Gopinath Asokan

Developed an AI application using LLM to analyze user resumes and provided the summarization, strengths, weaknesses, suggestions, suitable job titles, and also scraping job details from LinkedIn using Selenium. This application reduces time by 30% and helps candidates tailor their resumes effectively.

Engineered a robust deep learning model using Convolutional Neural Networks and TensorFlow to classify 114 bird species based on audio recordings. Model achieved an impressive accuracy of 93.4%, providing valuable insights for conservationists and ecologists in the wildlife & ecological research sectors.

Engineered an AI model utilizing advanced Computer Vision and Google Generative AI to solve math problems by recognizing hand gestures, offering real-time, accurate solutions through an intuitive interface inspired by Apple Tablet calculators.

Build a machine learning model to predict weekly sales with 97.4% accuracy. Integrated Exploratory Data Analysis tools to analyze trends, patterns, and actionable insights. The solution enables detailed sales comparisons, evaluates feature impacts and ranges, and identifies top performers, greatly enhancing decision-making in the retail industries.

Engineered an advanced deep learning model to automate the classification of financial documents, including Balance Sheets, Cash Flow and Income Statements using Bidirectional LSTM and TensorFlow. The model achieved an impressive accuracy of 96.2%, enhancing efficiency and reducing errors in document management for the finance and banking sectors.

Developed an LLM application using the YouTube Transcript API and Google Gemini AI to generate clear summaries of YouTube videos. It extracts transcripts in various languages and uses AI to provide actionable insights. Built with Streamlit, the app features an intuitive interface for quick and efficient video summarization, boosting productivity.