[gim]

Deck 7 has indoor/outdoor pools aft, the highlight being the vitality pool along the stern.

[matamotok]

The wheelhouse is located forward on Deck 7, followed by the guest accommodation. Aft of amidships is a yoga & Pilates fitness room, plus gym facilities that have a comprehensive selection of machines and other equipment. The Yacht Club is aft, leading outside to further facilities. here

[zooko]

The 168M CRUISE EXPLORER YACHT measures 551feet and was built by Uljanik Shipyard in 2018. She offers luxurious accommodation for up to 200 charter guests on board in 114 cabins and has a professional and well-trained crew of 176 that ensures everyone has a great time on board.

[MartinaIgnatius]

However, you should expect to pay from £ 4,000 to £10,000 depending on the size of the project and the quality of materials. Renovating a bathroom in stages is much cheaper than remodelling everything at once. Common bathroom updates include fixing the damage, repainting, resurfacing, or replacing fixtures.

[TylerJordan]

The average bathroom remodels costs around £ 5,000, with most homeowners spending £ 4,000 to £ 7,000. A small bathroom remodel costs £ 2,500 to £ 4,000 and a master bathroom can cost from £ 7,000 to £ 10, 000. Bathroom renovation cost per square metres comes around to £65.

[destine]

Bathroom renovations are typically fixing or replacing your current facilities, whereas renovation involves adding or replacing old fixtures such as showers, bathtubs, tiling, and possibly designing a new layout.

[JerGermanesls]

ELECTRICS AND ELECTRONICS here

[atomic_age]

There are day to day living expenses to consider over such a long period of time. We lived on land for the majority of the project, storing the bulk of our possessions in a container. In Satun, a one bedroom bungalow in 2014 was around £80 per month. Two scooters were around £30 per month. Food, usually cooked on the side of the road, is good and inexpensive, around £1.20 per meal. [links]

[parallel]

DECK FITTINGS REMOVED AND REPLACED more

[Mackendra99]

With option 3 immediately removed, option 1 did not make sense either (whichever way we added up the sums). Esper, in her state, would not achieve anything like the price we needed for another boat of equal performance. Inevitably we would have to spend a lot to get a second-hand boat to the spec we require for world cruising, and would it ever be as good as our Oyster 435?

[Argentum]

You can really save on living costs in Thailand. [links]

[billy3321]

If you use cross-validation to estimate the hyperparameters of a model (the $\alpha$s) and then use those hyper-parameters to fit a model to the whole dataset, then that is fine, provided that you recognise that the cross-validation estimate of performance is likely to be (possibly substantially) optimistically biased. This is because part of the model (the hyper-parameters) have been selected to minimise the cross-validation performance, so if the cross-validation statistic has a non-zero variance (and it will) there is the possibility of over-fitting the model selection criterion. here

[derka]

This is of course computationally expensive, but worth it as the bias introduced by improper performance estimation can be large. See my paper [links]

[artdegree12]

Now, say that after model selection I would like to use all the the data that I have available in an attempt to ship the best possible model in production. For this, I could use the parameters $\alpha^*$ that I chose via grid search with cross-validation, and then, after training the model on the full ( $F$ ) dataset, I would a get a single new learned model $\beta^_<\alpha^*>$ more

[schlow22]

TL:DR: Is it ever a good idea to train an ML model on all the data available before shipping it to production? Put another way, is it ever ok to train on all data available and not check if the model overfits, or get a final read of the expected performance of the model?

[bober182]

G. C. Cawley and N. L. C. Talbot, Over-fitting in model selection and subsequent selection bias in performance evaluation, Journal of Machine Learning Research, 2010. Research, vol. 11, pp. 2079-2107, July 2010. (www, pdf) [links]