Do you want to read a summery of what is BERT in 2 min read? (Bidirectional Encoder Representations from Transformers) – day 67

Transformer Models Comparison Transformer Models Comparison Feature BERT GPT BART DeepSeek Full Transformer Uses Encoder? ✅ Yes ❌ No ✅ Yes ❌ No ✅ Yes Uses Decoder? ❌ No ✅ Yes ✅ Yes ✅ Yes ✅ Yes Training Objective Masked Language Modeling (MLM) Autoregressive (Predict Next Word) Denoising Autoencoding Mixture-of-Experts (MoE) with Multi-head Latent Attention (MLA) Sequence-to-Sequence (Seq2Seq) Bidirectional? ✅ Yes ❌ No ✅ Yes (Encoder) ❌ No Can be both Application NLP tasks (classification, Q&A, search) Text generation (chatbots, summarization) Text generation and comprehension (summarization, translation) Advanced reasoning tasks (mathematics, coding) Machine translation, speech-to-text Understanding BERT: How It Works and Why It’s Transformative in NLP BERT (Bidirectional Encoder Representations from Transformers) is a foundational model in Natural Language Processing (NLP) that has reshaped how machines understand language. Developed by Google in 2018, BERT brought significant improvements in language understanding tasks by introducing a bidirectional transformer-based architecture that reads text in both directions (left-to-right and right-to-left). This blog post will dive deep into how BERT works, its architecture, pretraining strategies, and its applications, complemented by tables and figures for better comprehension. BERT’s Architecture At its core, BERT is based on the transformer architecture, specifically utilizing the encoder part of the…