i
Genesys
Filter interviews by
I was interviewed in Oct 2022.
MobileNet is a lightweight deep learning architecture designed for mobile and embedded vision applications.
MobileNet uses depthwise separable convolutions to reduce the number of parameters and computations.
It has a small memory footprint and can be easily deployed on mobile devices.
MobileNet has several variants, including MobileNetV1, MobileNetV2, and MobileNetV3.
MobileNetV2 introduced linear bottlenecks and inverted...
MobileNet is a lightweight deep learning model designed for mobile and embedded devices.
MobileNet uses depthwise separable convolutions to reduce the number of parameters and computations.
It has a small memory footprint and can be easily deployed on mobile and embedded devices.
MobileNet has been used for various applications such as image classification, object detection, and semantic segmentation.
It has achieved state...
DL architectures like CNNs require more computing power due to their complex structure and operations.
The architecture of Ai Ml is simpler compared to other DL architectures like CNNs.
It uses a single layer of neurons which reduces the number of computations required.
It also uses a linear activation function which is computationally less expensive.
Ai Ml is suitable for simpler tasks like linear regression and classific...
Mobilenet is a lightweight DL architecture designed for mobile devices.
Mobilenet uses depthwise separable convolutions to reduce computation and model size.
It has fewer parameters and lower computational requirements compared to other DL architectures like VGG and ResNet.
Mobilenet is optimized for mobile devices and can run in real-time on smartphones and other embedded devices.
Other DL architectures like VGG and ResNe...
Implement self-attention from scratch
I applied via Job Portal and was interviewed in Dec 2024. There was 1 interview round.
I applied via Approached by Company and was interviewed in Jul 2024. There were 2 interview rounds.
Developed a recommendation system for an e-commerce platform using collaborative filtering
Used collaborative filtering to analyze user behavior and recommend products
Implemented matrix factorization techniques to improve recommendation accuracy
Evaluated model performance using metrics like RMSE and precision-recall curves
I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.
Python programming language
TensorFlow framework
scikit-learn library
I would approach a machine learning problem by first understanding the problem, collecting and preprocessing data, selecting a suitable algorithm, training the model, evaluating its performance, and fine-tuning it.
Understand the problem statement and define the objectives clearly.
Collect and preprocess the data to make it suitable for training.
Select a suitable machine learning algorithm based on the problem type (clas...
I was interviewed in Oct 2024.
OHE is a technique used in machine learning to convert categorical data into a binary format.
OHE is used to convert categorical variables into a format that can be provided to ML algorithms.
Each category is represented by a binary vector where only one element is 'hot' (1) and the rest are 'cold' (0).
For example, if we have a 'color' feature with categories 'red', 'blue', 'green', OHE would represent them as [1, 0, 0],
Conditional probability is the likelihood of an event occurring given that another event has already occurred.
Conditional probability is calculated using the formula P(A|B) = P(A and B) / P(B)
It represents the probability of event A happening, given that event B has already occurred
Conditional probability is used in various fields such as machine learning, statistics, and finance
Precision and recall are evaluation metrics used in machine learning to measure the performance of a classification model.
Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.
Recall is the ratio of correctly predicted positive observations to the all observations in actual class.
Precision is important when the cost of false positives is high, while recall is i...
I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.
Developed a sentiment analysis model using NLP to analyze customer reviews for a product.
Collected and preprocessed text data from various sources
Performed tokenization, stopword removal, and lemmatization
Built a machine learning model using techniques like TF-IDF and LSTM
Evaluated the model's performance using metrics like accuracy and F1 score
Deployed the model for real-time sentiment analysis of new reviews
Cosine similarity is a measure of similarity between two non-zero vectors in an inner product space.
It measures the cosine of the angle between the two vectors.
Values range from -1 (completely opposite) to 1 (exactly the same).
Used in recommendation systems, text mining, and clustering algorithms.
Iterator is an object that allows iteration over a collection, while iterable is an object that can be iterated over.
Iterator is an object with a next() method that returns the next item in the collection.
Iterable is an object that has an __iter__() method which returns an iterator.
Example: List is iterable, while iter(list) returns an iterator.
Python function to calculate cosine similarity between two vectors.
Define a function that takes two vectors as input.
Calculate the dot product of the two vectors.
Calculate the magnitude of each vector and multiply them.
Divide the dot product by the product of magnitudes to get cosine similarity.
F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall.
F1 score is the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall).
It is a better metric than accuracy when dealing with imbalanced datasets.
A high F1 score indicates a model with both high precision and high recall.
F1 score ranges from 0 to 1, where
I applied via Company Website and was interviewed in Jul 2023. There were 4 interview rounds.
Python basic coding questions (pandas, numpy, OOPs concept.
AI ML theory questions
I applied via Referral and was interviewed in Jul 2024. There were 3 interview rounds.
30 MCQs where 15 Need to be answered correctly to get shortlisted.
Sanfoundary source is very helpful in cracking it.
Oops concepts refer to Object-Oriented Programming principles such as Inheritance, Encapsulation, Polymorphism, and Abstraction.
Inheritance: Allows a class to inherit properties and behavior from another class.
Encapsulation: Bundling data and methods that operate on the data into a single unit.
Polymorphism: Ability to present the same interface for different data types.
Abstraction: Hiding the complex implementation det
File handling refers to the process of managing and manipulating files on a computer system.
File handling involves tasks such as creating, reading, writing, updating, and deleting files.
Common file operations include opening a file, reading its contents, writing data to it, and closing the file.
File handling in programming languages often involves using functions or libraries specifically designed for file operations.
E...
Supervised learning uses labeled data to train a model, while unsupervised learning finds patterns in unlabeled data.
Supervised learning requires input-output pairs for training
Examples include linear regression, support vector machines, and neural networks
Unsupervised learning clusters data based on similarities or patterns
Examples include k-means clustering, hierarchical clustering, and principal component analysis
The zip function in Python is used to combine multiple iterables into a single iterable of tuples.
Zip function takes two or more iterables as arguments and returns an iterator of tuples where the i-th tuple contains the i-th element from each of the input iterables.
If the input iterables are of different lengths, the resulting iterator will only have as many elements as the shortest input iterable.
Example: zip([1, 2, 3...
NLP (Natural Language Processing) in machine learning is the ability of a computer to understand, interpret, and generate human language.
NLP enables machines to analyze and derive meaning from human language data.
It involves tasks such as text classification, sentiment analysis, named entity recognition, and machine translation.
Examples of NLP applications include chatbots, language translation services, and speech rec
based on 1 interview
Interview experience
Software Engineer
29
salaries
| ₹0 L/yr - ₹0 L/yr |
Senior Software Engineer
25
salaries
| ₹0 L/yr - ₹0 L/yr |
Associate Software Engineer
18
salaries
| ₹0 L/yr - ₹0 L/yr |
GIS Executive
17
salaries
| ₹0 L/yr - ₹0 L/yr |
Executive
13
salaries
| ₹0 L/yr - ₹0 L/yr |
TCS
Wipro
Infosys
HCLTech