat Online
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Jul 13, 2012 ... Former Congress leader Deepinder Singh Dhillon today joined the Shiromani ... its "tax" on stone crusher owners, ensuring that there is no relief for consumers. ... A doctor examines children at a private hospital in Patiala on Thursday. .... Speaking to The Tribune, Punjab State Power Corporation Limited...Chat Online
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Do I have toonphilia?
- 10 Questions - by: Aisukurimu - Developed on: - 388 takers
You know that moment when you see a certain anime/tv show character and you just wanna hug them? Well that can be an illness : D Take this test and find out!
Health tests -» Diseases -» Other Diseases


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Quote of the MomentDeath to the unbeliever
Recent Work of the Gods
- Video Tip: Glue Stick Handle For Securing Miniatures While Painting
- Comparison: Dark Vengeance vs Assault on Black Reach
- Review: Warhammer 40k Dark Vengeance 6th Edition Boxed Set
- News: Warhammer 40K Sixth Edition Boxed Set
- Reaper Bones Flex Test
- Games Workshop Vs Inflation: The True Numbers Behind The Price Increases
- Warhammer 40k Sixth Edition Boxed Set
- Warhammer 40k Sixth Edition Rules Are Here!
- News: 2012 Warhammer 40k Release Schedule and Timeline
- Project: Imperial Fists Captain Lysander and Space Marines Post #4 (SM Base Coat)
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Tag Archives: brush
Project: Imperial Fists Captain Lysander and Space Marines Post #4 (SM Base Coat)
Properly base coating a miniature is one of the most important steps to insuring good results. A spotty base coat will ruin the model no matter how well shaded or highlighted the rest of the model is. Today, we will … Continue reading
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Rumor Round-Up: New Games Workshop Paints
We've had several viewers ask about updated information on the new line of GW paints that will be coming out soon. We did some searching and chatting and compiled a list of the latest rumors surrounding the paints. All the … Continue reading
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Tutorial: Stripping Paint from Miniatures (Multiple Ways)
We recently had a question from a viewer about switching paint schemes. The user was apprehensive of changing his paint scheme even though he wasn't entirely satisfied with the choice. It can be disappointing to spend so much time on … Continue reading
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Client Case Study
Microsoft Corporation is engaged in developing, licensing and supporting a range of software products and services. The Company also designs and sells hardware, and delivers online advertising to the customers. It operates in five segments and th Company' products include operating systems for personal computers (PCs), servers, phones, and other intelligent devices; server applications for distributed computing environments; productivity applications; business solution applications; desktop and server management tools.
Microsoft wanted to:
Modules, activities, objectives, timing, and resources.
Final Design
List of suggested next steps.
Approach (cont)
3. Delivery
Material ordering, coordination, and shipping.
Communication of room and equipment requirements.
Facilitation, evaluation, and continuous improvement.
4. Measurement
Interesting Design Features
We recently hired LSA to help us take our client delivery methodology to the next level as we continue to grow and help our clients succeed. The custom strategy and design session exceeded our expectations. They really understood our business and delivered within tight time frames. They were the best I have seen for this type of complex, visible and strategic work. LSA's ability to get a group of 20+ executives from around the globe on the same page, push their thinking to new heights, and ensure an executable plan was excellent. I would recommend LSA to anyone looking to take their strategy and strategic execution to the next level.
Rob Sturgeon
EVP Client Delivery
What a personal and professional pleasure it has been to Partner with LSA. They are truly a strategic partner with Wyse. For more than 5 years, LSA has been engaged in delivering various training and development solutions for Wyse, from custom on-site programs targeting specific core competencies, to working individually with clients to build job skills, and offering invaluable services to me and my team as we continue to build our learning programs. LSA's representatives do more than sell a product, they offer a full service solution: they understand our business, our strategic initiatives, and the needs of our customers.
Jeff Rangel
Director of HR
We have had a very beneficial partnership with LSA for over 2 years. The recent Leadership Development Program that they designed and customized with us to help us identify, develop, and retain top leaders in the organization was a huge success. It was instrumental in helping take our company and our key people to the next level. I would recommend their training and consulting services to anyone looking to develop and retain top talent.
Steve Janowsky
Vice President of Operations
The customized new hire on-boarding process, coaching, tools, and measurement have provided a very useful structure and frame for the first 90-days for both me and our new hires. It has been a very positive experience from my perspective and will make it much easier to on-board my new hires.
Martyn Storey
Systems Engineer Manager
LSA Global has provided our Employee Engagement Survey for the past 3 years. Their ability to administer the survey, help us sort through complex organizational changes that impact reporting, and provide flexible, customized report formats gives a high level of comfort and confidence in the work. The survey results have helped us identify and take actions to address the concerns of our Gen Y workforce. In all, LSA provides us with great value, and I would highly recommend them.
Mike Normant
Senior Director, Training & Development
We came to LSA Global to improve the overall behavioral based interviewing capabilities of our managers to increase the probability of both hiring top talent and future leaders. LSA designed a highly customized solution for our unique business model and culture. I would highly recommend LSA Global as a top quality consulting and training partner.
Rae Phillips
HR Project Manager - Training & Development, PHR
We turned to LSA Global to help take our behavioral interviewing process to the next level in terms of both efficiency and effectiveness. LSA quickly understood our unique culture and challenging requirements. They immediately helped us to improve our ability to continue to hire top talent that fits our culture and talent management strategy as we rapidly scale.
Julie Pearl
Technical Program Manager
LSA has a very unique and powerful methodology regarding learning and learning strategy. LSA's ability to overcome skepticism, obtain executive support, and create an aligned learning strategy that provides measurable business impact is second to none. They partner with you to understand your goals, flex to your needs, and solve your problems – all within your budget. They certainly help make my job easier and are a pleasure to work with. I highly recommend LSA to anyone trying to connect the dots between strategy, skills, knowledge, and results.
