"Clarifying Q
Is the search traffic meaning # visits clicked through the 'search' button from the homepage(search CTR)? or does this include # pages viewed after the search result pages (search depth)?
Hypothesis
I believe introducing Gen AI as a default feature on the Google's search box will increase #search traffic. I assume the biggest user pain point within current google homepage is that deciding on a keyword for search. Unlike other alternative platforms such as"
Cj K. - "Clarifying Q
Is the search traffic meaning # visits clicked through the 'search' button from the homepage(search CTR)? or does this include # pages viewed after the search result pages (search depth)?
Hypothesis
I believe introducing Gen AI as a default feature on the Google's search box will increase #search traffic. I assume the biggest user pain point within current google homepage is that deciding on a keyword for search. Unlike other alternative platforms such as"See full answer
"i do feel like the question itself is kind of confusing. Youtube does have a product called YouTube’s Analytics, which is a channel analytics tool for creator lol"
Anonymous Ferret - "i do feel like the question itself is kind of confusing. Youtube does have a product called YouTube’s Analytics, which is a channel analytics tool for creator lol"See full answer
"How much does the Empire State Building weigh?
I will estimate the weight of the Empire State Building using a structured approach. I’ll start with clarifying questions, define an equation, make reasonable assumptions, perform calculations, and address trade-offs and potential errors.
Clarifying Questions
To refine the scope, I considered:
How many floors does the Empire State Building have?
What is the area per floor?
What materials are used (e.g., steel, concrete)"
Nishtha - "How much does the Empire State Building weigh?
I will estimate the weight of the Empire State Building using a structured approach. I’ll start with clarifying questions, define an equation, make reasonable assumptions, perform calculations, and address trade-offs and potential errors.
Clarifying Questions
To refine the scope, I considered:
How many floors does the Empire State Building have?
What is the area per floor?
What materials are used (e.g., steel, concrete)"See full answer
"Gradient Descent is an optimisation strategy used in several supervised learning models. It is the technique for finding the optimum solution of an objective function. Typically, for a linear regression use case, it is used to find the weights and bias that produce the lowest loss.
It involves computing the partial derivative of the objective function with respect to the weight and bias vectors. To find the optima of the function, the derivative is equated to 0, and iteratively the weight and b"
Megha V. - "Gradient Descent is an optimisation strategy used in several supervised learning models. It is the technique for finding the optimum solution of an objective function. Typically, for a linear regression use case, it is used to find the weights and bias that produce the lowest loss.
It involves computing the partial derivative of the objective function with respect to the weight and bias vectors. To find the optima of the function, the derivative is equated to 0, and iteratively the weight and b"See full answer
Machine Learning Engineer
Concept
+1 more
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"Because testing many engagement metrics at once increases the risk of finding effects that aren't real (the 'multiple comparisons problem'), you must adjust your criteria for statistical significance. For social media data, the Benjamini-Hochberg procedure is often a practical choice as it controls the rate of false discoveries (FDR) while still allowing you to detect genuine changes; however, the ideal adjustment method will vary depending on your specific number of metrics (e.g., use Bonferron"
Lucas G. - "Because testing many engagement metrics at once increases the risk of finding effects that aren't real (the 'multiple comparisons problem'), you must adjust your criteria for statistical significance. For social media data, the Benjamini-Hochberg procedure is often a practical choice as it controls the rate of false discoveries (FDR) while still allowing you to detect genuine changes; however, the ideal adjustment method will vary depending on your specific number of metrics (e.g., use Bonferron"See full answer
"To speed up A/B tests results with limited sample sizes, we can apply advanced techniques like CUPED to reduce variance for faster statistical significance, interleaving to gather more comparative data per user (e.g., ranking), MAB to dynamically allocate traffic to winning variations for quicker optimization (e.g., campaigns), and Bayesian A/B testing which offers probabilistic conclusions that can be reached earlier. Each method, when appropriately applied, allows you to gain m"
Lucas G. - "To speed up A/B tests results with limited sample sizes, we can apply advanced techniques like CUPED to reduce variance for faster statistical significance, interleaving to gather more comparative data per user (e.g., ranking), MAB to dynamically allocate traffic to winning variations for quicker optimization (e.g., campaigns), and Bayesian A/B testing which offers probabilistic conclusions that can be reached earlier. Each method, when appropriately applied, allows you to gain m"See full answer
"Define: How is daily post view calculated
Isolate Issues:
Data issue
Time period
Geo
IOS vs Android vs Web
Correlated Metrics in the funnel
DAU
Time spent/ scrolls
Engagement - likes, comments
External factors
Competitor actions
Big events
Internal factors
Product launch
Feature change"
Steve Y. - "Define: How is daily post view calculated
Isolate Issues:
Data issue
Time period
Geo
IOS vs Android vs Web
Correlated Metrics in the funnel
DAU
Time spent/ scrolls
Engagement - likes, comments
External factors
Competitor actions
Big events
Internal factors
Product launch
Feature change"See full answer
"def changeString(org: str,target:str) -> bool:
lOrg = len(org)
lTarget = len(target)
\# They have to be equal in lenght
if lOrg != lTarget:
return False
counter1 = Counter(org)
counter2 = Counter(target)
\# Counter internally iterates through the input sequence, counts the number of times a given object occurs, and stores objects as keys and the counts as values.
if counter1 != counter2:
return False
diff = sum(org[i] != target[i] for i in range(n))
return diff == 2 or (diff == 0 and any(v > 1 f"
Rafał P. - "def changeString(org: str,target:str) -> bool:
lOrg = len(org)
lTarget = len(target)
\# They have to be equal in lenght
if lOrg != lTarget:
return False
counter1 = Counter(org)
counter2 = Counter(target)
\# Counter internally iterates through the input sequence, counts the number of times a given object occurs, and stores objects as keys and the counts as values.
if counter1 != counter2:
return False
diff = sum(org[i] != target[i] for i in range(n))
return diff == 2 or (diff == 0 and any(v > 1 f"See full answer