In AI model fine tuning, data is only half the story. The other half lies in how we guide the learning process, through hyperparameters. These are high level configuration setting that control the training process, like how fast a model learns(learning rate), how many times it sees the data( epochs), and how much data it […]
Image credit: bdtechtalks In every interaction with an AI model, there’s a limit to how much information it can process at once. This is known as the context window, and it includes both the prompt you provide and the response the model generates. The model doesn’t just read the input and reply, it analyzes token […]
Image credit: otalk9focus Reinforcement Learning (RL) is a core pillar of modern AI systems. It mimics how human learn by trying, receiving feedback, and improving over time. (To better understand RL, think of how dogs are trained with rewards and penalties.) Unlike supervised learning, where models learn from labeled datasets, RL allows AI to explore […]
Image credit: nature In AI, learning isn’t one-size-fits-all. Models learn from data in different ways, and understanding the three core types – supervised, unsupervised and deep learning (descriptive learning) – can help us better grasp how modern AI powers everyday applications, especially recommendation systems on platforms like Netflix and Amazon. Supervised learning is like training […]