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NEW QUESTION: 1
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create a multi-hot column
B. Create an embedding column
C. Create a one-hot column
D. Create a hash bucket
Answer: B
Explanation:
Explanation
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference:
https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-dee

NEW QUESTION: 2

A. Option E
B. Option F
C. Option C
D. Option D
E. Option A
F. Option B
Answer: B,C

NEW QUESTION: 3
Refer to the exhibit. If a Layer 3 switch running OSPF in a VRF-lite configuration reports this error, which action can you take to correct the problem?

A. Configure the control plane with a larger memory allocation to support the Cisco Express Forwarding Information Base.
B. Set mls cef maximum-routes in the global configuration.
C. Upgrade the Layer 3 switch to a model that can support more routes.
D. Add the vrf-lite capability to the OSPF configuration.
Answer: B

NEW QUESTION: 4
Are the main features of big data understood and described correctly? "Multiple choices"
A. has a large amount of storage and a large amount of calculation
B. many sources, many formats
C. grows fast and processing speed is fast
D. Low value density of data
Answer: A,B,C,D