Transformers in Deep Learning: Breakthroughs from ChatGPT to DeepSeek – Day 66

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 Table 1: Comparison of Transformers, RNNs, and CNNs Feature Transformers RNNs CNNs Processing Mode Parallel Sequential Localized (convolution) Handles Long Dependencies Efficient Struggles with long sequences Limited in handling long dependencies Training Speed Fast (parallel) Slow (sequential) Medium speed due to parallel convolution Key Component Attention Mechanism Recurrence (LSTM/GRU) Convolutions Number of Layers 6–24 layers per encoder/decoder 1-2 (or more for LSTMs/GRUs) Typically 5-10 layers Backpropagation Through attention and feed-forward layers Backpropagation Through Time (BPTT) Standard backpropagation Self-Attention Mechanism The self-attention mechanism allows each word in a sequence to attend to every other word, capturing relationships between distant parts of the input. This mechanism is…