This article presents six key regression evaluation metrics—RMSE, MAPE, MSE, MAE, R² Score, and Weighted MAPE—explaining their formulas, advantages, and limitations, and shows how each helps assess model performance in machine learning tasks.
The article presents a comparative table of variance, covariance, RMSE, MAE, and MSE, detailing their definitions, formulas, units, core functions, and typical application scenarios, and offers guidance on selecting the appropriate metric for single‑variable versus multivariate analysis, robustness needs, and model optimization.
This article introduces the MAE (Masked AutoEncoder) self‑supervised learning method, explains its asymmetric encoder‑decoder design and high masking ratio, evaluates its performance, and provides a step‑by‑step guide to reproduce MAE using Alibaba’s EasyCV framework, including code snippets, training tips, and troubleshooting.
This article traces the history of deep‑learning pre‑training techniques, comparing the parallel developments in natural‑language processing and computer vision—from early word2vec and bag‑of‑words models through ELMo and BERT to recent transformer‑based vision models like iGPT, ViT, BEiT and MAE—highlighting key innovations, challenges, and the convergence of the two fields.
This article provides a detailed technical walkthrough of Masked Autoencoders (MAE) for computer vision, covering its BERT‑inspired masking strategy, asymmetric encoder‑decoder design, implementation specifics, experimental findings on mask ratios and decoder depth, and the resulting performance gains over supervised ViT models.
This article traces the evolution of deep‑learning pre‑training techniques, starting with word2vec in NLP, moving through ELMo and BERT, then shifting to computer‑vision models such as iGPT, ViT, BEiT, and MAE, highlighting key innovations, challenges, and the convergence of NLP and CV paradigms.
