GPT vs BERT Explained : Transformer Variations & Use Cases Simplified

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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

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⏱️ 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

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#GPTvsBERT #Transformers #DeepLearning #NLP #LLMs #BERTExplained #GPTExplained #LanguageModels #AI #NeuralNetworks #MachineLearning