Udemy - Understanding Prompt Engineering

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Free Download Udemy - Understanding Prompt Engineering
Last updated 3/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.86 GB | Duration: 0h 43m
Towards Excellence

What you'll learn
This course delves into the principles, strategies, and best practices of prompt engineering, a crucial aspect in shaping AI models' behavior and performance.
Digital Challenges and Problem-Solving
Enhance self-awareness and provide insights into patterns and triggers.
Help individuals stay present, observe their emotions without judgment, and reduce reactivity.
Requirements
No programming experience needed
Description
This course delves into prompt engineering principles, strategies, and best practices, a crucial aspect in shaping AI models' behaviour and performance. Understanding Prompt Engineering is a comprehensive course designed to equip learners with the knowledge and skills to effectively generate and utilize prompts in natural language processing (NLP) and machine learning (ML) applications. This course delves into prompt engineering principles, strategies, and best practices, a crucial aspect in shaping AI models' behaviour and performance.Module 1: Introduction to Prompt EngineeringLesson 1: Foundations of Prompt EngineeringOverview of prompt engineering and its significance in NLP and ML.Historical context and evolution of prompt-based approaches.Module 2: Types of Prompts and Their ApplicationsLesson 2: Closed-Ended PromptsUnderstanding and creating prompts for specific answers.Applications in question-answering systems.Lesson 3: Open-Ended PromptsCrafting prompts for creative responses.Applications in language generation models.Module 3: Strategies for Effective PromptingLesson 4: Probing PromptsDesigning prompts to reveal model biases.Ethical considerations in using probing prompts.Lesson 5: Adversarial PromptsCreating prompts to stress-test models.Enhancing robustness through adversarial prompting.Module 4: Fine-Tuning and Optimizing with PromptsLesson 6: Fine-Tuning Models with PromptsTechniques for incorporating prompts during model training.Balancing prompt influence and generalization.Lesson 7: Optimizing Prompt SelectionMethods for selecting optimal prompts for specific tasks.Customizing prompts based on model behavior.Module 5: Evaluation and Bias MitigationLesson 8: Evaluating Prompt PerformanceMetrics and methodologies for assessing model performance with prompts.Interpreting and analyzing results.Lesson 9: Bias Mitigation in Prompt EngineeringStrategies to identify and address biases introduced by prompts.Ensuring fairness and inclusivity in prompt-based models.Module 6: Real-World Applications and Case StudiesLesson 10: Case Studies in Prompt EngineeringExploration of successful implementations and challenges in real-world scenarios.Guest lectures from industry experts sharing their experiences.
Intended Learners for basics

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Understanding Prompt Engineering
Last updated 3/2024
Duration: 43m | .MP4 1920x1080, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.85 GB
Genre: eLearning | Language: English​

Towards Excellence

What you'll learn
- This course delves into the principles, strategies, and best practices of prompt engineering, a crucial aspect in shaping AI models' behavior and performance.
- Digital Challenges and Problem-Solving:
- Enhance self-awareness and provide insights into patterns and triggers.
- Help individuals stay present, observe their emotions without judgment, and reduce reactivity.

Requirements
- No programming experience needed

Description
This course delves into prompt engineering principles, strategies, and best practices, a crucial aspect in shaping AI models' behaviour and performance. Understanding Prompt Engineering is a comprehensive course designed to equip learners with the knowledge and skills to effectively generate and utilize prompts in natural language processing (NLP) and machine learning (ML) applications. This course delves into prompt engineering principles, strategies, and best practices, a crucial aspect in shaping AI models' behaviour and performance.

Module 1: Introduction to Prompt Engineering

Lesson 1: Foundations of Prompt Engineering

Overview of prompt engineering and its significance in NLP and ML.

Historical context and evolution of prompt-based approaches.

Module 2: Types of Prompts and Their Applications

Lesson 2: Closed-Ended Prompts

Understanding and creating prompts for specific answers.

Applications in question-answering systems.

Lesson 3: Open-Ended Prompts

Crafting prompts for creative responses.

Applications in language generation models.

Module 3: Strategies for Effective Prompting

Lesson 4: Probing Prompts

Designing prompts to reveal model biases.

Ethical considerations in using probing prompts.

Lesson 5: Adversarial Prompts

Creating prompts to stress-test models.

Enhancing robustness through adversarial prompting.

Module 4: Fine-Tuning and Optimizing with Prompts

Lesson 6: Fine-Tuning Models with Prompts

Techniques for incorporating prompts during model training.

Balancing prompt influence and generalization.

Lesson 7: Optimizing Prompt Selection

Methods for selecting optimal prompts for specific tasks.

Customizing prompts based on model behavior.

Module 5: Evaluation and Bias Mitigation

Lesson 8: Evaluating Prompt Performance

Metrics and methodologies for assessing model performance with prompts.

Interpreting and analyzing results.

Lesson 9: Bias Mitigation in Prompt Engineering

Strategies to identify and address biases introduced by prompts.

Ensuring fairness and inclusivity in prompt-based models.

Module 6: Real-World Applications and Case Studies

Lesson 10: Case Studies in Prompt Engineering

Exploration of successful implementations and challenges in real-world scenarios.

Guest lectures from industry experts sharing their experiences.

Who this course is for:
- Intended Learners for basics
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