Noelle Van Vlierbergen
Human Resources Director
LSA is an instrumental training outsourcing partner in helping us to build management capacity and strategically invest in our people. Their flexibility, depth, and breadth of expertise allows us to deliver top quality solutions that are directly tied to key business plans. Their ability to custom design, deliver, and evaluate our Management Curriculum has been excellent. They are true business partners and a pleasure to work with.
Margaret M. Mader
Vice President, Human Resources
We partnered with LSA to pilot a customized behavioral interviewing program to help ensure that we continue to hire the best and brightest as we continue to rapidly scale. LSA's ability to understand our business and our unique culture combined with their desire to ensure that the best practices framework truly fits our performance environment and is highly adopted by our people is a real blessing. Most other firms want to just 'give you training.' Not LSA, they are focused on helping me hit our hiring forecasts and attract A players.
Jennifer Remling
Director of Global Recruiting
LSA Global has been a great partner in helping ServiceSource move toward its Vision of being "the leading developer of talent in the service performance management industry." I have engaged LSA's services to help develop best-in-class programs in the areas of: Executive On-boarding, Manager Training, Sales Training, Employee Engagement and Instructional Design. They have consistently met or exceeded my (very) high expectations and are always responsive to my needs.
Mike Normant
Senior Director, Training & Development
LSA Global brought a fresh perspective to our behavioral based interviewing strategy for key executive positions that we needed to fill. Their consultant helped us to develop questions that produced a much deeper understanding of candidates' capabilities. Throughout the process LSA was also very responsive in dealing with short lead time requests. Thank you!
SPO Partners & Co
Kim Silva, CFO
I consistently look to LSA Global when I need custom programs put together for my client groups. I can always count on LSA to do a thorough needs analysis and to put together spectacular programs that are well received. Their management development and performance management expertise and professionalism make them one of my top-rated learning and development partners.
Stephanie Webb
HR Program Manager
Thank you for a great leadership development session today. The facilitator was brilliant, and we really enjoyed his style and personality. The sales team got a lot out of it (as did I). I believe it will help us create an even higher performance environment by ensuring we have a common understanding of our environmental structure and are all working towards the same goals.
Simon Jefferson
Managing Director
Thank you to LSA Global for their partnership and support in helping us to design and roll-out our valuable management development training program. I personally have partnered with LSA Global at four of my previous companies. It continues to be a delight working with LSA. We look forward to partnering with them at Blackhawk Networks for future training and consulting needs.
Suzanne Kinner
VP Human Resources
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Why some media die
Out-sourcing your local news coverage to India
Media writer Dan Kennedy continues to astound with his insightful reporting of our inky trades' descent down the slippery slope to the trash bin of history. Dan's column below is another whopper, this time about an online medium;
The Associated Press reports that the Web site Pasadena Now has decided to outsource coverage of the local city council to reporters in India. In a follow-up, the Los Angeles Times says that one of these distant journalists, based in Mumbai, will make $12,000 a year, while the other, in Bangalore, will make $7,200. They'll watch webcasts of the council meetings, consult relevant documents online and send their stories by e-mail. Who cares if they wouldn't know Pasadena from Rawalpindi?
Pasadena Now editor and publisher James Macpherson tells the Times: "A lot of the routine stuff we do can be done by really talented people in another time zone at much lower wages."
Reacting to Macpherson's quote, Kevin Roderick of LA Observed digs up a terrific rejoinder from an anonymous Pasadena blogger:
That's true, to a certain degree. The type of journalistic coverage McPherson [sic] is talking about really could be done by someone in another country, largely because their "coverage" often consists of little more than glorified press releases and parroting of the local media.
This is really quite a bit worse than the Boston Globe's decision to outsource some circulation and advertising functions to India. Indeed, they're even scratching their heads at the Hindustan Times, observing that "it remains to be seen how reporters would file their dispatches on local news — with all its flavour — from such a distant geographical location."
No telltale byline, but here's a possible example.
Dan Gillmor: "For the money he's paying, he [Macpherson] could hire local bloggers. They'd do it better, with more perspectives — and have the advantage of, uh, being there."
Dan Kennedy is a visiting assistant professor at the Northeastern University School of Journalism, a contributing writer for CommonWealth Magazine and the Boston Phoenix, and a commentator for WGBH-TV's "Greater Boston with Emily Rooney".
CapeCodToday.com welcomes thoughtful comments and the varied opinions of our readers. We are in no way obligated to post or allow comments that our moderators deem inappropriate. We reserve the right to delete comments we perceive as profane, vulgar, threatening, offensive, racially-biased, homophobic, slanderous, hateful or just plain rude. Commenters may not attack or insult other commenters, readers or writers. Commenters who persist in posting inappropriate comments will be banned from commenting on CapeCodToday.com.


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19/12/2011 16:20
Hayk Demoyan: No expiration date for recognition of genocide
"The denial of the Armenian Genocide should be punished in a European country which has democratic values as it is a manifestation of racism," said Hayk Demoyan the Director of the Armenian Genocide Museum-Institute in Yerevan, Armenia, speaking of the criminalization of the denial of Armenian Genocide by the Senate of France.
Speaking on Armenian – Turkish protocols H. Demoyan mentioned that it is a "dead" document now. Nevertheless Turkey tries to "get" something from it pushing Armenia into a historical fact proving process. However, H. Demoyan thinks it will not happen and will stay like dream for the Turkish.
As for the belief that it will not be possible to speak about the recognition of the Genocide when the 100th year of the tragic event passes Demoyan mentioned that there can't be such a thing as there is no such document where you can find an expiration date for genocide.
Recent news of this section
01/04/2020 15:19
The fact of wounding of two Armenian servicemen and 14-year old resident of Voskevan very concerning: CSTO Secretary General
CSTO Secretary General Stanislav Zas has expressed concern over the ceasefire violation that took place on March 30th at the Armenia-Azerbaijan border which resulted in two Armenian soldiers and one civilian – a 14-year-old child – being wounded.


