Deepak has worked on numerous impressive projects. One of his notable projects is the Character-Level-GPT, where he developed a language model capable of generating coherent text at the character level, showcasing his expertise in deep learning and natural language processing. He also implemented an Audio/Video Summarizer using OpenAI's Whisper model for automatic transcription and content generation, which aids in quick comprehension of key points. Another project, the Bigram, MLP - Neural Network implementation, involved generating new Indian baby names using a dataset of over 55,000 names. He also created gradDescentor, a minimalist neural network implementation from scratch, which included a compact Autograd engine and a concise neural networks library. Additionally, Deepak developed a personal portfolio website using HTML, CSS, and JS. His FeedMePDF project involved creating a PDF reader application that parses data from PDFs and stores it in a vector database, enhancing data accessibility. Lastly, he developed a full-stack Movie Review Application using Java Spring Boot for the backend, React for the frontend, and MongoDB for the database.
Describe the projects completed by Deepak
<s>[INST] Describe the projects completed by Deepak[/INST] Deepak has worked on numerous impressive projects. One of his notable projects is the Character-Level-GPT, where he developed a language model capable of generating coherent text at the character level, showcasing his expertise in deep learning and natural language processing. He also implemented an Audio/Video Summarizer using OpenAI's Whisper model for automatic transcription and content generation, which aids in quick comprehension of key points. Another project, the Bigram, MLP - Neural Network implementation, involved generating new Indian baby names using a dataset of over 55,000 names. He also created gradDescentor, a minimalist neural network implementation from scratch, which included a compact Autograd engine and a concise neural networks library. Additionally, Deepak developed a personal portfolio website using HTML, CSS, and JS. His FeedMePDF project involved creating a PDF reader application that parses data from PDFs and stores it in a vector database, enhancing data accessibility. Lastly, he developed a full-stack Movie Review Application using Java Spring Boot for the backend, React for the frontend, and MongoDB for the database. </s>