Netflix Machine Learning Engineer Interview Questions

Anonymous Roadrunner - "def is_valid(s: str) -> bool: stack = [] closeToOpen = { ")" : "(", "]" : "[", "}" : "{" } for c in s: if c in closeToOpen: if stack and stack[-1] == closeToOpen[c]: stack.pop() else: return False else: stack.append(c) return True if not stack else False debug your code below print(is_valid("()[]")) `"See full answer

Prajwal M. - "from typing import List def maxprofitgreedy(stock_prices: List[int]) -> int: l=0 # buying r=1 # selling max_profit=0 while rstock_prices[l]: profit=stockprices[r]-stockprices[l] maxprofit=max(maxprofit,profit) else: l=r r+=1 return max_profit debug your code below print(maxprofitgreedy([7, 1, 5, 3, 6, 4])) `"See full answer

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

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