In the rapidly evolving field of AI, the distinction between foundation models and task models is critical for understanding how modern AI systems work. Foundation models, like GPT-4 or BERT, provide…
Pretraining vs fine-tuning in AI: what each stage does, masked and causal language modeling, key differences, why they are separate, and real uses.
An embedding is the “translator” that converts language into numbers, enabling AI models to understand and process human language. AI doesn’t comprehend words, sentences, or syntax—it only works with…
Prompt Engineering is a core technique in the field of generative AI. Simply put, it involves crafting effective input prompts to guide AI in producing the desired results.
In deep learning, parameters are the trainable components of a model, such as weights and biases, which determine how the model responds to input data. These parameters adjust during training to…
So, I did a thing. I earned my first Microsoft certificate: Azure AI Engineer Associate! 🎉
Fixing azure-cli install failures on macOS: the Homebrew OpenSSL and Python permission errors I hit, and the chown and brew unlink commands that worked.
Days 11-12 of Andrew Ng's Supervised Machine Learning course: overfitting vs underfitting, and fixing them with more data, simpler models or regularization.