"It might not be a good idea to predict stock prices only based on reddit comments.
You could create a signal from reddit comments that can indicate "social media interest" and feed it into a ML system (along with other features) that predicts prices. Collecting good data to train the model and evaluating it correctly are going to be huge challenges."
Satyajit G. - "It might not be a good idea to predict stock prices only based on reddit comments.
You could create a signal from reddit comments that can indicate "social media interest" and feed it into a ML system (along with other features) that predicts prices. Collecting good data to train the model and evaluating it correctly are going to be huge challenges."See full answer
"I've participated in several competitions in Kaggle concerning medical images. My most recent competition deals with images of skin lesions and classifying them as either melanoma or not. I focused on fine-tuning pretrained models and ensembling them.
I also like to keep track of the latest trends of computer vision research, with a focus on making models memory-efficient through model compression and interpretability."
Xuelong A. - "I've participated in several competitions in Kaggle concerning medical images. My most recent competition deals with images of skin lesions and classifying them as either melanoma or not. I focused on fine-tuning pretrained models and ensembling them.
I also like to keep track of the latest trends of computer vision research, with a focus on making models memory-efficient through model compression and interpretability."See full answer
"A typical computer vision pipeline consists of several key stages that process and analyze visual data to extract meaningful information. Here’s a general outline of the steps involved:
Image Acquisition:Capturing images or videos using cameras or other imaging devices.
Preprocessing steps such as resizing, cropping, and converting color spaces.
Image Preprocessing:Noise reduction (e.g., using filters like Gaussian blur).
Image normalization to standardize pixel values.
Contrast e"
Shibin P. - "A typical computer vision pipeline consists of several key stages that process and analyze visual data to extract meaningful information. Here’s a general outline of the steps involved:
Image Acquisition:Capturing images or videos using cameras or other imaging devices.
Preprocessing steps such as resizing, cropping, and converting color spaces.
Image Preprocessing:Noise reduction (e.g., using filters like Gaussian blur).
Image normalization to standardize pixel values.
Contrast e"See full answer
Machine Learning Engineer
Concept
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"inheritance means its acquire all properties from parent class to child class.composition means its acquire some properties as our request is called composition"
Niteesh V. - "inheritance means its acquire all properties from parent class to child class.composition means its acquire some properties as our request is called composition"See full answer
"There are two parts to this questions:
Part 1: Why did Meta create Rooms? ( limiting to video collaboration)
Answer: Potential reasons for Meta to create their own Zoom equivalent:
Provide a video collaboration platform to employees.
Due to size and global distribution of its workforce it may make economic sense to make an in-house product that is cheaper than buying an enterprise license of Zoom
Meta already has the technology and infrastructure to do videocalls and meetings because of"
Tony A. - "There are two parts to this questions:
Part 1: Why did Meta create Rooms? ( limiting to video collaboration)
Answer: Potential reasons for Meta to create their own Zoom equivalent:
Provide a video collaboration platform to employees.
Due to size and global distribution of its workforce it may make economic sense to make an in-house product that is cheaper than buying an enterprise license of Zoom
Meta already has the technology and infrastructure to do videocalls and meetings because of"See full answer
"Clarifying question: just want to make sure that I'm designing the right product, are you referring to ATM as in a machine that takes in a debit or credit card and dispenses cash? Also, is there any location restriction for where the machine is located at London Heathrow Airport, eg. departure hall after security or arrival hall before exiting baggage claim? These could potentially impact the use case. (Assume interviewer answers yes for first and no for second)
Approach:
Identify or segment"
Anonymous Sparrow - "Clarifying question: just want to make sure that I'm designing the right product, are you referring to ATM as in a machine that takes in a debit or credit card and dispenses cash? Also, is there any location restriction for where the machine is located at London Heathrow Airport, eg. departure hall after security or arrival hall before exiting baggage claim? These could potentially impact the use case. (Assume interviewer answers yes for first and no for second)
Approach:
Identify or segment"See full answer
"As a direct competitor to my favorite product, our mission is to enhance productivity for creative professionals. Targeting small design agencies, our solution offers a streamlined project management platform tailored to their specific needs. Launch feasibility relies on user feedback, ensuring impact and monetization through subscription-based pricing. Success metrics include user engagement and retention rates."
Devendra B. - "As a direct competitor to my favorite product, our mission is to enhance productivity for creative professionals. Targeting small design agencies, our solution offers a streamlined project management platform tailored to their specific needs. Launch feasibility relies on user feedback, ensuring impact and monetization through subscription-based pricing. Success metrics include user engagement and retention rates."See full answer
"Clarifying question: why would Google consider sunsetting Gmail? Is there an opportunity to make a case against sunsetting or has the executive decision already been made and we're truly looking for a flawless sunset? If decision has already been made - why? Is this a global decision or country-specific?
If the answer from interviewer is that the decision is open to debate, pivot case toward identifying the problem that causes leadership to even consider sunset. Then analyze and propose ways to"
Anonymous Sparrow - "Clarifying question: why would Google consider sunsetting Gmail? Is there an opportunity to make a case against sunsetting or has the executive decision already been made and we're truly looking for a flawless sunset? If decision has already been made - why? Is this a global decision or country-specific?
If the answer from interviewer is that the decision is open to debate, pivot case toward identifying the problem that causes leadership to even consider sunset. Then analyze and propose ways to"See full answer
"I would go look in the mirror and ask that person why the legal team is only flagging issues a week ahead of launch date. Did I not engage our legal team way in advance at the conception stage? Did I not follow up or collaborated with my program manager to follow up shortly with the legal team to check their progress of evaluating issues? If I did not do these things, it is on me.
Let's assume I did do those things, but the legal team needed a lot of time to evaluate or get external opinion, th"
Anonymous Sparrow - "I would go look in the mirror and ask that person why the legal team is only flagging issues a week ahead of launch date. Did I not engage our legal team way in advance at the conception stage? Did I not follow up or collaborated with my program manager to follow up shortly with the legal team to check their progress of evaluating issues? If I did not do these things, it is on me.
Let's assume I did do those things, but the legal team needed a lot of time to evaluate or get external opinion, th"See full answer
"TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING:
TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency.
The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"
Satyam C. - "TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING:
TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency.
The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"See full answer
"Yes, I need to compare the first half of the first string with the reverse order of the second half of the second string. Repeat this process to the first half of the second string and the second half of the first string."
Anonymous Condor - "Yes, I need to compare the first half of the first string with the reverse order of the second half of the second string. Repeat this process to the first half of the second string and the second half of the first string."See full answer
"You can ask some clarifying questions like
1) Ask if the list is already sorted or not
2) is zero included in the list ?
3) Natural numbers are usually positive numbers ( clarify they are non negatives)
Solution :
1) If sorted use two pointers and sort them in O(N)
2) if not sorted , -ve / only +ve numbers in the list doesn't matter - the easiest solution is
Use a priority queue and push the number and its square in each iteration
Finally return the list returned by the priority Queue. N"
Bless M. - "You can ask some clarifying questions like
1) Ask if the list is already sorted or not
2) is zero included in the list ?
3) Natural numbers are usually positive numbers ( clarify they are non negatives)
Solution :
1) If sorted use two pointers and sort them in O(N)
2) if not sorted , -ve / only +ve numbers in the list doesn't matter - the easiest solution is
Use a priority queue and push the number and its square in each iteration
Finally return the list returned by the priority Queue. N"See full answer