- Creating an AI Agent to solve Sudoku
- Implement a Planning Search
- Dog Breed Classifier
- Time Series Prediction and Text Generation using RNNs
Refer links for individual project requirements.
Created an AI to solve Diagonal Sudokus using constraint propagation and search techniques. Additionally, taught the agent to use the Naked Twins advanced Sudoku strategy.
Used logic and planning techniques to create an AI that finds the most efficient route to route cargo around the world to their respective destinations. This project used a combination of propositional logic and search along with A* heuristics to find optimal planning solutions.
Built an algorithm to identify canine breed given an image of a dog. If given image of a human, the algorithm identifies a resembling dog breed.
Built RNNs that can generate sequences based on input data - with a focus on two applications: used real market data in order to predict future Apple stock prices using an RNN model. The second one was trained on Sir Arthur Conan Doyle's classic novel Sherlock Holmes and generates wacky sentences based on it.
Built a deep neural network that functions as part of an end-to-end machine translation pipeline. The completed pipeline accepts English text as input and returns the French translation.