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What is Gradient Descent?
If you've read about how neural networks are trained, you've almost certainly come across the term "gradient descent" before. Gradient descent is the primary method of optimizing a neural network's performance, reducing the network's loss/error rate. However, gradient descent can be a little hard to understand for those new to machine learning, and this article will endeavor to give you a decent intuition for how gradient descent operates.
Gradient descent is an optimization algorithm. It's used to improve the performance of a neural network by making tweaks to the parameters of the network such that the difference between the network's predictions and the actual/expected values of the network (referred to as the loss) is a small as possible. Gradient descent takes the initial values of the parameters and uses operations based in calculus to adjust their values towards the values that will make the network as accurate as it can be. You don't need to know a lot of calculus to understand how gradient descent works, but you do need to have an understanding of gradients.
What Are Gradients?
Assume that there is a graph that represents the amount of error a neural network makes. The bottom of the graph represents the points of lowest error while the top of the graph is where the error is the highest. We want to move from the top of the graph down to the bottom. A gradient is just a way of quantifying the relationship between error and the weights of the neural network. The relationship between these two things can be graphed as a slope, with incorrect weights producing more error. The steepness of the slope/gradient represents how fast the model is learning.
A steeper slope means large reductions in error are being made and the model is learning fast, whereas if the slope is zero the model is on a plateau and isn't learning. We can move down the slope towards less error by calculating a gradient, a direction of movement (change in the parameters of the network) for our model.
Let's shift the metaphor just slightly and imagine a series of hills and valleys. We want to get to the bottom of the hill and find the part of the valley that represents the lowest loss. When we start at the top of the hill we can take large steps down the hill and be confident that we are heading towards the lowest point in the valley.
However, as we get closer to the lowest point in the valley, our steps will need to become smaller, or else we could overshoot the true lowest point. Similarly, it's possible that when adjusting the weights of the network, the adjustments can actually take it further away from the point of lowest loss, and therefore the adjustments must get smaller over time. In the context of descending a hill towards a point of lowest loss, the gradient is a vector/instructions detailing the path we should take and how large our steps should be.
Now we know that gradients are instructions that tell us which direction to move in (which coefficients should be updated) and how large the steps we should take are (how much the coefficients should be updated), we can explore how the gradient is calculated.
Calculating Gradients and Gradient Descent Procedure
In order to carry out gradient descent, the gradients must first be calculated. In order to calculate the gradient, we need to know the loss/cost function. We'll use the cost function to determine the derivative. In calculus, the derivative just refers to the slope of a function at a given point, so we're basically just calculating the slope of the hill based on the loss function. We determine the loss by running the coefficients through the loss function. If we represent the loss function as "f", then we can state that the equation for calculating the loss is as follows (we're just running the coefficients through our chosen cost function):
Loss = f(coefficient)
We then calculate the derivative, or determine the slope. Getting the derivative of the loss will tell us which direction is up or down the slope, by giving us the appropriate sign to adjust our coefficients by. We'll represent the appropriate direction as "delta".
delta = derivative_function(loss)
We've now determined which direction is downhill towards the point of lowest loss. This means we can update the coefficients in the neural network parameters and hopefully reduce the loss. We'll update the coefficients based on the previous coefficients minus the appropriate change in value as determined by the direction (delta) and an argument that controls the magnitude of change (the size of our step). The argument that controls the size of the update is called the "learning rate" and we'll represent it as "alpha".
coefficient = coefficient – (alpha * delta)
We then just repeat this process until the network has converged around the point of lowest loss, which should be near zero.
It's very important to choose the right value for the learning rate (alpha). The chosen learning rate must be neither too small or too large. Remember that as we approach the point of lowest loss our steps must become smaller or else we will overshoot the true point of lowest loss and end up on the other side. The point of smallest loss is small and if our rate of change is too large the error can end up increasing again. If the step sizes are too large the network's performance will continue to bounce around the point of lowest loss, overshooting it on one side and then the other. If this happens the network will never converge on the true optimal weight configuration.
In contrast, if the learning rate is too small the network can potentially take an extraordinarily long time to converge on the optimal weights.
Types Of Gradient Descent
Now that we understand how gradient descent works in general, let's take a look at some of the different types of gradient descent.
Batch Gradient Descent: This form of gradient descent runs through all the training samples before updating the coefficients. This type of gradient descent is likely to be the most computationally efficient form of gradient descent, as the weights are only updated once the entire batch has been processed, meaning there are fewer updates total. However, if the dataset contains a large number of training examples, then batch gradient descent can make training take a long time.
Stochastic Gradient Descent: In Stochastic Gradient Descent only a single training example is processed for every iteration of gradient descent and parameter updating. This occurs for every training example. Because only one training example is processed before the parameters are updated, it tends to converge faster than Batch Gradient Descent, as updates are made sooner. However, because the process must be carried out on every item in the training set, it can take quite a long time to complete if the dataset is large, and so use of one of the other gradient descent types if preferred.
Mini-Batch Gradient Descent: Mini-Batch Gradient Descent operates by splitting the entire training dataset up into subsections. It creates smaller mini-batches that are run through the network, and when the mini-batch has been used to calculate the error the coefficients are updated. Mini-batch Gradient Descent strikes a middle ground between Stochastic Gradient Descent and Batch Gradient Descent. The model is updated more frequently than in the case of Batch Gradient Descent, which means a slightly faster and more robust convergence on the model's optimal parameters. It's also more computationally efficient than Stochastic Gradient Descent
What is Bayes Theorem?
If you've been learning about data science or machine learning, there's a good chance you've heard the term "Bayes Theorem" before, or a "Bayes classifier". These concepts can be somewhat confusing, especially if you aren't used to thinking of probability from a traditional, frequentist statistics perspective. This article will attempt to explain the principles behind Bayes Theorem and how it's used in machine learning.
