Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting? Classifying emails as spam or not spam. Watching you label emails as spam or not spam. O The number (or fraction) of emails correctly classified as spam/not spam. None of the abov solutionspile.com

Question

Suppose your email program watches which emails you do or do
not mark as spam, and based on that learns how to better filter

Of the following examples, which would you address using an
unsupervised learning algorithm? (Check all that apply.)
Given em Youre running a company, and you want to develop learning algorithms to address each
of two problems.
Problem 1: You have a Q7) How many Weights, Biases and
Parameters do we need for constructing the
following Neural Network Model Structure:
(Note:I

Q8) How many Weights, Biases and Parameters
do we need for constructing the following
Neural Network Model Structure :
sizes:

Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting? Classifying emails as spam or not spam. Watching you label emails as spam or not spam. O The number (or fraction) of emails correctly classified as spam/not spam. None of the above-this is not a machine learning problem. Of the following examples, which would you address using an unsupervised learning algorithm? (Check all that apply.) Given email labeled as spam/not spam, learn a spam filter. Given a set of news articles found on the web, group them into set of articles about the same story. Given a database of customer data, automatically discover market segments and group customers into different market segments. Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not. You're running a company, and you want to develop learning algorithms to address each of two problems. Problem 1: You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months. Problem 2: You'd like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised. Should you treat these as classification or as regression problems? O Treat both as classification problems. O Treat problem 1 as a classification problem, problem 2 as a regression problem. Treat problem 1 as a regression problem, problem 2 as a classification problem. O Treat both as regression problems. Q7) How many Weights, Biases and Parameters do we need for constructing the following Neural Network Model Structure: (Note:Input Layer:10, Hidden Layers 16,16,16,Output Layer 2) OOOOOOOOOO 0000000000000000 OOO0 Q8) How many Weights, Biases and Parameters do we need for constructing the following Neural Network Model Structure : sizes: 10, 8, 8, 8, 2 2 ›0000000 00000000 DO000000

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