Overview

Our meticulously designed practice tests keeps pace with the AI industry's latest advancements, covers both depth and breath, while concentrating on the important topics including Model Architectures of Small Language Models (SLM) like BERT, T5, BART, ROBERTa, ALBERT, ELECTRA, Flan-T5, Reformer, DistilBERT, MobileBERT, Multilingual BERT, SentenceBERT, SpanBERT, Model Pretraining, Fine-tuning, Knowledge Distillation, Hugging Face library, Transformers Architecture, Attention Mechanism, Model Compression techniques, Tokenization, GLUE. Additionally, the course features real questions that are asked by leading tech companies.Sample Questions:1. How does multilingual BERT (mBERT) handle cross-lingual tasks?2. How does RoBERTa handle token masking differently than BERT during training?3. How are the dimensions of the Q, K, and V matrices determined in BERT?4. How does WordPiece tokenization handle unknown words during tokenization?5. For a BERT model with an embedding size of 1024 and 24 layers, how many parameters are in the embedding layer if the vocabulary size is 50,000?6. How does byte-level BPE improve upon traditional word-based tokenization methods?7. What is the main training objective of the ELECTRA model's generator component?8. What aspect of FLAN-T5 improves its performance over T5?9. What is the role of an attention mask in Transformer models?10. What is typically used as the "soft targets" in knowledge distillation?Topics Covered in the Course:-Model Architectures of Small Language Models (SLM) like BERT, T5, BART, ROBERTa, ALBERT, ELECTRA, Flan-T5, Reformer, DistilBERT, MobileBERT, Multilingual BERT, SpanBERT, SentenceBERT, XLM-ROBERTa, LongformerHugging Face Transformers LibraryModel Compression Techniques - Quantization, Pruning, Parameter sharing & Knowledge DistillationModel - Pretraining, Fine-tuning & Feature Extraction TechniquesEmbedding Models Encoder-Decoder ModelsDeep Learning for NLPSmall language models (SLMs) are gaining traction and are equally important as large language models (LLMs) to master for Gen AI and NLP Engineer roles because they offer faster inference times and require fewer computational resources, making them ideal for deployment in resource-constrained environments like mobile devices. They are more cost-effective to train and fine-tune for specific tasks, enabling broader accessibility and customization. Additionally, SLMs can still achieve high accuracy on specialized tasks when properly trained, providing a practical balance between performance and efficiency.Prepare comprehensively for Generative AI Engineer interviews with our Udemy course, "Gen AI Interview Questions - Small Language Models"

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