Defining Bayes Theorem
Bayes Theorem is a method of calculating conditional probability. The traditional method of calculating conditional probability (the probability that one event occurs given the occurrence of a different event) is to use the conditional probability formula, calculating the joint probability of event one and event two occurring at the same time, and then dividing it by the probability of event two occurring. However, conditional probability can also be calculated in a slightly different fashion by using Bayes Theorem.
When calculating conditional probability with Bayes theorem, you use the following steps:
- Determine the probability of condition B being true, assuming that condition A is true.
- Determine the probability of event A being true.
- Multiply the two probabilities together.
- Divide by the probability of event B occurring.
This means that the formula for Bayes Theorem could be expressed like this:
Calculating the conditional probability like this is especially useful when the reverse conditional probability can be easily calculated, or when calculating the joint probability would be too challenging.
A Practical Example
This might be easier to interpret if we spend some time looking at an example of how you would apply Bayesian reasoning and Bayes Theorem. Let's assume you were playing a simple game where multiple participants tell you a story and you have to determine which one of the participants is lying to you. Let's fill in the equation for Bayes Theorem with the variables in this hypothetical scenario.
We're trying to predict whether each individual in the game is lying or telling the truth, so if there are three players apart from you, the categorical variables can be expressed as A1, A2, and A3. The evidence for their lies/truth is their behavior. Like when playing poker, you would look for certain "tells" that a person is lying and use those as bits of information to inform your guess. Or if you were allowed to question them it would be any evidence their story doesn't add up. We can represent the evidence that a person is lying as B.
To be clear, we're aiming to predict Probability(A is lying/telling the truth|given the evidence of their behavior). To do this we'd want to figure out the probability of B given A, or the probability that their behavior would occur given the person genuinely lying or telling the truth. You're trying to determine under which conditions the behavior you are seeing would make the most sense. If there are three behaviors you are witnessing, you would do the calculation for each behavior. For example, P(B1, B2, B3 * A). You would then do this for every occurrence of A/for every person in the game aside from yourself. That's this part of the equation above:
Finally, we just divide that by the probability of B.
If we received any evidence about the actual probabilities in this equation, we would recreate our probability model, taking the new evidence into account. This is called updating your priors, as you update your assumptions about the prior probability of the observed events occurring.
Machine Learning Applications
The most common use of Bayes theorem when it comes to machine learning is in the form of the Naive Bayes algorithm.
Naive Bayes is used for the classification of both binary and multi-class datasets, Naive Bayes gets its name because the values assigned to the witnesses evidence/attributes – Bs in P(B1, B2, B3 * A) – are assumed to be independent of one another. It's assumed that these attributes don't impact each other in order to simplify the model and make calculations possible, instead of attempting the complex task of calculating the relationships between each of the attributes. Despite this simplified model, Naive Bayes tends to perform quite well as a classification algorithm, even when this assumption probably isn't true (which is most of the time).
There are also commonly used variants of the Naive Bayes classifier such as Multinomial Naive Bayes, Bernoulli Naive Bayes, and Gaussian Naive Bayes.
Multinomial Naive Bayes algorithms are often used to classify documents, as it is effective at interpreting the frequency of words within a document.
Bernoulli Naive Bayes operates similarly to Multinomial Naive Bayes, but the predictions rendered by the algorithm are booleans. This means that when predicting a class the values will be binary, no or yes. In the domain of text classification, a Bernoulli Naive Bayes algorithm would assign the parameters a yes or no based on whether or not a word is found within the text document.
If the value of the predictors/features aren't discrete but are instead continuous, Gaussian Naive Bayes can be used. It's assumed that the values the continuous features have been sampled from a gaussian distribution.
What are RNNs and LSTMs in Deep Learning?
Many of the most impressive advances in natural language processing and AI chatbots are driven by Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. RNNs and LSTMs are special neural network architectures that are able to process sequential data, data where chronological ordering matters. LSTMs are essentially improved versions of RNNs, capable of interpreting longer sequences of data. Let's take a look at how RNNs and LSTMS are structured and how they enable the creation of sophisticated natural language processing systems.
Feed-Forward Neural Networks
So before we talk about how Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) work, we should discuss the format of a neural network in general.
A neural network is intended to examine data and learn relevant patterns, so that these patterns can be applied to other data and new data can be classified. Neural networks are divided into three sections: an input layer, a hidden layer (or multiple hidden layers), and an output layer.
The input layer is what takes in the data into the neural network, while the hidden layers are what learn the patterns in the data. The hidden layers in the dataset are connected to the input and output layers by "weights" and "biases" which are just assumptions of how the data points are related to each other. These weights are adjusted during training. As the network trains, the model's guesses about the training data (the output values) are compared against the actual training labels. During the course of training, the network should (hopefully) get more accurate at predicting relationships between data points, so it can accurately classify new data points. Deep neural networks are networks that have more layers in the middle/more hidden layers. The more hidden layers and more neurons/nodes the model has, the better the model can recognize patterns in the data.
Regular, feed-forward neural networks, like the ones I've described above are often called "dense neural networks". These dense neural networks are combined with different network architectures that specialize in interpreting different kinds of data.
Recurrent Neural Networks
Recurrent Neural Networks take the general principle of feed-forward neural networks and enable them to handle sequential data by giving the model an internal memory. The "Recurrent" portion of the RNN name comes from the fact that the input and outputs loop. Once the output of the network is produced, the output is copied and returned to the network as input. When making a decision, not only the current input and output are analyzed, but the previous input is also considered. To put that another way, if the initial input for the network is X and the output is H, both H and X1 (the next input in the data sequence) are fed into the network for the next round of learning. In this way, the context of the data (the previous inputs) is preserved as the network trains.
The result of this architecture is that RNNs are capable fo handling sequential data. However, RNNs suffer from a couple of issues. RNNs suffer from the vanishing gradient and exploding gradient problems.
The length of sequences that an RNN can interpret are rather limited, especially in comparison to LSTMs.
