AI, ML & Robotics

• Anomaly Detection in copper coils - BHEL• Experimented with Computer vision algorithms like Patchcore, YOLO, Object tracking and Image compressionalgorithms.• Video Streaming using Flask,React,NodeJS,Express.• ROS Operating System, Path finding algorithms, Linux, Gazebo• Modelled and trained LSTM for predicting time until failure of aircraft engines.• Focused on building scalable systems implementing machine learning models.

Position Overview:During my internship at ResoluteAI.in, I had the opportunity to work as a Deep Learning Intern. It was an exciting role that allowed me to delve into the field of artificial intelligence (AI) with a specific focus on deep learning, computer vision, natural language processing (NLP), and machine learning. Over the course of the 4-month program, I gained hands-on experience, contributed to cutting-edge projects, and worked in a collaborative and dynamic environment.Key Responsibilities:▪️ Research and Development: I collaborated closely with the AI team and explored various applications of AI, such as computer vision, NLP, and machine learning. It was important for me to stay updated with the latest advancements in the field and share my insights and ideas to enhance the AI capabilities of the company.▪️Model Development and Training: I actively participated in designing, implementing, and training deep learning models using popular frameworks like TensorFlow and PyTorch. I had the chance to experiment with different architectures, optimization techniques, and hyperparameter tuning to improve the performance and accuracy of the models.▪️Data Preprocessing and Analysis: Working with large datasets, I engaged in data preprocessing tasks, which involved cleaning, normalization, and feature extraction. I also conducted in-depth data analysis to identify patterns and extract meaningful insights that could be utilized in model development and evaluation.▪️Experimentation and Evaluation: An essential part of my role was to conduct experiments to evaluate the performance of deep learning models on specific tasks. I developed metrics and benchmarks to measure the effectiveness and efficiency of the models, and I carefully analysed and interpreted the results to provide valuable recommendations for further improvement.