Machine Learning Engineer Interview Questions

Zain R. - "I've worked on projects not quite like this, but very similar, in the past - I'll borrow from that to answer this: The Broader Context this problem doesn't specify the type of data we're working with, or how it's being ingested to align with my personal background, I'll assume a picture that lends this problem well to being a computer vision (abbreviated "CV") related question: let's say we have a conveyor belt in a waste facility, which sequentially carries a stream of waste w"See full answer

Ankit R. - "i use google frequently, but most of the time i use for syntax and terms which i dont undestand, google is my go to guy."See full answer

Rick E. - " O(n) time, O(1) space from typing import List def maxsubarraysum(nums: List[int]) -> int: if len(nums) == 0: return 0 maxsum = currsum = nums[0] for i in range(1, len(nums)): currsum = max(currsum + nums[i], nums[i]) maxsum = max(currsum, max_sum) return max_sum debug your code below print(maxsubarraysum([-1, 2, -3, 4])) `"See full answer

Tiago R. - "class TrieNode { constructor() { this.children = {}; this.isEndOfWord = false; } } class Trie { constructor() { this.root = new TrieNode(); } insert(word) { let node = this.root; for (const char of word) { if (!node.children[char]) { node.children[char] = new TrieNode(); } node = node.children[char]; } node.isEndOfWord = true; } search(word) { l"See full answer

Vishwanath K. - "DevOps Engineer Interview Questions for 3+ yrs experience candidate"See full answer

Bhavya V. - "BERT - bidirectional encoder representations from transformer. For example:- it takes an entire sentence as input at once and understands the meaning of the words in that sentence and calculate the relations of words with each other irrespective of their positions from the original word to understand the meaning of the word using neighboring words. BERT model is a pre trained transformer model which can be fine-tuned for our purposes. It is used for tasks such sentimental analysis, question answ"See full answer

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

William M. - "Relu = 0 if > some threshold else x sigmoid normalizes to 0-1 asymptotically"See full answer