Remove topics scenario-analysis
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Sentiment analysis in 2023: Empowering marketing with large language models (LLMs)

Data Science Dojo

Sentiment analysis, a dynamic process, extracts opinions, emotions, and attitudes from text. Here, sentiment analysis becomes the compass guiding marketing campaigns. Supercharging Marketing with Sentiment Analysis and LLMs Under the lens: How does sentiment analysis work?

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Top 7 large language models evaluations methods

Data Science Dojo

Benchmarks like MMLU and HellaSwag : These benchmarks test an LLM’s ability to handle complex language tasks and scenarios, gauging its generalizability and robustness. Bias and fairness analysis : Diversity and bias analysis are critical for evaluating the ethical aspects of LLMs.

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Real-Time Sentiment Analysis with Kafka and PySpark

Towards AI

Additionally, we’ll create an end-to-end sentiment analysis process, demonstrating the practical application of these technologies in real-world scenarios. Operating on a publish-subscribe messaging model, where producers are tasked with publishing data to topics while consumers subscribe to these topics to process the data.

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GPT 3.5 and GPT 4 comparative analysis

Data Science Dojo

Then skip the intro and quickly head to its comparative analysis. vs GPT-4: A Comparative Analysis 1. Complex Query Handling: Example: In a scenario where a user asks about the implications of a specific economic policy, GPT-4 provides a more nuanced and comprehensive analysis than GPT-3.5. cannot perform.

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Improve Cluster Balance with CPD Scheduler?—?Part 2

IBM Data Science in Practice

You can see more details in part 1 on this topic. This article continues on the same topic as part 2. The medium usage pressure scenario from using the medium cluster with medium deployment showed the biggest gap as 67%. The tests focus on the high resource usage pressure scenario. It covers more comprehensive test cases.

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RAG vs finetuning: Which is the best tool for optimized LLM performance?

Data Science Dojo

You can read the first blog of the series here – A guide to understanding RAG and finetuning While we provided a detailed guideline on understanding RAG and finetuning, a comparative analysis of the two provides a deeper insight. Fine-tuning is the go-to approach for achieving unparalleled depth and precision within a specific domain.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques. Statistical Analysis: Learn the Central Limit Theorem, correlation, and basic calculations like mean, median, and mode.