I'm a machine learning practitioner. I enjoy everything about the field of artificial intelligence: experimenting, developing and tuning models, studying the latest research papers in ML, testing new ideas, and experiencing the "magic" of what can be accomplished with linear algebra, backpropagation, and lots of training data.
If you're new here, check out my ml-projects repo, which is my incubation space for ideas and projects. Most of my projects have detailed explanations of algorithms and mathematical operations and are structured to be easy to follow to encourage learning.
Some examples of what I've worked on:
| ML from scratch Machine learning algorithms implemented in NumPy |
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| Neural network (for regression or classification) |
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| KMeans clustering |
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| Logistic regression |
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| and more... |
| Natual language processing (NLP) |
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| Topic modeling on 50 years of magazine issues - Using Non-Negative Matrix Factorization, Latent Dirichlet Allocation, and doc-topic Cosine Similarity |
| Extractive text summarization - And application to Wikipedia articles |
| Feature engineering with regex pattern matching - To analyze groups within a corpus |
| Dictionary key search - With fuzzy matching, to find keys in a nested JSON or dictionary object |
| and more... |
| Deep learning in PyTorch |
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| Full-page handwritten text recognition - Implementation of a research paper; uses a combination of a ResNet encoder and a Transformer decoder to capture text from a full page of my handwritten journal - 1D and 2D positional encoding; dataset and dataloader prep (e.g., torch.transform image transformations); gradient accumulation for memory-constrained GPUs; synthetic data generation; data augmentation; input sequence masking; training and validation |
| Class projects from an upper-level university computer science course - image style transfer; fine-tuning a ResNet classifier; training GANs; building language models using the Transformer and RNN architectures; using a U-Net for image segmentation; reinforcement learning (Deep Q and PPO networks) |
| Others |
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Vehicle specs analysis with dominant color labeling |
| Sentiment analysis |
| Part-of-speech tagging |
| Web scraping and APIs |
| Data cleaning |
| Object-oriented programming |
| Hyperparameter tuning |
| Feature selection |
| Model performance comparison and model selection |
| UI for interactive exploration |
| ML pipelines |
| Animated visualization |
| and more... |
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