GPT vs BERT Explained : Transformer Variations & Use Cases Simplified скачать в хорошем качестве

GPT vs BERT Explained : Transformer Variations & Use Cases Simplified

🎓 Full Course HERE 👉 https://community.superdatascience.co... In this lesson, we break down the differences between two major Transformer model variations — GPT (decoder-only) and BERT (encoder-only). This visual tutorial dives into how these architectures differ in structure, function, and real-world application. We cover use cases such as machine translation, grammar correction, code generation, sentiment analysis, and more. You’ll gain a solid understanding of how GPT generates text versus how BERT classifies it — with clear visual explanations to make it all click. ✅ Understand the key architectural differences between GPT and BERT ✅ Discover how GPT enables generation and BERT enables classification ✅ Learn when to use decoder-only vs encoder-only models ✅ Visualize how masking and causality impact model capabilities ✅ Explore BERT’s bidirectionality and GPT’s autoregression with examples 🔗 Also find us here: 🌐 Website: https://www.superdatascience.com/ 💼 LinkedIn:   / superdatascience   📬 Contact: support@superdatascience.com ⏱️ Chapters: 00:00 – Welcome & Overview of Transformer Models 00:06 – GPT vs BERT: High-Level Comparison 00:17 – Transformer Applications (Translation, Summarization, Code) 01:35 – Decoder-Only (GPT) Architecture Explained 02:16 – Use Cases for GPT Models 03:04 – Encoder-Only (BERT) Architecture Breakdown 03:40 – Sentiment Analysis Example with BERT 04:13 – CLS Token and Its Role in Classification 05:16 – Linear Layers and Class Probability Output 06:00 – BERT vs GPT Output Mapping (3 Classes vs 200k Words) 06:45 – No Masking in BERT: What That Means 07:45 – GPT as Causal, BERT as Non-Causal 08:20 – Summary: Understanding Transformer Variations 🧠 Hashtags: #GPTvsBERT #Transformers #DeepLearning #NLP #LLMs #BERTExplained #GPTExplained #LanguageModels #AI #NeuralNetworks #MachineLearning