This post is divided into seven parts; they are: – Core Text Generation Parameters – Experimenting with Temperature – Top-K and Top-P Sampling – Controlling Repetition – Greedy Decoding and Sampling – Parameters for Specific Applications – Beam Search and…
Python is one of the most popular languages for machine learning, and it’s easy to see why. Source link
In the era of generative AI, people have relied on LLM products such as ChatGPT to help with tasks. Source link
In 2000, Patrick J. McGovern ’59 and Lore Harp McGovern made an extraordinary gift to establish the McGovern Institute for Brain Research at MIT, driven by their deep curiosity about the human mind and their belief in the power of…
What happens when a fashion legend taps into the transformative power of artificial intelligence? For more than five decades, fashion designer and entrepreneur Norma Kamali has pioneered bold industry shifts, creating iconic silhouettes worn by celebrities including Whitney Houston and…
Essential for many industries ranging from Hollywood computer-generated imagery to product design, 3D modeling tools often use text or image prompts to dictate different aspects of visual appearance, like color and form. As much as this makes sense as a…
Despite significant advances in reasoning capabilities through reinforcement learning (RL), most large language models (LLMs) remain fundamentally dependent on supervised data pipelines. RL frameworks such as RLHF have pushed model alignment and instruction-following performance but rely heavily on human feedback…
Optuna is a machine learning framework specifically designed for automating hyperparameter optimization , that is, finding an externally fixed setting of machine learning model hyperparameters that optimizes the model’s performance. Source link
Be sure to check out the previous articles in this series: • <a href=" Source link
Revisiting the Grokking Challenge In recent years, the phenomenon of grokking—where deep learning models exhibit a delayed yet sudden transition from memorization to generalization—has prompted renewed investigation into training dynamics. Initially observed in small algorithmic tasks like modular arithmetic, grokking…
