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Start Dating Singles in Orange County Finding a soulmate for a serious relationship is easier today than ever, because you have the internet with the best dating service.Start your visit by driving your car through the acre Buffalo Ranch. These two 2,pound beasts—bred from brahmas and buffalo—are said to be the largest buffalo bulls in the entire United States.
Buffalo are Asian animals. The Buffalo Ranch began with 74 bison, but the number has been increasing. Sometimes the population of buffalo goes down rather than up. A few of the animals you see through your car window will wind up as Buffaloburgers here at the Ranch or on dinner plates somewhere else. Do you see the white silo next to the barn? At the base of the white silo, you can order Buffaloburgers made from meat raised on the Ranch.
Texas Tiny, the pound country disc jockey, has a big appetite. Dick Dale invented surf music, but T. Texas Tiny invented the name Dick Dale. Step up to the window and place your order. The menu above the order window is simple:. In the picture above, a Buffaloburger and a paper tray of beans rest on the sill. A real Buffaloburger is quite a bit smaller than what T. Texas Tiny is holding.
Do you have children with you today? The Burro Ride puts your children onto the backs of trained ring burros.
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The Martin Tractor Company has been serving Kansas farmers since You can take horseback riding lessons. Or sign your group for a moon-lit hayride on a horse-drawn wagon. Once there were over a hundred buffalo grazing on the hills north of Corona del Mar.
Now there are apartments, condominiums, and very wide roads. InGene Clark, an ex-building contractor from Independence, Kansas, leased acres of rolling grasslands from the Irvine Company. The buffalo would arrive Saturday. The public would be admitted Sunday, even though the official opening would not be until early In NovemberClark announced plans for a 2,seat auditorium for conferences, entertainment, and television programs; the 11, square-foot addition to the Buffalo Ranch would be themed to look like a traditional hay barn.
But arguably it was the most unusual. By the late s, the area around the Buffalo Ranch was changing. Across MacArthur Blvd. Plans were in the works for a University of California campus a short distance from the Buffalo Ranch.
The land had become too valuable to use as a tourist attraction in the five years since the Ranch had opened. According to the newspaper, the operator was looking for a buyer for his herd of 17 buffalo. Just 17 buffalo?Elite Connections International is the most exclusive Orange County matchmaking service with twenty-five years of unrivaled achievements.
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Start a new group. Sign up. Singles groups in Orange Here's a look at some Singles groups near Orange. Join Meetup. Let's Meetup! Orange, CA.Meet Single Women In Orange County
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Hanging Out Having Fun! Southern California Christian Singles. Lakewood Single Moms Meetup Group.Then the entire layer transforms an input vector into a new intermediate feature vector.
This new vector is fed as input to another layer of nodes. This process continues layer by layer, until we reach the final output layer of nodes, where the output is the network's prediction: an array of per-class probabilities for classification problems or a single, real value for regression problems. The network architectures supported by BigML can be deep or shallow. The advantage of training deep architectures is that hidden layers have the opportunity to learn higher-level representations of the data that can be used to make correct predictions in cases where a direct mapping between input and output is difficult.
For example, when classifying images of numeric digits, the input layer is raw pixels, the output layer is the probability for each digit, and the intermediate layers may learn features that represent the presence of, say, a loop or a vertical stroke. Deep Neural Networks are notoriously sensitive to the chosen topology and the algorithm used to optimize the parameters thereof. This sensitivity means that hand-tuning the topology and optimization algorithm can be difficult and time-consuming as the number of choices that lead to poor networks typically vastly outnumber the choices that lead to good ones.
To combat this problem, BigML offers first class support for automatic network topology search and parameter optimization. The algorithm BigML uses is a variant on the hyperband algorithm.
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Instead of selecting candidates for evaluation at random, however, we use an acquisition technique based on techniques from Bayesian parameter optimization. You can also list all of your deepnets. This argument can be used to change the names of the fields in the models of the deepnet with respect to the original names in the dataset.
All the fields in the dataset Specifies the fields to be included as predictors in the models of the deepnet. Example: false name optional String,default is dataset's deepnet The name you want to give to the new deepnet. One per objective class.
