Hey, I'm Yogesh
Available for hire
Driven and dedicated aspiring Machine Learning Engineer, fervently striving to cultivate a deep understanding of the art and science of predictive modeling, artificial intelligence, and data-driven decision making.
This platform is a showcase of my exploration, learning journey, where I endeavor to bridge the gap between theoretical understanding and practical application.
Latest projects
News Research Tool
This project showcases a sophisticated news retrieval and question-answering (Q&A) system using Gradio, LangChain, and OpenAI's GPT models. The system is designed to process user-provided URLs of news articles, analyze their content, and deliver contextually relevant answers to user queries.
NLP
News Research Tool
This project showcases a sophisticated news retrieval and question-answering (Q&A) system using Gradio, LangChain, and OpenAI's GPT models. The system is designed to process user-provided URLs of news articles, analyze their content, and deliver contextually relevant answers to user queries.
NLP
News Research Tool
This project showcases a sophisticated news retrieval and question-answering (Q&A) system using Gradio, LangChain, and OpenAI's GPT models. The system is designed to process user-provided URLs of news articles, analyze their content, and deliver contextually relevant answers to user queries.
NLP
News Research Tool
This project showcases a sophisticated news retrieval and question-answering (Q&A) system using Gradio, LangChain, and OpenAI's GPT models. The system is designed to process user-provided URLs of news articles, analyze their content, and deliver contextually relevant answers to user queries.
NLP
Dog Vision
This project presents a robust multi-category image classification solution, developed utilizing TensorFlow and TensorFlow Hub. If you're interested in the practical application, feel free to jump directly to the final section where you can interactively evaluate the model with your own images stored in Google Drive. The heart of this system is the incorporation of transfer learning principles, leveraging the power of the pre-trained MobileNet V3 Convolutional Neural Network (CNN). By acting as the foundation, this model expedited the learning process while enhancing the performance. The architecture is topped with a Softmax output layer, tailored for multi-category classification, and optimized using the efficient Adam algorithm.
Computer Vision
Dog Vision
This project presents a robust multi-category image classification solution, developed utilizing TensorFlow and TensorFlow Hub. If you're interested in the practical application, feel free to jump directly to the final section where you can interactively evaluate the model with your own images stored in Google Drive. The heart of this system is the incorporation of transfer learning principles, leveraging the power of the pre-trained MobileNet V3 Convolutional Neural Network (CNN). By acting as the foundation, this model expedited the learning process while enhancing the performance. The architecture is topped with a Softmax output layer, tailored for multi-category classification, and optimized using the efficient Adam algorithm.
Computer Vision
Dog Vision
This project presents a robust multi-category image classification solution, developed utilizing TensorFlow and TensorFlow Hub. If you're interested in the practical application, feel free to jump directly to the final section where you can interactively evaluate the model with your own images stored in Google Drive. The heart of this system is the incorporation of transfer learning principles, leveraging the power of the pre-trained MobileNet V3 Convolutional Neural Network (CNN). By acting as the foundation, this model expedited the learning process while enhancing the performance. The architecture is topped with a Softmax output layer, tailored for multi-category classification, and optimized using the efficient Adam algorithm.
Computer Vision
Dog Vision
This project presents a robust multi-category image classification solution, developed utilizing TensorFlow and TensorFlow Hub. If you're interested in the practical application, feel free to jump directly to the final section where you can interactively evaluate the model with your own images stored in Google Drive. The heart of this system is the incorporation of transfer learning principles, leveraging the power of the pre-trained MobileNet V3 Convolutional Neural Network (CNN). By acting as the foundation, this model expedited the learning process while enhancing the performance. The architecture is topped with a Softmax output layer, tailored for multi-category classification, and optimized using the efficient Adam algorithm.
Computer Vision
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