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Would this SQL tuning technique work?


By : Kenny Preston
Date : October 17 2020, 11:12 AM
I wish this help you First of all, as the commenter points out on the post you link to, there is a bit of FUD going on here -- to summarize for those that don't want to click the link: "Things can go bad... Buy my book!"
Second what you list in your question is not hard coding, it is using hard coding to figure out the best ways to work with a DB. This seems ok to me. As long as you don't leave the hard coded hints in there, things should be ok, SQL server can still change the optimization as data changes.
code :


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CSS Unordered List: Why Does One Technique Work But Another Doesn't Work?


By : user2123661
Date : March 29 2020, 07:55 AM
To fix this issue The space character pretty much means "now let's look at the children".
So when you have .help ul you are saying "grab all things with a class of help" then "grab all ul children within those things".

SQL Server Tuning: Database Engine Tuning Advisor


By : Роман Вевдюк
Date : March 29 2020, 07:55 AM
I wish this help you DTA is very good when it has sufficient work load to operate on. However, for 1 or 2 Odd queries, DTA can't suggest you very good solutions. I suggest, Review the indexes suggested and check if they need to be created. Do not create all indexes suggested as it may have adverse effect on the overall system. I believe if your select queries are very slow, then it should be duw to missing index on the columns in your where clause. Choose the best option from DTA, review it and then create your indexes and stats as required.

How does Fine-tuning Word Embeddings work?


By : naouf3l
Date : March 29 2020, 07:55 AM
wish of those help Yes, if you feed the embedding vector as your input, you can't fine-tune the embeddings (at least easily). However, all the frameworks provide some sort of an EmbeddingLayer that takes as input an integer that is the class ordinal of the word/character/other input token, and performs a embedding lookup. Such an embedding layer is very similar to a fully connected layer that is fed a one-hot encoded class, but is way more efficient, as it only needs to fetch/change one row from the matrix on both front and back passes. More importantly, it allows the weights of the embedding to be learned.
So the classic way would be to feed the actual classes to the network instead of embeddings, and prepend the entire network with a embedding layer, that is initialized with word2vec / glove, and which continues learning the weights. It might also be reasonable to freeze them for several iterations at the beginning until the rest of the network starts doing something reasonable with them before you start fine tuning them.

How to get best params after tuning by pyspark.ml.tuning.TrainValidationSplit?


By : Adam JF
Date : March 29 2020, 07:55 AM
should help you out You can access best model using bestModel property of the TrainValidationSplitModel:
code :
best_model = model.bestModel
best_model.rank
10
(best_model
    ._java_obj     # Get Java object
    .parent()      # Get parent (ALS estimator)
    .getMaxIter()) # Get maxIter
10

Tuning XGboost parameters Using Caret - Error: The tuning parameter grid should have columns


By : PRMagento
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I know it's 'tad' bit late but, check your spelling of gamma in the grid of tuning parameters. You misspelled it as gammma (with triple m's).
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