Each entry is map containing the specific parameters for the algorithm. See the Optimizer Object for more details. Specifies the type of ordering followed to build the models of the deepnet. The range of successive instances to build the models of the deepnet. The final deepnet returned by the search is a compromise between the top n networks found in the search. Example: true seed optional String A string to be hashed to generate deterministic samples.
Dataset sampling doesn't apply to evaluations for time series. BigML has learned some general rules about what makes one network structure better than another for a given dataset. Given your dataset, BigML will automatically suggest a structure and a set of parameter values that are likely to perform well for your dataset. This option only builds one network. Example: true tags optional Array of Strings A list of strings that help classify and retrieve the deepnet.
The theory is that these engineered features will linearize obvious non-linear dependencies before training begins, and so make learning proceed more quickly. Example: "000005" Depending on the descent algorithm chosen and the topology of the network, certain other parameters may apply. You can also use curl to customize a new deepnet from the command line. For example, to create a new deepnet named "my deepnet" using descent algorithm "adam".
Once a deepnet has been successfully created it will have the following properties. Creating a deepnet is a process that can take just a few seconds or a few days depending on the size of the dataset used as input and on the workload of BigML's systems.
The deepnet goes through a number of states until its fully completed. Through the status field in the deepnet you can determine when deepnet has been fully processed and ready to be used to create predictions. Once you delete a deepnet, it is permanently deleted. If you try to delete a deepnet a second time, or a deepnet that does not exist, you will receive a "404 not found" response.We had to drive a little further, but it was well worth it.
The last night was right on the busiest street in Reykjavic. Very noisy with everyone on the street, but my 22 yo daughter was out there, too, so we could not complain. We wanted to travel around the Ring Road in Iceland for two weeks on our own (parents and one teen) - hate tour buses.
But trying to book all the accommodation felt daunting, especially since we knew very little about the country, and where it would be nice to stay. Instead of overwhelming myself sifting through guide books and websites, I found Nordic Visitor. I must have sent them at least a dozen e mails with all of my questions, big and small, and they always responded promptly and kindly (even though I suspect I was getting to be a pain in the butt.
When we arrived, a driver brought us to our first hotel and gave us our package, which included all of our vouchers for guest houses and activities, an excellent road map, and even an Icelandic cell phone, which came in handy frequently throughout the trip for booking restaurants and activities. I can't recommend them highly enough. Very good organization, All Hotel perfect and the combination of transport cruise train and flight was very good for us We enjoy all cities and all travels One of our best familiar experiences.
You people at Nordic visitor are doing a very good job. I had a lot of expectations about travelling around Iceland and you helped us to make the experience easy and smooth.
Iceland is a wonderful country and we had a fantastic time. Hilmar was attentive and helpfulI emailed him and even called him using the toll free telephone number several times before going to work in the morning from New York, and he answered all my questions patiently.
The guesthouse experiences were great as were the tours. It was nice to meet some locals and learn more of the Icelandic culture through them. Everyone was very helpful and kind.
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We were pleasantly surprised with the quality of the guest houses. The suggestions for the route was excellent and Erla Sonja was very flexible in allowing us to make changes to our itinerary. The trip was well above our expectation and we've already recommended Nordic Visitor to 3 of our friends. We travel all over the world and this was by far the best self-drive vacation we've ever had. We felt like a good friend was taking care of us.
Our experience with Nordic Visitor was great. From the very first initial enquiry via your website, right through to the time we got out of the taxi at the airport to fly on to the next part of our trip, everything went smoothly and exactly to plan. Hilmar was a delight to work with and it was really nice to have the same person from my first email all the way through to final documents and beyond.
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Our lodgings often exceeded our expectations (we pre-selected a moderately-priced package) and the day tours recommended to us were all top-rate.Read The Full Premium Article Subscribe to receive exclusive PREMIUM content Here algorithmic predictions housing crisis market bubbles stock market bubble stock market crashAlgorithmic Articles Premium Top 3 Stock Valuation Methods Plus the Algorithmic Valuation Tool November 5, 2017 Tali Soroker is a Financial Analyst at I Know First.