Long Short-Term Memory Networks
Long Short-Term Memory networks can be considered extensions of RNNs, once more applying the concept of preserving the context of inputs. However, LSTMs have been modified in several important ways that allow them to interpret past data with superior methods. The alterations made to LSTMs deal with the vanishing gradient problem and enable LSTMs to consider much longer input sequences.
LSTM models are made up of three different components, or gates. There's an input gate, an output gate, and a forget gate. Much like RNNs, LSTMs take inputs from the previous timestep into account when modifying the model's memory and input weights. The input gate makes decisions about which values are important and should be let through the model. A sigmoid function is used in the input gate, which makes determinations about which values to pass on through the recurrent network. Zero drops the value, while 1 preserves it. A TanH function is used here as well, which decides how important to the model the input values are, ranging from -1 to 1.
After the current inputs and memory state are accounted for, the output gate decides which values to push to the next time step. In the output gate, the values are analyzed and assigned an importance ranging from -1 to 1. This regulates the data before it is carried on to the next time-step calculation. Finally, the job of the forget gate is to drop information that the model deems unnecessary to make a decision about the nature of the input values. The forget gate uses a sigmoid function on the values, outputting numbers between 0 (forget this) and 1 (keep this).
An LSTM neural network is made out of both special LSTM layers that can interpret sequential word data and the densely connected like those described above. Once the data moves through the LSTM layers, it proceeds into the densely connected layers.
What is K-Nearest Neighbors?
K-Nearest Neighbors is a machine learning technique and algorithm that can be used for both regression and classification tasks. K-Nearest Neighbors examines the labels of a chosen number of data points surrounding a target data point, in order to make a prediction about the class that the data point falls into. K-Nearest Neighbors (KNN) is a conceptually simple yet very powerful algorithm, and for those reasons, it's one of the most popular machine learning algorithms. Let's take a deep dive into the KNN algorithm and see exactly how it works. Having a good understanding of how KNN operates will let you appreciated the best and worst use cases for KNN.
An Overview Of KNN
Let's visualize a dataset on a 2D plane. Picture a bunch of data points on a graph, spread out along the graph in small clusters. KNN examines the distribution of the data points and, depending on the arguments given to the model, it separates the data points into groups. These groups are then assigned a label. The primary assumption that a KNN model makes is that data points/instances which exist in close proximity to each other are highly similar, while if a data point is far away from another group it's dissimilar to those data points.
A KNN model calculates similarity using the distance between two points on a graph. The greater the distance between the points, the less similar they are. There are multiple ways of calculating the distance between points, but the most common distance metric is just Euclidean distance (the distance between two points in a straight line).
KNN is a supervised learning algorithm, meaning that the examples in the dataset must have labels assigned to them/their classes must be known. There are two other important things to know about KNN. First, KNN is a non-parametric algorithm. This means that no assumptions about the dataset are made when the model is used. Rather, the model is constructed entirely from the provided data. Second, there is no splitting of the dataset into training and test sets when using KNN. KNN makes no generalizations between a training and testing set, so all the training data is also used when the model is asked to make predictions.
How The KNN Algorithm Operates
A KNN algorithm goes through three main phases as it is carried out:
- Setting K to the chosen number of neighbors.
- Calculating the distance between a provided/test example and the dataset examples.
- Sorting the calculated distances.
- Getting the labels of the top K entries.
- Returning a prediction about the test example.
In the first step, K is chosen by the user and it tells the algorithm how many neighbors (how many surrounding data points) should be considered when rendering a judgment about the group the target example belongs to. In the second step, note that the model checks the distance between the target example and every example in the dataset. The distances are then added into a list and sorted. Afterward, the sorted list is checked and the labels for the top K elements are returned. In other words, if K is set to 5, the model checks the labels of the top 5 closest data points to the target data point. When rendering a prediction about the target data point, it matters if the task is a regression or classification task. For a regression task, the mean of the top K labels is used, while the mode of the top K labels is used in the case of classification.
The exact mathematical operations used to carry out KNN differ depending on the chosen distance metric. If you would like to learn more about how the metrics are calculated, you can read about some of the most common distance metrics, such as Euclidean, Manhattan, and Minkowski.
Why The Value Of K Matters
The main limitation when using KNN is that in an improper value of K (the wrong number of neighbors to be considered) might be chosen. If this happen, the predictions that are returned can be off substantially. It's very important that, when using a KNN algorithm, the proper value for K is chosen. You want to choose a value for K that maximizes the model's ability to make predictions on unseen data while reducing the number of errors it makes.
Lower values of K mean that the predictions rendered by the KNN are less stable and reliable. To get an intuition of why this is so, consider a case where we have 7 neighbors around a target data point. Let's assume that the KNN model is working with a K value of 2 (we're asking it to look at the two closest neighbors to make a prediction). If the vast majority of the neighbors (five out of seven) belong to the Blue class, but the two closest neighbors just happen to be Red, the model will predict that the query example is Red. Despite the model's guess, in such a scenario Blue would be a better guess.
If this is the case, why not just choose the highest K value we can? This is because telling the model to consider too many neighbors will also reduce accuracy. As the radius that the KNN model considers increases, it will eventually start considering data points that are closer to other groups than they are the target data point and misclassification will start occurring. For example, even if the point that was initially chosen was in one of the red regions above, if K was set too high, the model would reach into the other regions to consider points. When using a KNN model, different values of K are tried to see which value gives the model the best performance.
KNN Pros And Cons
Let's examine some of the pros and cons of the KNN model.
KNN can be used for both regression and classification tasks, unlike some other supervised learning algorithms.
KNN is highly accurate and simple to use. It's easy to interpret, understand, and implement.
KNN doesn't make any assumptions about the data, meaning it can be used for a wide variety of problems.
KNN stores most or all of the data, which means that the model requires a lot of memory and its computationally expensive. Large datasets can also cause predictions to be take a long time.
KNN proves to be very sensitive to the scale of the dataset and it can be thrown off by irrelevant features fairly easily in comparison to other models.
