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Name: VIVEK REDDY CHITHARI

Type: User

Bio: Machine Learning Engineer ๐Ÿš€๐Ÿ’ก | Python, TensorFlow, PyTorch, Keras | Always Learning ๐Ÿ“˜๐Ÿ’ป | Exploring the AI frontier ๐Ÿค–

Location: New Brunswick, NJ

Hi there, I'm Vivek! ๐Ÿ‘‹

๐Ÿš€ About Me

I'm a Machine Learning Engineer passionate about pushing the boundaries of AI. I specialize in leveraging Python, TensorFlow, PyTorch, and Keras to create intelligent systems. I believe in the power of learning ๐Ÿ“˜๐Ÿ’ป and am constantly exploring new technologies and tools.

๐Ÿ”ง What I Do

  • Design and implement machine learning models for predictive analysis ๐Ÿ“Š
  • Develop robust data processing pipelines to ensure data integrity and usability ๐Ÿ› ๏ธ
  • Optimize AI algorithms for efficiency and scalability ๐Ÿ”
  • Integrate machine learning models with cloud services like AWS โ˜๏ธ
  • Create immersive data visualizations to provide actionable insights ๐Ÿ“ˆ

๐Ÿ“ซ How to reach me

You can reach me at [email protected]. I'm always open to interesting conversations and collaboration.

๐Ÿ“š What I'm currently learning

Currently, I'm learning about Deep Learning, Natural Language Processing and LLMs

Feel free to have a look at my work and let's get in touch!

VIVEK REDDY CHITHARI's Projects

advanced-nlp-with-transformers icon advanced-nlp-with-transformers

๐Ÿ”ฌ๐Ÿค– Explore advanced NLP with Transformers! ๐Ÿ’ก A hands-on project covering 5๏ธโƒฃ major tasks: text summarization ๐Ÿ“, classification ๐Ÿท๏ธ, question answering โ“, named entity recognition ๐Ÿ“‡, and relationship extraction ๐Ÿ’ผ. Leverages Hugging Face's Transformers, PyTorch, SpaCy, NLTK, and more! ๐Ÿš€๐Ÿ› ๏ธ๐Ÿ”ฅ Happy learning! ๐ŸŽ“๐Ÿ’ป๐ŸŒ

credit-decisions icon credit-decisions

๐Ÿ“š "Credit_Decisions" is an ML project ๐Ÿ’ผ that classifies credit applications ๐Ÿ’ณ predicting repayment likelihood. It offers explainability, offering insight into factors affecting the credit decision. ๐ŸŽ›๏ธ Using AWS services and LightGBM model with SHAP for explanations, this project seeks to improve transparency in credit decisions. ๐Ÿค–๐Ÿ”๐Ÿฆ

customer-churn-prediction icon customer-churn-prediction

๐Ÿ“ˆ Harness the power of LightGBM, CatBoost, TabTransformer & AutoGluon-Tabular in Amazon SageMaker ๐Ÿš€ for customer churn prediction. ๐Ÿ’ก๐Ÿฑ๐Ÿ”„๐Ÿ“Š We employ Automatic Model Tuning (AMT) to train ML models ๐Ÿง , identifying unhappy customers ๐Ÿ˜ž early to prevent business loss. ๐Ÿ’ธ Comparison of models included! ๐Ÿ“

dgl-fraud-detection icon dgl-fraud-detection

๐Ÿš€ Detecting financial fraud ๐Ÿ’ณ๐Ÿ’ฐ using Graph Neural Networks (GNNs) ๐Ÿ”€! Dive into how GNNs, Amazon SageMaker ๐Ÿงช, and the Deep Graph Library (DGL) collaborate to spot fraudulent transactions from complex datasets ๐Ÿ‘ฅ. Leverage R-GCN models ๐ŸŒ for multi-relational data ๐ŸŽฏ and enjoy better predictive performance ๐Ÿ†. Experiment with us! ๐Ÿ’ก๐Ÿ‘

dl-approach-to-lip-reading icon dl-approach-to-lip-reading

๐Ÿ“š๐Ÿ—ฃ๏ธ "LipNet: Lip Reading Project" ๐Ÿ‘€ Transforms video frames ๐ŸŽž๏ธ into text ๐Ÿ“„ using a spatiotemporal model ๐Ÿง ๐ŸŒ Achieves 95.2% accuracy ๐Ÿ† on the GRID corpus, outperforming humans and previous models! Model training, data prep ๐Ÿ”„๐Ÿ“‚, and inference ๐Ÿ’ก all included. Code can be run on StudioLab, nbviewer, and Google Colab ๐Ÿ”„๐Ÿ’ป.

fashiongan icon fashiongan

๐Ÿ‘—๐Ÿง FashionGAN: Dive into this hands-on tutorial to learn about Generative Adversarial Networks (GANs) using the Fashion MNIST dataset. Master GAN basics while generating fashionable grayscale images! ๐Ÿ“š๐Ÿ’ป #GAN #MachineLearning #AI

go_coding icon go_coding

๐Ÿš€ A straightforward Go application simulating a conference booking system for "Go Conference". Users can book tickets ๐ŸŽŸ๏ธ, with their details stored in memory ๐Ÿ’พ. The app checks ticket availability, accepts user details ๐Ÿ‘ค๐Ÿ‘ฅ๐Ÿ“ง, books tickets, and simulates email delivery ๐Ÿ“ค. A hands-on project to dive into Go!

itsjustacoincidenceprofessor_cfi-4 icon itsjustacoincidenceprofessor_cfi-4

"It's just a coincidence professor!" is a plagiarism checker for source code. It uses the Wagnerโ€“Fischer algorithm to precisely and accurately determine percentage similarity of two given strings. We also cross reference common sites like GitHub and Stackoverflow, for potential cheating.

llms-gpt-toolbox icon llms-gpt-toolbox

๐Ÿค– GPT For Y'all, a Streamlit-based language model application offering a range of features like basic language generation ๐Ÿ’ฌ, creative writing โœ, text summarization ๐Ÿ“, few-shot learning ๐ŸŽฏ, and Python code generation ๐Ÿ. An easy-to-use interface, designed for any language-related tasks. Your GPT model, our interface! ๐Ÿš€

object-detection-birds-species icon object-detection-birds-species

๐Ÿฆ AI-powered bird species object detection project leveraging Amazon SageMaker and the Single Shot Multi-Box Detection (SSD) algorithm. Utilizes the Caltech Birds dataset, provides detailed data prep, model training, hosting & testing instructions. Includes a bonus model improvement method. ๐Ÿš€๐Ÿง ๐Ÿ”ฌ

predictive-maintenance icon predictive-maintenance

๐Ÿ”งPredictive Maintenance: A comprehensive Jupyter ๐Ÿ“’ guide to create a predictive model for turbofan engines ๐Ÿš€ using Amazon SageMaker ๐Ÿง . It leverages NASA's Prognostic Center's dataset for authentic insights. Contribute, Fork & Improve! โš™๏ธ๐Ÿš€๐Ÿ”ฌ๐Ÿงฎ #DataScience #AWS #PredictiveMaintenance

recommender-system icon recommender-system

๐Ÿš€ Dive into custom recommender systems using Neural Collaborative Filtering (NCF) with this project! Utilizing the power of Amazon SageMakerโ˜๏ธ & TensorFlow 2.0๐Ÿ’ป, we experiment with MovieLens dataset๐ŸŽฌ for effective user-item interactions. Clone, build, and contribute! ๐Ÿ› ๏ธ๐Ÿ’ซ๐Ÿ“š

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