QCards AI

Market capitalization (market cap) is a measure of a company’s value calculated by multiplying the stock price by outstanding shares. It helps investors assess a company’s size, categorize investments (large-cap, mid-cap, small-cap), and diversify portfolios. However, it’s crucial to consider other financial metrics beyond market cap, such as revenue, earnings, cash flow, industry trends, and management quality when making investment decisions.

The P/E ratio compares a company’s stock price to its earnings per share (EPS) and is calculated by dividing the stock price by the EPS. It helps investors assess if a stock is overvalued or undervalued. A high P/E ratio suggests overvaluation, while a low P/E ratio may indicate undervaluation.

Hyperparameter tuning involves adjusting parameters of a machine learning model to optimize its performance. This process can be done manually or with automated algorithms. It is iterative, with different combinations of parameters tested to maximize the target metric, like accuracy. Cross-validation is often used to ensure the model generalizes well.

Hyperparameter tuning is crucial as it directly impacts model performance. By adjusting hyperparameters, data scientists can optimize model results. Choosing appropriate values during tuning is essential for achieving accurate and high-performing models.

External knowledge sources like databases or documents provide contextual information for RAG models to generate accurate responses, particularly when the information is not present within the model itself.

Transfer learning is using knowledge from one task to help with another. Fine-tuning is adjusting a pretrained model for a specific task. RAG is an NLP model that combines a language model with a knowledge retrieval system.

Fine-tuning and transfer learning involve adapting preexisting models to new tasks, while RAG is a model architecture that combines external information retrieval with generative AI abilities.

It is crucial to address flaws in pretrained models before fine-tuning because any biases or security vulnerabilities in the pretrained model can transfer to the fine-tuned model. Failure to correct these flaws beforehand can result in the persistence or worsening of these issues in the fine-tuned model.

Flaws in pretrained models can impact fine-tuned models due to heavy reliance during fine-tuning. Biases or vulnerabilities in the pretrained model may persist in the fine-tuned model, possibly worsening if not corrected beforehand.

Balancing new and previously learned knowledge during model training is crucial. Freezing too many layers can hinder adaptation to new tasks, while freezing too few may lead to loss of important pre-learned features. It’s essential to find a balance to avoid forgetting general knowledge or being unable to adapt effectively.