Summing Up
K-Nearest Neighbors is one of the simplest machine learning algorithms. Despite how simple KNN is, in concept, it's also a powerful algorithm that gives fairly high accuracy on most problems. When you use KNN, be sure to experiment with various values of K in order to find the number that provides the highest accuracy.


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More odd fishes and cool stuff
Deep sea fish are often 'red' fish, in that they have red, orange, and purple coloration, since red is the first color to get stopped by water...at depth these fish are close to black, but on the surface or in the shallows a cool array of sunset colors.
First I added two Cardinal fish...popular aquarium fish all over.The Banggai cardinalfish is a popular aquarium fish but int he wild has a very limited range, on reefs around the Banggai Islands in Indonesia. It is not red, but just interesting in general.
A relative is more orange, the Pajama or Spotted Cardinalfish (fyi, they don't look like the bird or the churchman, wonder if they are important in something?), which has some orange on the fins.
A redder fish family is the Squirrelfish (big eyes = squirrel-like maybe?), which have scales and stripes, and are again a popular saltwater reef fish family. Drew two common types: Squirrelfish of the Atlantic, and the Hawaiian Squirrelfish of the Pacific. The Hawaiian model, like the Hawaiian shirt is a bit more colorful.
I also added the deep water Queen Snapper, a purplish-reddish-Atlantic fish caught in water from a few hundred to thousand feet deep.
The same fishes are on patterns and decor also at my Spoonflower site (or soon will be).
Tight lines, calm seas, and BE SAFE!
Bryce L. Meyer


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How to Save Time on diary selling with IFTTT
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In Her New Book 'What Happened', Hillary Clinton Blames Many for Defeat. Including Herself.
At the height of the scandal over her email server, Hillary Clinton was "tempted to make voodoo dolls of certain members of the press and Congress and stick them full of pins," she writes in a new memoir of her 2016 presidential campaign.
"Mostly, I was furious at myself," she writes, adding: "It was a dumb mistake. But an even dumber 'scandal.'"
That "yes, but" tone defines Clinton's "What Happened," a book obtained by NBC News through a source who had purchased it ahead of its official publication Tuesday. The book offers her understanding of one of the biggest upsets in presidential history.
Song pick of the week... Pink....


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Auto Repair - No Appointment Necessary near Springfield, VA
When your vehicle is making strange sounds driving down the road, it's best to have it inspected by a professional sooner rather than later. If your hectic lifestyle is keeping you from getting the repairs you need, consider visiting the Service Department at the Pohanka Automotive Group. In addition to offering unrivaled care and service, we never require you to make an appointment before coming to see us, so you can swing by at your own convenience!
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Here at Pohanka, we pride ourselves in being a bit different from the rest. This means upholding our storied history of excellence each and every day through outstanding customer service. Our family has been in the business since 1919, so we understand that it takes honesty, respect, and fairness to build such an enduring reputation. When you walk into our showroom, we think you notice the difference! No matter what brings you through our doors, you'll be greeted with a smile by one of our professionals, who will direct you to the appropriate department.
Auto Servicing
Though it may seem inconsequential to delay repairs and maintenance, doing so can have serious consequences on the health and longevity of your favorite set of wheels. Therefore, whenever something seems off or it's been awhile since someone looked it over, you can bring your ride by our service bay at any time, as we don't require appointments. That way, you don't have to worry about clearing an afternoon weeks ahead of time just to receive quality care. Simply show up, hand over the keys, and we'll take it from there.
Intuitive Tools
At any point in time, you're more than welcome to utilize the resources on our website to make your life a bit easier. Although appointments are never necessary, we do have online scheduling form if for any reason you prefer to have a specified time slot for your vehicle's maintenance. Additionally, we know that things happen from time to time, so if you ever have a question, you can always send it our way with the Ask a Tech form before making the trip out to see us. Our goal is become your one-stop shop for all of your vehicle's needs, so let us know if there's any other way we can help!
Do you need auto repair – no appointment necessary near Springfield, VA? The Pohanka team is prepared to assist! With convenient locations throughout the area, it shouldn't be too difficult to find a Pohanka dealership near you. See you soon!
Dealership Information
Service Hours
|6:00 am - 7:00 pm
|6:00 am - 7:00 pm
|6:00 am - 7:00 pm
|6:00 am - 7:00 pm
|6:00 am - 7:00 pm
|6:00 am - 5:00 pm
" Great service! "
" Great experience. Hunter from financing did a great job finalizing the sale. "
" Excellent dealer, sales, financing, and managers were great to work with and it was a nice buying experience. They gave us a great deal on top of great service! "
" Always great! "
"17 February 2020, Pohanka Service"
" I arrived at 0700 just a little after the doors had opened and warmly greeted by Ryan Bartholomew, my Service Technician. He is a pleasure to work with and he goes the extra mile to ensure that your repairs and vehicle needs are attended to. Moreover, Ryan and I have been connected since I purchased my vehicle from Pohanka and he can ALWAYS call me by name when he sees me. I come an hour away (Bowie, MD) for the customer service, to have my 2013 Suburban cared for. Job well done Ryan and Pohanka Chevorlet! "
" Fantastic dealership. Amad Tariq and Hunter Bowles were excellent to deal with. Highly recommend! "
" They had the car I wanted and worked to get it to me at the price I could afford. Anuj was prompt and professional with all contact, over e-mail and in person. This is the second car I've leased from Pohanka, and I don't know why I would visit any other Chevy Dealer. "
" See Ryan he's the main reason I go there. "
" This was the fastest and best used car experience I've had. David Poole, Juan Andrade, and Hunter Bowles were all very courteous and professional. A great value on a good used car with a wonderful customer experience. I would definitely go back to this dealership! "
" My wife and I just bought a used car from Pohanka Chevrolet and had an excellent experience! We loved working with our manager David Poole. I called and spoke with him a couple days before we bought the car. He texted me a couple days later and we worked out the arrangements before we even went to the dealership. This was great since we live over an hour away. When we got to the dealership Juan Andrade went on a test drive with us. He was very professional. Once we decided to buy the car we got in right away to finalize the deal. Hunter Bowles, who is the finance manager, processed a registration transfer for us right there then printed out a new registration card. Thank you for a fabulous experience!! "
Amad Tariq and Pohanka
" The experience was very positive. The sales associate focused on our needs and wanted to work with us to reach an agreeable negotiated price. Was very pleased with the final negotiated sales price. At all times, the sales associate was very transparent with us and he was focused on meeting our needs. I will recommend Pohanka to friends in the market for new or used car. "
" My Service Advisor was excellent today I was in and out in under an hour for an oil change and tire rotation. I don't know my advisor's name but she was very nice very punctual and well educated. I will return. "
" Parts person forgot to order my part caused me to go 3 more days without a car "
" Just picked up my new Blazer yesterday. My sales consultant, Anuj Bharadwaj, was great! This is the 2nd car I've bought from him and my son has also purchased a car from Anuj. He is always the ultimate, gentleman, actively listens to my concerns and doesn't pressure me on anything. I always find buying a car from him to be a pleasant experience. In addition, meeting the Business Manager, Hunter Bowles, was also a good interaction. He has wonderful customer service skills and is also pleasant to deal with. He thoroughly explained all my options for extended warranties and found me the best interest rate. I have been buying from Pohanka Chevrolet for 20 years. I do recommend this car dealership. "
Professional and low pressure.
