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UDK Davulis, P. Koverovas, J. Vadovas prof. Darbo kodekso komentaro antro tomo autoriai:. Tomas Davulis DK str. Paulius Koverovas DK str. Pirmasis skirsnis. Darbo sutarties turinys ir jos sudarymas str. Ketvirtasis skirsnis Darbo sutarties pasibaigimas str. P OILS. Respublikos darbo kodekso patvirtinimo. Respublikos Konstitucija. LR KT. Respublikos Konstitucinis Teismas. Journal ofthe European Union. DK 99 str. DK88 str. DK 2 str. Individualus darbo santykiai. DK 92 str. Al 2. DK13 str. Komentuojamo straipsnio 2 dalyje nurodoma, kad bedarbiams 3.

DK 91 str. DK str. DK 15 str. DK 95 str. DK 98 str. Susumavus komentuojamo straipsnio nuostatas, reglamentuo. Darbo sutartis. Jei darbo sutarties. Komentuojamo straipsnio 1 dalyje darbo sutarties turinys api- i. JlHBiinm, Kn.Prisegti dokumentus:. Jevgenij Goncarov IP 2g44crcv. Ok - taisykles yra taisykles. Gerbiamas kliente, apgailestaujame, kad teko atsidurti tokioje situacijoje. Pagarbiai, Lina. Gerbiama Lina, labai apgailestaujame, kad teko atsidurti tokioje situacijoje. Diana Astilovska Sveiki, labai turiu dideliu nusiskundimu apie Kalvelio km.

Aciu uz isklausyma. Laukiame patikslintos informacijos. Diana Astilovska IP 28mid. Sveiki, labai turiu dideliu nusiskundimu apie Kalvelio km. Raimund IP 2i24t8vl.

Jokiu ten acakymu neduoda. Aciu jums. Siuntinis nusiustas atgal. Dekoju jum. Puiki kompanija ir nera kitu zodziu :. Raimund Nesamone darosi sitoj kompanijei. Dangora IP 2df6apak. Klientas Standartinis atsakimas. Nieko naujo. Matau kad LP absoliuciai nusispajauti i zmones. Jeigu jus tokie "rupestingi", tai padarikite kad butu normalus sisisekimas su OZO centru, bet matau kad jusu atsakime nei zodzio apie tai.

Jums nerupi kad zmones turi gaisti laika laukdami autobusu, kurie vazioja karta per valanda. Specialiai patikrinau, kiek reikia laiko vaziavimui su autobusu ten ir atgal - maziausiai 2 val. Tai ka jus galvojate? Jums tai nerupi - svarbiausai pinigai. Turint noro, galejote ir Didlaukio gatveje surasti patalpu.

Netgi zinau kur tos patalpos galima rasti.When I told her about my walking condition she booked hotels close to stations or the ship we needed to do. She made our trip smooth and enjoyable. It was first time using a self directed trip, and we were a little worried about the connections, but in fact it was very easy and smooth. Everything was wonderful, you more than met our expectations. The quality of the rooms and hotels was excellent, the locations were perfect.

Sofia was an outstanding person to work with. She managed everything, assisted us in getting a visa issued and when there was one small glitch, promptly arranged for a refund for that portion of the tour. If I book another tour, I will not hesitate to request her again. I couldn't be happier. Oh yeah, Scandinavia is pretty cool too. We both felt the whole package was really well organised and structured. The road map was well marked with things of interest and the Iceland Road Guide was an excellent source book.

Hotels were good quality and staff were helpful and really welcoming. Meals in hotels were good. Meals in restaurants were invariably good and (particularly Nordic Visitor's recommendation in Stokkseyri) often excellent I'm convinced that Nordic Visitor sells a high quality and well organised product.

We enjoyed our Iceland experience hugely and I'm certain our family will return. This was a great experience of a beautiful country. Iceland was a wonderful experience -- the scenery, the culture, the history, the colorful houses, the sheep and horses in their wide-open spaces, the fascinating geology, and even the language.

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We are thankful to all of you who helped us to enjoy this experience and to bring home the memories. The itinerary, highlighted map, mobile phone, and GPS with the rental car were all very helpful. The hotels were fantastic and food (though expensive. We especially enjoyed some of the suggestions Solveig noted on the map along the way.

We had a wonderful week full of beautiful, other-worldly sights. All of our travel plans went smoothly - from the airport transfer to our first hotel, getting our rental car, hotel reservations, dinner suggestions, and back to the airport. My family had such a fantastic, memorable week. Overall we thoroughly enjoyed our brief exploration of a beautiful country, Iceland We found everyone we met to be courteous and helpful and felt that Icelanders had great pride in their country.

Our visit was well organised by Nordic Visitor and the thorough pre trip information, maps and guidebook were of great assistance. All our pre trip enquiries were dealt with quickly and efficiently. I travel around the world and have been in every continent and used many travel agencies.

Nordic visitor ranks as one of the best in organization and trip support that I have experienced and I would highly recommend them for any tour planning.If false, these predictors are not created, and rows containing missing numeric values are dropped.

