Consider the following DOM representation. Element: author Text: Amy Select one: O a. Attribute: lang: EN O b. Text: Today's lecture cancelled! OC. Element: body Root: announcement d. Text: Important notice Element: date Text: 2021-01-10 Attribute: lang: EN Element: title Text: Important notice Element: body In the DOM representation of the previous question, imagine beginning at the element "Root: announcement" and making the following sequence of traversals: 1. first Child 2. nextSibling 3. nextSibling 4.parentNode 5. lastChild At which element does the traversal end? Text: Today's lecture cancelled!

You will be using on RV related review comments to train a Sentence completion model. You could use the code below. The code is written in Jupyter notebook python.  please follow the following instructions: 1. Build a working sentence completion model. Training data is provided in train.csv. This model should take partial sentences (e.g., "hey how ar," "hello, please") and provide completion for that sentence. Not every input needs to be completed into a correct sentence (e.g., "asdfauaef" does not need to have a sentence completion). For this model's purposes, a sentence is considered completed if an "end of sentence" character (like a full stop, question mark, etc.) occurs within the next five words OR. We predict up to 5 words from the input. 2. BONUS: If you use word vectors or other techniques from what we shared in class using character encoding and have a working model. 3. The test data (test.csv) will be used to evaluate the model. The test file contains partial sentences. Use…

): Draw use case diagram for an online library system. (Eg. Saudi Digital Library SDL . **e-3: Draw Class Diagram for Toyota Company. Company deals with two types of ithat are Cars and Jeeps. Vehicles have Spare Parts (0 Breaks and Lights. Technicians (fix the spare parts whereas Engineer Inspect () Vehicles (; Alike Engine, the Vehicles. Fua-- a. Cauman Dis ADD.

Define the Support Vector Classifier (a.k.a. Soft Margin Classifier) when used to classify observations.

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