" Jason provided a first class sales experience. He was friendly, knowledgeable, and provided just the right amount of follow up. He knew the vehicle and was able to answer all of my questions. He also did a good job explaining the closing documents. I highly recommend his services. "
" Very good customer service "
" I came in to have 2 recalls fixed on my vehicle. I tried calling another dealership who I ended up contacting 3x and they never got back to me. I called Pohanka and they ordered the parts for me the next day. My service consultant was Adrian and he was so nice. I hate going to car dealerships as a woman because they can tell I'm totally not a car person and try to take advantage. He was really nice and made me feel very comfortable. They even offered to shuttle me to and from my house while they fixed my car! I ended up getting an oil change as well. Would def return. Thank you Adrian! "
" Jason is incredible. Super helpful not pushy. Offered an excellent deal "
" Great experience. I worked with Jason Buckley, made the deal over the phone/emails because I am out of the area. They provided me a ride from the airport and the vehicle was ready upon my arrival. I highly recommend them, their price couldn't be beat in my area. "
" Great experience and quick. Love the Colorado and plan to be back for another in 39 months "
" Excellent as usual. In and out no appointment needed. "
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Great Experience
" Working with Pohanka Chevrolet was a great experience and they couldn't have made the process any easier. Andrew Brunt was very attentive to what I was looking for without the high pressure sales pitch. I was able to take advantage of the employee pricing promo which allowed me to purchase a 6 yr 75000 mile extended warranty. Hunter Bowled explained all of the details of the warranty to my wife and I and was able to provide a price that we can afford. I will definitely return for my next vehicle and will tell my friends to visit as well. "
" Working with Pohanka Chevrolet was a great experience and they couldn't have made the process any easier. Andrew Brunt was very attentive to what I was looking for without the high pressure sales pitch. I was able to take advantage of the employee pricing promo which allowed me to purchase a 6 yr 75000 mile extended warranty. Hunter Bowles explained all of the details of the warranty to my wife and I and was able to provide a price that we can afford. I will definitely return for my next vehicle and will tell my friends to visit as well. "


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iOS Apps With Ford SDK
Ford is currently installed in 46 apps. As of 2019-11-01, it was installed in 47 apps. That is a change of -2% over six months.
Monthly Installs And Uninstalls of Ford SDK
Ford was installed in 0 apps and uninstalled in 0 apps last month. That's a net change of 0 apps over the last month.
apps in the top 200 apps
Ford has 3% of the top 200 apps, for a total of 5 apps.
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Rico Nasty is an American rapper and record producer. Her full name is Maria-Cecilia Simone Kelly. Rico first started rapping in high school and has released five mixtapes. Her single \"Poppin\" has 4.
6 million views on YouTube and was featured on Issa rae\'s HBO show \"Insecure\". She signed with Atlantic Records in June 2018 and released her mixtape Nasty. In 2019, she was named a Forbes 30 Under 30 honoree in music.
PhotosNo additional photos.
This website is a resource for event professionals and strives to provide the most comprehensive catalog of popular musicians and bands to consider for performances at corporate events, fundraising galas, or university events. A listing or profile on this website does not imply an agency affiliation or endorsement by the talent.
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Vivo V19 (Global) debuts with dual-selfie cameras and Snapdragon 712 SoC
Vivo V19 smartphone was scheduled to launch in India on March 26 but it was postponed due to the ongoing lockdown.
Vivo Y50 launched with 5000mAh battery and quad-camera setup
Vivo Y50 features a 6.53-inch full HD+ punch-hole display with a resolution of 1080 x 2340 pixels.
Vivo S6 5G announced with 48MP quad rear cameras, Exynos 980, 4500mAh battery
Vivo S6 5G runs Android 10 with Funtouch OS 10 and features an in-display fingerprint scanner.
Coronavirus Impact: Xiaomi Mi 10, Realme Narzo 10, Narzo 10A ,Vivo V19, Redmi Note 9 Pro Max sale and launch postponed
Here's is a list of smartphones that have been postponed due to Coronavirus outbreak.
Coronavirus Impact: Apple, Oppo, Vivo, Realme, Xiaomi, Samsung and more shut down manufacturing in India
The list includes Oppo, Vivo, Realme, Apple, Samsung, LG, Motorola, Xiaomi and more.


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House in Orihuela costa for 6 people
by Mustapha C.
