"This happened in my previous job in [company X]. I was the Single threaded Owner for a new business growth initiative from this company to launch a new eCommerce supply chain solution that X was planning to experiment.
Being the single threaded owner, I was accountable for researching the customer problems and coming up with business goals and requirements, including identifying the engineering headcount needed to solve the customer pain point and eventually execute it across 11 different produ"
VictorSage - "This happened in my previous job in [company X]. I was the Single threaded Owner for a new business growth initiative from this company to launch a new eCommerce supply chain solution that X was planning to experiment.
Being the single threaded owner, I was accountable for researching the customer problems and coming up with business goals and requirements, including identifying the engineering headcount needed to solve the customer pain point and eventually execute it across 11 different produ"See full answer
"Front Page Layout Design for Newspaper App
Header Section
Logo: Displays at the top left.
App Name: Displays alongside, so very prominent.
Search Bar: Centered with search to find articles within the application.
Navigation Menu: The links to the respective sections, World, Politics, Sports, Entertainment, and Opinion, etc
Main Content Area
Top Stories Carousel:
It is a rotating banner that displays the top 3-5 news stories with images along with their headlines.
Each story should be cl"
Midde V. - "Front Page Layout Design for Newspaper App
Header Section
Logo: Displays at the top left.
App Name: Displays alongside, so very prominent.
Search Bar: Centered with search to find articles within the application.
Navigation Menu: The links to the respective sections, World, Politics, Sports, Entertainment, and Opinion, etc
Main Content Area
Top Stories Carousel:
It is a rotating banner that displays the top 3-5 news stories with images along with their headlines.
Each story should be cl"See full answer
"
This is mostly correct and fairly fast.
My code has a bug somewhere where it fails on cases like the last case, where there are negative number on both ends of the array and the sums .
from collections import deque
debug = True # False
def prdbg(*x):
global debug
debug = True # False
if debug:
print(x)
else:
return
def max_sum(arr, start, end):
if type(arr) == type('''
"
Nathan B. - "
This is mostly correct and fairly fast.
My code has a bug somewhere where it fails on cases like the last case, where there are negative number on both ends of the array and the sums .
from collections import deque
debug = True # False
def prdbg(*x):
global debug
debug = True # False
if debug:
print(x)
else:
return
def max_sum(arr, start, end):
if type(arr) == type('''
"See full answer
Data Structures & Algorithms
Coding
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"This is due to sticky sessions.
The load balancer is not correctly configured with sticky session option.
It is likely the servers were storing session data on the server themselves (in-memory), and thus when user makes a request, the load balancer routes this to a different server than the one they started with, that second server may not recognise the user's session. This could prompt the user to log in again.
One way to resolve this, is to use a centralised session storage, something like"
T I. - "This is due to sticky sessions.
The load balancer is not correctly configured with sticky session option.
It is likely the servers were storing session data on the server themselves (in-memory), and thus when user makes a request, the load balancer routes this to a different server than the one they started with, that second server may not recognise the user's session. This could prompt the user to log in again.
One way to resolve this, is to use a centralised session storage, something like"See full answer
"Given the dataset does not contain many labels, it implies we cannot directly use supervised learning.
I would ask more about the type of dataset we are given. Is it images, text, etc? This may inform the types of transformations we do the dataset.
I can see two approaches to training
Given the labels we do have, we can find a method to generate labels for the other unlabeled data. This likely will introduce some error since they may not be true labels, but it at least allows processing the"
Matt M. - "Given the dataset does not contain many labels, it implies we cannot directly use supervised learning.
I would ask more about the type of dataset we are given. Is it images, text, etc? This may inform the types of transformations we do the dataset.
I can see two approaches to training
Given the labels we do have, we can find a method to generate labels for the other unlabeled data. This likely will introduce some error since they may not be true labels, but it at least allows processing the"See full answer
"Effective loss functions for computer vision models vary depending on the specific task, some commonly used loss functions for different tasks:
Classification
Cross-Entropy Loss:Used for multi-class classification tasks.
Measures the difference between the predicted probability distribution and the true distribution.
Binary Cross-Entropy Loss:Used for binary classification tasks.
Evaluates the performance of a model by comparing predicted probabilities to the true binary labe"
Shibin P. - "Effective loss functions for computer vision models vary depending on the specific task, some commonly used loss functions for different tasks:
Classification
Cross-Entropy Loss:Used for multi-class classification tasks.
Measures the difference between the predicted probability distribution and the true distribution.
Binary Cross-Entropy Loss:Used for binary classification tasks.
Evaluates the performance of a model by comparing predicted probabilities to the true binary labe"See full answer
"This is an Improve a Product question. Let's first go over the Improve a Product formula:
Ask clarifying questions
Identify users, behaviors, and pain points
State product goal
Brainstorm small improvements
Brainstorm bolder improvements
Measure success
Summarize
Now, let's begin!
Ask clarifying questions
Before we begin listing off recommendations, it's important you ask questions to ensure you and the interviewer are on the same page"
Exponent - "This is an Improve a Product question. Let's first go over the Improve a Product formula:
Ask clarifying questions
Identify users, behaviors, and pain points
State product goal
Brainstorm small improvements
Brainstorm bolder improvements
Measure success
Summarize
Now, let's begin!
Ask clarifying questions
Before we begin listing off recommendations, it's important you ask questions to ensure you and the interviewer are on the same page"See full answer