🧩 Part 1/8

✔ What is Natural Language Processing (NLP)?
✔ What are the Challenges of NLP?
✔ Ambiguity
✔ Diversity
✔ Complexity
✔ What are the Traditional Approaches to NLP?
✔ Rule-Based Methods
✔ Statistical Methods
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🧩 Part 2/8

✔ What is BERT?
✔ Pre-training BERT
✔ Fine-tuning BERT
✔ BERT Variants and Applications
✔ RoBERTa
✔ ALBERT
✔ DistilBERT
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🧩 Part 3/8

✔ What is GPT-3 and how does it work?
✔ The architecture of GPT-3
✔ The training data and parameters of GPT-3
✔ What are the main features and capabilities of GPT-3?
✔ Text generation
✔ Zero-shot and few-shot learning
✔ Language modeling and natural language understanding
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🧩 Part 4/8

✔ What is Text-to-Text Transfer Learning?
✔ What is T5 and How Does It Work?
✔ The T5 Model Architecture
✔ The T5 Pre-training and Fine-tuning Objectives
✔ How to Use T5 for Various NLP Tasks?
✔ Text Summarization
✔ Text Generation
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🧩 Part 5/8

✔ Transformer-XL Architecture
✔ Recurrence Mechanism
✔ Relative Positional Encoding
✔ Transformer-XL Applications
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🧩 Part 6/8

✔ Transformer Architecture and BERT
✔ Transformer Encoder and Decoder
✔ BERT and Masked Language Modeling
✔ XLNet and Permutation Language Modeling
✔ Permutation Language Modeling and Autoencoding
✔ Two-Stream Attention and Autoregressive Modeling
✔ Comparison and Applications of XLNet and BERT
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🧩 Part 7/8

✔ Transformer Architecture and BERT
✔ Transformer Encoder and Decoder
✔ BERT Model and Pre-training
✔ ALBERT and Parameter Reduction Techniques
✔ Factorized Embedding Parameterization
✔ Cross-Layer Parameter Sharing
✔ ALBERT and Training Speed Improvement
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🧩 Part 8/8

✔ Transformer Architecture and Memory Bottleneck
✔ Encoder-Decoder Structure
✔ Self-Attention Mechanism
✔ Memory Complexity and Limitations
✔ Reformer: The Efficient Transformer
✔ Locality-Sensitive Hashing for Approximate Attention
✔ Reversible Residual Layers for Reduced Memory Footprint
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