Artificial Intelligence
Overview
I began my journey into Artificial Intelligence by researching and practicing the concepts that are required to build AI models. My focus is on understanding deep learning architectures, neural networks, and optimization techniques that enhance model performance. This will enable me to build and optimize AI models capable of processing and generating meaningful results.
Key Skills & Technologies
- Programming: Python
- Frameworks/Libraries: PyTorch, Transformers
- Tools: Google Colab, Jupyter Notebook
- Concepts: Deep Learning, NLP, Model Optimization, Self-Supervised Learning
Notable Projects
- Mini GPT Model
- AI Fundamentals Exploration: Developed a mini GPT model to dive into core AI concepts, implementing techniques like tokenization, attention mechanisms, and self-supervised learning to generate coherent text.
- Model Optimization: Applied strategies to enhance model stability and accuracy, including efficient batching, layer normalization, dropout regularization, and mixed-precision training.
Achievements & Impact
- Optimized Text Generation: Improved text generation capabilities by fine-tuning hyperparameters for training and memory allocation.
- Enhanced Model Performance: Boosted training stability and overall performance through techniques like efficient batching, gradient clipping, and optimization strategies.
Future Goals & Learning
I am currently focused on refining AI models through advanced techniques in hyperparameter optimization, transfer learning, and deploying AI models for real-world applications. Additionally, I aim to deepen my expertise in Natural Language Processing (NLP) to improve language models and text generation.