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Article - McKinsey Quarterly - October 2016 Chinese consumers: Revisiting our predictions By Yuval Atsmon and Max Magni Chinese consumers: Revisiting our predictions Article Actions Share this article on LinkedIn Share this article on Twitter Share this article on Facebook Email this article Download this article As their incomes rise, Chinese consumers are trading up and going beyond necessities. In 2011, we tried our hand at predicting the ways in which, in the decade to come, Chinese consumers would change their preferences and behaviors.
This article takes stock of those predictions. Why check in now. Another is a comprehensive new McKinsey survey, which follows nearly ten years of previous research that includes interviews with more than 60,000 people in upward of 60 cities in China. Deeper and more nuanced understanding of Chinese consumers can help reveal fresh opportunitiesfor new entrants and incumbents alikeand signal those areas where established players may need to be more wary.
While geographic differences persist, Chinese consumers are, on the whole, more individualistic, more willing to pay for nonnecessities and discretionary items, more brand loyal, and more willing to trade up to more expensive purchaseseven as their hallmark pragmatism endures.
Just as it was then, generalizing about Chinese consumers continues to be almost as difficult (and maybe as foolish) as it is to generalize about European consumers.
We predicted these differences would remainand even grow more significant, especially in the consumption patterns and tastes that relate to discretionary items. To help companies better tailor their go-to-market approach, we grouped most cities in China into clusters based on their similarities, including their geographic proximity and the transportation infrastructure that connects them.
Furthermore, when our latest survey compared the consumers in the Shanghai area to those around Beijing and Hangzhou, certain spending attitudes also showed marked differences. For example, brand loyalty increased much faster in Shanghai (24 percent increase in three years versus just 7 percent in Beijing and 9 percent in Hangzhou), as did the willingness to pay for better or healthier products.
Despite geographic differences, there are broad similarities among Chinese consumers. These mirror the general trends economists have found among consumers around the world as economies develop. The general tendency is for consumers, as they earn more, to spend a lower percentage of their income on food, a little more on healthcare, and even more on travel and transportation, as well as on recreational activities.
It was no great stretch then, in our report five years ago, to predict a significant shift in consumption from necessities and seminecessities into discretionary categories. Sure enough, our new survey shows Chinese consumers following the anticipated pattern. When we asked how they plan to increase spending as their income increases, dramatically fewer consumers said they will increase it on food (46 percent in the latest survey, compared to the 76 percent who said they would do so three years earlier).
Responses trended slightly up for healthcare products (from 16 percent to 17 percent), and increased for travel (from 14 percent to 23 percent) and leisure (from 17 percent to 25 percent).
In our previous predictions, we also argued that as the income of Chinese consumers grew, they would aspire to improve their quality of life by not only spending more on discretionary items, but also by shifting their spending to more expensive items in the same categories. In necessity categories such as food, for example, we predicted consumers would be willing to spend more for healthier versions of the same productsfor instance, that olive oil would grow much faster than less healthy (and less expensive) oils.
In seminecessity categories like apparel, we predicted people would buy more special-occasion and premium brands. We anticipated that the strongest beneficiaries of these changes would be in the more discretionary and aspirational categories, such as skincare and automotive.
So what has happened so far. Premium categories have really accelerated. Comparing cosmetics purchases between 2011 and 2015, 44 percent of consumers have traded up their purchases, compared with 4 percent who traded down. Even for rice, 25 percent of consumers traded up versus 3 percent who traded down.
Automotive was not included in our survey, but sales data from the Traffic Management Bureau of the Ministry of Public Security in China suggest significant trading up. In 2011, 51 percent of the renminbi spent on cars by Chinese consumers were for autos cheaper than 100,000 RMB.
These sales accounted for only 43 percent of the market. Cars selling for 100,000 to 250,000 RMB grew twice as fast with a compound annual growth rate (CAGR) of 19 percent versus 9 percent. And cars with price tags between 250,000 and 400,000 RMB grew the fastest of all, with 23 percent CAGR.
In 2011, we observed a big generational difference between consumers in their late 50s and early 60s, who were very conservative spenders, and all of the age cohorts younger than them.
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