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An application-based round in my case involves a chatbot that is based on LLM (Language Model).
I have worked on various projects including developing a mobile app for tracking fitness goals and creating a website for a local non-profit organization.
Developed a mobile app for tracking fitness goals using React Native and Firebase
Created a website for a local non-profit organization using HTML, CSS, and JavaScript
Collaborated with a team to implement new features and fix bugs in existing projects
A large language model (LLM) uses deep learning techniques to understand and generate human language.
LLMs are trained on vast amounts of text data to learn patterns and relationships in language.
They use neural networks to process and generate text, allowing them to understand and produce human-like language.
Examples of LLMs include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representa
I applied via Company Website and was interviewed in Aug 2024. There were 2 interview rounds.
It was on python and sql mostly
Data science, sql and deep learning
I applied via Campus Placement and was interviewed in Apr 2024. There were 2 interview rounds.
Speech to Speech bot
Implementing CNN code on notepad
Start by defining the CNN architecture with layers like Conv2D, MaxPooling2D, Flatten, and Dense
Compile the model with appropriate loss function and optimizer
Train the model on a dataset using fit() function
Evaluate the model's performance using test data and metrics like accuracy
LLM stands for Latent Language Model, which is a type of machine learning model used for natural language processing tasks.
LLM is a type of language model that learns to predict the next word in a sentence based on the context provided.
It uses latent variables to capture the underlying structure of the language.
LLM can be trained using unsupervised learning techniques such as autoencoders or variational autoencoders.
Ex...
TensorGo Technologies interview questions for popular designations
I appeared for an interview in Aug 2020.
Developed a real-time data processing system for a financial institution
Implemented a distributed system using Apache Kafka and Apache Storm
Designed a fault-tolerant architecture with multiple redundancy layers
Optimized the system for high throughput and low latency
Provided real-time monitoring and alerting using Grafana and Prometheus
posted on 14 Oct 2021
I applied via Walk-in and was interviewed before Oct 2020. There were 5 interview rounds.
I applied via Recruitment Consultant and was interviewed in Jun 2019. There were 4 interview rounds.
Answering questions related to Spring Boot and core Java
Spring Boot addons include Spring Security, Spring Data, and Spring Cloud
Spring Boot is a framework for building standalone, production-grade Spring-based applications
Ways of configuring a bean in Spring include XML configuration, Java-based configuration, and annotation-based configuration
Core Java topics include OOP concepts, collections, multithreading, and exc
I applied via Naukri.com and was interviewed before Oct 2019. There were 3 interview rounds.
I applied via Walk-in and was interviewed before Oct 2020. There was 1 interview round.
based on 5 interviews
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