Despite significant progress in natural language processing, many AI systems continue to encounter difficulties with advanced reasoning, especially when faced with complex mathematical problems and intricate coding tasks. Current large language models sometimes struggle with multi-step logic and may not generalize well beyond their training data. Moreover, limitations in common-sense reasoning often hinder their broader… →

Deep Neural Network (DNN) training has experienced unprecedented growth with the rise of large language models (LLMs) and generative AI. The effectiveness of these models directly correlates with increasing their size, a development made possible by advances in GPU technology and frameworks like PyTorch and TensorFlow. However, training neural networks with billions of parameters presents… →

Feature selection plays a crucial role in statistical learning by helping models focus on the most relevant predictors while reducing complexity and enhancing interpretability. Lasso regression has gained prominence among various methods because of feature selection while simultaneously building a predictive model. It achieves this by enforcing sparsity through an optimization process that penalizes large… →