Documenting my path through the world of AI and machine learning. Every step, every milestone, every lesson learned.
After months of learning and experimenting, I finally launched this website to document my journey and share what I've learned with others. It represents not just a portfolio, but a commitment to continuous learning.
Spent the month understanding the transformer architecture that powers modern AI models like GPT and BERT. Built a small language model from scratch to solidify my understanding.
Successfully deployed my first machine learning model to production! An image classifier with 94% accuracy that can identify different types of AI-generated vs real images.
Started working with large language models, experimenting with different prompting techniques, building custom chatbots, and exploring the capabilities of GPT-4 and Claude.
Completed a comprehensive course on computer vision. Learned about CNNs, image classification, object detection, and semantic segmentation.
To truly understand how neural networks work, I built one from scratch using only NumPy. No frameworks, just pure mathematics and Python. This was a challenging but incredibly rewarding experience.
The beginning of an exciting adventure. I decided to dive deep into artificial intelligence and machine learning. Set up my development environment, started learning Python for ML, and completed my first tutorials.
Train and deploy a machine learning model with real-world data.
Become proficient in Python libraries like NumPy, Pandas, and Scikit-learn.
Learn the fundamentals of deep learning and neural network architectures.
Create a production-ready application using large language models.
Master Retrieval-Augmented Generation for building knowledge-based AI.
Fine-tune a language model for a specific domain or use case.