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Course Outline
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Module 01 |
Introduction to Prompting and ChatGPT |
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1.1 |
Basics of Prompting - Prompt Structure & Prompt Styles |
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1.2 |
Pitfalls of LLM Model |
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1.3 |
Open AI Models – Definition & Types |
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1.4 |
Need & Working ChatGPT |
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1.5 |
ChatGPT Capabilities |
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1.6 |
Key Concepts of GPT-3.5 |
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1.7 |
Demo: Account Creation (PaaS / SaaS) |
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Module 02 |
Applications of Effective Prompting using ChatGPT |
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2.1 |
Daily Tasks - Summary Generation, Proofreading, Language Translation, Email Writing, Blog Writing, Cold Sales Email, etc. |
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2.2 |
Technical Tasks – Excel Formula Creation, Code Writing, Code Debugging, etc. |
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2.3 |
Creativity Tasks – Headline/Tagline Creation, Content Creation, Blog Writing, etc. |
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Module 03 |
Different OpenAI Applications |
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3.1 |
GPT-3.5 Playground – Fine-Tuning of ChatGPT |
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3.2 |
DALL.E 2 – Image Creation / Image Editing |
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3.3 |
Codex – Natural Language to Animation Creation |
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Module 04 |
Techniques of Image Prompting |
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4.1 |
Need for Image Prompting |
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4.2 |
Advanced Techniques - Style Modifiers, Quality Booster, Repetition, Weighted Terms, Fix Deformed Generations, etc. |
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4.3 |
Use Cases - DALLE.2, Stable Diffusion & Midjourney |
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Module 05 |
EnhancingPrompt Reliability |
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5.1 |
Promote Debiasing & Ensemble Learning |
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5.2 |
Transparency & Privacy Concerns |
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5.3 |
Use Cases of Prompt Reliability |
Requirements
1. Python programming: Being proficient in Python is important since most deep learning libraries, like PyTorch and TensorFlow, use Python as the primary programming language.
2. Machine learning: Familiarity with basic machine learning concepts, techniques, and algorithms can help in understanding the foundations of ChatGPT.
3. Deep learning: Understanding neural networks, particularly recurrent neural networks (RNNs) and transformers, can help with fine-tuning and managing models like GPT.
4. Natural language processing (NLP): Familiarize yourself with common NLP techniques and libraries like NLTK and Spacy and understand context generation and text generation algorithms.
5. Reinforcement learning: GPT-3 involves reinforcement learning from human feedback, so understanding the basics of reinforcement learning and reward systems is useful.
Additional experience with cloud platforms like Google Cloud, Microsoft Azure or AWS and the basics of data storage, manipulation, and processing (SQL, pandas, NumPy) can also help when working with large datasets and model deployment.
7 Hours
Testimonials (3)
I very much appreciated the way the trainer presented everything. I understood everything even if Finance is not my area, he made sure that every participant was on the same page, while keeping up with the time left. The exercises were placed at good intervals. Communication with the participants was always there. The material was perfect, not too much, not too little. He elaborated very well on a bit more complicated subjects so that it can be understood by everyone.
Diana
Course - ChatGPT for Finance
The subject matter and the way it was presented.
Mark Mackintosh - IUM Insurance
Course - ChatGPT for Executives
Practical examples in Insurance
