The test 'Language Model Training Techniques' covers key topics in neural networks, optimization algorithms, and data processing methods. It includes content such as hyperparameter tuning and implementing effective data augmentation techniques.
The 'Transfer Learning in NLP' test covers key questions on the fundamentals of transfer learning, the use of pre-trained models, and the challenges in adapting to specific tasks. Additional focuses include evaluating model performance and optimizing fine-tuning processes.
The 'Attention Mechanism' test covers key questions on how attention models work, especially self-attention and its application in neural networks. It includes topics like the mathematical formulation of attention scores and implementation in Transformer architectures.
The 'BPE Tokenization' test covers key topics in dividing text into smaller units, optimizing models through reduced vocabulary usage, and efficiently processing natural language. It also addresses the importance of token frequency and its impact on model performance.
