Projects

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AISR: AI Sports Recap -- usc logo
  • Developed an AI-powered tool that generates concise summaries and engaging highlight reels from sports press conference videos, enhancing fan and media experience.
  • Integrated advanced NLP and video analysis models, including GPT-4 and Pegasus1 (from Twelve Labs), to automate post-game recaps, interview snippets, and social media content creation.
  • Secured second place at "Twelve Labs: Multimodal AI Media & Entertainment" Hackathon. 🏆🏆
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Super Mario Brothers Gameplay using Reinforcement Learning -- usc logo
  • Utilized the Proximal Policy Optimization (PPO) algorithm to enhance gameplay strategies in "Super Mario Bros," leveraging the OpenAI Gym environment for robust and dynamic interaction.
  • Employed frame extraction and grayscale conversion, integrating a history of the previous four frames with the current one for improved performance and analysis, with the agent designed to run for an extensive one million iterations.
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Hidden Markov Model for Part of Speech Tagging -- usc logo
  • Implemented a Hidden Markov Model to perform Part of Speech (POS) tagging, leveraging the robustness of the Viterbi algorithm and greedy decoding for efficient and accurate analysis.
  • Employed statistical methods to calculate transition and emission probabilities, ensuring a high level of precision in identifying the grammatical parts of speech in text data.
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Variational Autoencoders for Digit Generation -- usc logo
  • Developed a comprehensive project on "Variational Autoencoders for Digit Generation," focusing on the implementation and analysis of Variational Auto-Encoders (VAEs) in machine learning. VAEs are renowned for their likelihood maximization and unsupervised representation learning capabilities.
  • Executed key tasks including setting up a PyTorch data loading pipeline, constructing an auto-encoder architecture, extending it to a VAE, tuning parameters, analyzing representations, and enhancing generative capabilities using the MNIST dataset of handwritten digits. This provided practical experience with VAEs' architecture and applications in generative modeling.
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Generative Adversarial Networks for Image Generation -- usc logo
  • Implemented a Generative Adversarial Network (GAN) based on "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", utilizing the CIFAR-10 dataset for training, and developed both discriminator and generator components.
  • Conducted a comprehensive training process with a robust loop, loss computation, and parameter updates, accompanied by activation maximization technique for visualization.
  • Combined practical coding of advanced neural network architectures with theoretical analysis, resulting in a deepened understanding of GANs, all documented in the repository for reference.
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Image White Balance Correction using Variational Autoencoders -- usc logo
  • Implemented a Variational Autoencoder Model for white balance correction in images, focusing on neutralizing color casts caused by different lighting conditions to achieve natural and accurate colors.
  • Utilized the Adobe White-Balanced Images Dataset, which includes 2,881 rendered images from various camera models, including mobile phones and a DSLR camera, for testing and experiments.
  • Developed an encoder with convolutional layers and ReLU activation, a reparameterization technique for sampling from the latent space, and a decoder with linear and transposed convolutional layers, complemented by a loss function combining reconstruction loss and Kullback-Leibler (KL) Divergence loss.
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Bidirectional LSTM for Named Entity Recognition -- usc logo
  • Implemented a Bidirectional LSTM (BLSTM) for Named Entity Recognition (NER) using GloVe word embeddings. Initialized neural network embeddings with GloVe vectors, addressing the case-insensitivity of GloVe by adding a boolean mask for case-sensitive NER recognition.
  • Developed model architecture with components like Embedding, LSTM, Linear, Dropout, and ELU layers, and trained using hyperparameters such as a batch size of 64, SGD optimizer, and a learning rate of 0.5 over 50 epochs, achieving an accuracy of 98.43%.