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City: Beg-Meil
Country: France
Region: Bretagne
District: Finistère
Tourist region: Pays de Fouesnant
Address: Résidence Duchesse Anne 1 rue Saint Guénolé
Proximity: 3 km from Fousnant , 15 km from Concarneau , 150 m from sea
Capacity: 2 people , 1 double bed
Type: Studio in building , 1 room, Entresol , floor 2 , out of 3 floors in total, lift, independant access, surface 23 sqm
Description: 1 bedroom, 1 bathroom, 1 WC, kitchennette, with balcony, Facing S
Appliances/Equipments: fridge, oven, hobs, microwave, washing machine, vacuum cleaner, iron, fan, central heating, TV, garden furniture
Activities: golf, tennis, fishing, ice-skating, swimming, bowling, water-sports, hiking, moutain biking, thalassotherapy, casino, cinema, night-clubbing
Landlord information
Name of owner/contact: Evelyne Fatras
Contact details: Tel: 02.98.35.40.48
Spoken languages: Français
Very high season price: 320 € per week
High season price: 260 € per week
Medium season price: 260 € per week
Low season price: 220 € per week
Other information
Last update on 08-05-2018 10hLink to Contact form >
Contact form
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Other nearby destinations: Brigneau Tourism (39) | Melgven Holiday (48) | Beg-meil (43) | Le Juch (39) | Chateaulin rentals (18) | rentals La Forêt-fouesnant (68) | Tourism Plogonnec (184) | Holiday Ruolh (21) | holiday home 29350 (42) |
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Kane is irreplaceable – Mourinho Tottenham striker Harry Kane has been ruled out until April after the club said he needs surgery on a hamstring injury. Kane, 26, suffered the injury during Tottenham's defeat at Southampton on New Year's Day. No timescale was originally given on the England captain's return but Spurs now say specialists […]Read more
Sissoko injured his right knee at Southampton on New Year's Day Tottenham Hotspur midfielder Moussa Sissoko has been ruled out until April after having knee surgery. Sissoko, 30, injured the medial collateral ligament (MCL) in his right knee during Spurs' 1-0 defeat at Southampton on New Year's Day. The France international has featured in 20 […]Read more
FA Cup: Chelsea 2-0 Nottingham Forest highlights Callum Hudson-Odoi scored one goal and made another as Chelsea beat Nottingham Forest in their FA Cup third round tie. The hosts only needed six minutes to score as Hudson-Odoi cut in from the right and shot into the bottom corner. Forest, fourth in the Championship, were then […]Read more
Image copyright Getty Images Image caption Prince William visited West Bromwich Albion last year as part of the Heads Up mental health campaign A short film narrated by the Duke of Cambridge will be played at FA Cup matches to encourage football fans to look after their mental health. The film will be played in […]Read more
Image copyright PA Media Image caption Fireworks are let off from Edinburgh Castle as part of the Hogmanay New Year celebrations Police are warning people not to attend New Year celebrations on the River Thames in London without a ticket, as spectators gather to usher in 2020. More than 100,000 tickets have been bought for […]Read more
Image copyright PA Media Image caption A council worker removed the graffiti which someone had covered with a note wishing all people "Peace and Good Will" Anti-Semitic graffiti has been daubed on a synagogue and several shops in north London during the Jewish festival of Hanukkah. The Star of David, a Jewish holy symbol, and […]Read more
Image copyright Getty Images George Michael's sister, Melanie Panayiotou, has died aged 55, exactly three years after her brother. The family confirmed in a statement that Melanie "passed away suddenly" over Christmas. Her brother, pop icon George Michael, died on Christmas Day 2016 at his home in Goring-on-Thames, Oxfordshire. Andrew Ridgeley, George's former Wham! bandmate, […]Read more
Image caption Sadiq Khan helped serve 100 Christmas Dinners to guests at City Hall Christmas dinners have been served to Londoners who are reliant on the city's homelessness services. Hairdressers and opticians were also made available at City Hall before guests were given a three-course meal. Last year, 8,855 people were seen rough sleeping in […]Read more
Image copyright Met Police Image caption Michael Paul Baptista died after an altercation in Waltham Forest on Thursday Police have named 24-year-old Michael Paul Baptista as the man killed in a stabbing in London last week. Mr Baptista, of Waltham Forest, north-east London, was stabbed to death in nearby Walthamstow on Thursday evening. Police were […]Read more
Pierre-Emerick Aubameyang had the game's best chance early in the second half but he was thwarted by Everton keeper Jordan Pickford Everton and Arsenal played out a dire goalless draw at Goodison Park as respective new bosses Carlo Ancelotti and Mikel Arteta watched from the stands. Three-time Champions League winner Ancelotti was confirmed as successor […]Read more


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You Can Rely on The Quiz
Marnie N
When the planet is in turmoil and all around you are sad because the World Cup is over, fear not, The Marquis Cornwallis Quiz is back to help you through tough times and brain fries! "One Man Band" came alone and may have not one by the most amount (there are no losers at The Marquis!) but he got a free beer in round one! Newbies "Dropkicks" and "2 Quiz 2 Furious" were mid-table and gave a solid performance but the night was meant for "In Defence of A4" who led from the front and took the pot of cash and the glory! Join us next week for more fun and good times!Disclaimer: Blog posts are written by our Quiz hosts. Any opinions or views expressed may or may not be shared by Question One Limited. If you feel the material here is inappropriate please report it to us so we may address it.
Top Teams
- 35.0
- In Defence of A4
- 32.0
- Whole Hog
- 30.0
- 2 Quiz 2 Furious
- 30.0
- Dropkicks
- 19.0
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You Can Rely on The Quiz @marquiswc1! #Quiz #Bloomsbury https://t.co/rS1T2gkzSD pic.twitter.com/LzKl1kwz8j— Marquis Cornwallis (@marquiswc1) July 18, 2018


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by Mohamed Rias
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bindu - has written 35 posts on Smashing Tips
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