Automated Image Captioning with AI" PROJECT_DESCRIPTION: I'm looking for a developer proficient in implementing challenging AI projects. You'll be handling a project about automated image captioning, using convolutional neural networks (CNNs) for image fe
Budget: ₹1,500 – ₹12,500 INR
2) Automatic Image Captioning
Automatic Image Captioning is a fascinating project that combines computer vision and natural language processing. The goal is to generate descriptive captions for images. This project uses convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) to generate captions. Implementing this project will demonstrate your ability to work with complex neural network architectures and handle multimodal data.
3)Building a multi-step AI agent involves creating a system that can perform a series of tasks autonomously. Using frameworks like LangChain, you can develop AI agents that can handle complex workflows.
In this project, you will create an AI application that takes a user’s query to search the web using the Tavily API and also generates Python code to use the data. The application will then use Python REPL to execute the code and return the visualization requested by the user. Before starting the project, you will learn about the Cohere API and its various features.
Automatic Image Captioning is a fascinating project that combines computer vision and natural language processing. The goal is to generate descriptive captions for images. This project uses convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) to generate captions. Implementing this project will demonstrate your ability to work with complex neural network architectures and handle multimodal data.
3)Building a multi-step AI agent involves creating a system that can perform a series of tasks autonomously. Using frameworks like LangChain, you can develop AI agents that can handle complex workflows.
In this project, you will create an AI application that takes a user’s query to search the web using the Tavily API and also generates Python code to use the data. The application will then use Python REPL to execute the code and return the visualization requested by the user. Before starting the project, you will learn about the Cohere API and its various features.