Example: false name optional String,default is dataset's name The name you want to give to the new logistic regression. Example: "my new logistic regression" normalize optional Boolean,default is false Whether to normalize feature vectors in training and predicting. The type of the field must be categorical. The type of the fields must be categorical. The range of successive instances to build the logistic regression.

Regularizing with respect to the l1 norm causes more coefficients to be zero, using the l2 norm forces the magnitudes of all coefficients towards zero.

Example: "l1" replacement optional Boolean,default is false Whether sampling should be performed with or without replacement. The minimum between that number and the total number of input rows will be used. Example: 1000 tags optional Array of Strings A list of strings that help classify and index your logistic regression. By default, they are "one-hot" coded. That is, one numeric variable is created per categorical value, plus one for missing values.

For a given instance, the variable corresponding to the instance's categorical value has its value set to 1, while the other variables are set to 0. Using the iris dataset as an example, we can express this coding scheme as the following table:The parameter value is an array where each element is a map describing the coding scheme to apply to a particular field, and containing the following keys:The value for coding determines which of the following methods is used to code the field: If multiple coding schemes are listed for a single field, then the coding closest to the end of the list is used.

Codings given for non-categorical variables are ignored. The dummy class will be the first by alphabetical order. This is because the default one-hot encoding produces collinearity effects which result in an ill-formed covariance matrix.

You can also use curl to customize a new logistic regression. Once a logistic regression has been successfully created it will have the following properties. The coefficients output field is an array of pairs, one pair per class. The first element in the pair is a class value, and the second element is a nested array of coefficients for the logistic model that gives the probability of that class. Each inner array within the nested array contains the group of coefficients that pertain to a single input field.

The class-coefficient pairs are listed in the same order as the class values in the objective field summary. If the model was trained with missing values in the objective field, then a vector of coefficients will also be created for the missing class value, labeled with "", and listed last. These are found under a field named stats. It is possible for null to appear among the values contained in stats. Wald test statistics cannot be computed for zero-value coefficients, and so their corresponding entries are null.

Moreover, if the coefficients' information matrix is ill-conditioned, e. Creating a logistic regression 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 logistic regression goes through a number of states until its fully completed. Through the status field in the logistic regression you can determine when the logistic regression has been fully processed and ready to be used to create predictions.

Thus when retrieving a logisticregression, it's possible to specify that only a subset of fields be retrieved, by using any combination of the following parameters in the query string (unrecognized parameters are ignored): Fields Filter Parameters Parameter TypeDescription fields optional Comma-separated list A comma-separated list of field IDs to retrieve.This section describes the parameters of both types.

These parameters are defined in the Version object or added for convenience in the gcloud ml-engine versions create command. You can specify the number of training nodes to keep running for your model version. See the section on scaling for more information.

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If you are using the gcloud command-line tool to deploy your model, you can use a SavedModel on your local computer. The tool stages it in the Cloud Storage location you specify before deploying it to Cloud ML Engine.

You may have included TensorFlow Ops in your computation graph that were useful primarily in the context of training. Once you've trained your model, you can remove those ops from your graph before exporting your final version. Much of the advice given in the training application development page is aimed at the prediction experience.

In some cases those are changes that you make to your model when the bulk of your training is done and you're ready to start deploying versions. You can send new data to your deployed model versions to get predictions. The following sections describe important prediction considerations.

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Cloud ML Engine provides two ways to get predictions from trained models: online prediction (sometimes called HTTP prediction), and batch prediction. In both cases, you pass input data to a cloud-hosted machine-learning model and get inferences for each data instance.

The differences are shown in the following table:The needs of your application dictate the type of prediction you should use. You should generally use online prediction when you are making requests in response to application input or in other situations where timely inference is needed. Batch prediction is ideal for processing accumulated data when you don't need immediate results. For example a periodic job that gets predictions for all data collected since the last job.

You should also inform your decision with the potential differences in prediction costs. If you use a simple model and a small set of input instances, you'll find that there is a considerable difference between how long it takes to finish identical prediction requests using online versus batch prediction. It might take a batch job several minutes to complete predictions that are returned almost instantly by an online request.

This is a side-effect of the different infrastructure used by the two methods of prediction. Cloud ML Engine allocates and initializes resources for a batch prediction job when you send the request. Online prediction is typically ready to process at the time of request. Cloud ML Engine measures the amount of processing you consume for prediction in node hours.

This section describes these nodes and how they are allocated for the different types of prediction. It's easiest to think of a node as a virtual machine (VM), even though they are implemented with a different mechanism than a traditional VM. Each node is provisioned with a set amount of processing power and memory. It also has an operating system image and a set configuration of software needed to run your model to get predictions.

Both online and batch prediction run your node with distributed processing, so a given request or job can use multiple nodes simultaneously. You are charged for total node usage by the minute, using an hourly rate. For example, running two nodes for ten minutes is charged the same as running one node for twenty minutes. Online and batch prediction allocate nodes differently, which can have a substantial effect on what you will be charged. The batch prediction service scales the number of nodes it uses to minimize the amount of elapsed time your job takes.

To do that, the service:Scales the number of nodes during the job in an attempt to optimize efficiency. Each node takes time to get started, so the service tries to allocate just enough of them so that the startup time is countered by the reduction in elapsed time.

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