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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 an embedding column
B. Create a one-hot column
C. Create a hash bucket
D. Create a multi-hot column
Answer: A
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-deep-model.html
NEW QUESTION: 2
A technician needs to install a server to authenticate remote users before they have access to corporate network resources when working from home.
Which of the following servers should the technician implement?
A. RAS
B. DNSSEC
C. NAT
D. PPP
E. VLAN
Answer: A
NEW QUESTION: 3
What is the optimal encryption setting to improve user experience when configuring PCoIP?
A. SALSA20-256
B. SSL/TSL
C. AES-128
D. PGP encryption
Answer: A
Explanation:
Maybe AES-128 is not the same the AES-128-GCM
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