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by Ajit Dash (Author)
Dive into the world of OpenAI and Azure OpenAI in 36 hrs with this comprehensive guide. From understanding the fundamentals of AI and machine learning to exploring the power of GPT models and advanced NLP techniques, this book equips you with the knowledge to leverage cutting-edge technologies. Learn how to harness tokens, navigate installations, and master prompt engineering. Whether you're a developer, data professional, or AI enthusiast, this book offers hands-on engagement, real-world relevance, and practical insights. Unveil the potential of OpenAI and Azure OpenAI to transform your AI journey
In this Step-by-Step Guide, you'll find clear and concise instructions, practical examples, and hands-on exercises that will accelerate your learning journey. From understanding the fundamentals of AI, OpenAI, Azure OpenAI & Machine learning to exploring natural language processing and building chatbots, you'll gain the knowledge and confidence to apply these technologies in real-world scenarios.
Chapter 2: Understanding AI and ML - Delving into artificial intelligence and machine learning, key components, and the role of deep learning and neural networks.
Chapter 3: Introduction to NLP - Basics of natural language processing, techniques like tokenization and text classification, and language model applications.
Chapter 4: Exploring GPT Models - Overview of GPT models, details of GPT-3 and GPT-4, their architecture, capabilities, and differences, including GPT-3.5 Turbo and Codex.
Chapter 5: How Tokens Work - Understanding tokens in GPT models, their benefits, and examples of successful Azure OpenAI implementations.
Chapter 6: Installation of OpenAI - Setting up an OpenAI account, exploring OpenAI product offerings, understanding ChatGPT, DALL-E 2, and APIs.
Chapter 7: Installation of Azure OpenAI - Installing and setting up Azure CLI, creating an Azure account, deploying models, and utilizing Azure OpenAI playground and resources.
Chapter 8: GPT Model Architecture - Unveiling the architecture of GPT models, their history, and learning more about LLM.
Chapter 9: Prompt Engineering - Exploring prompt engineering, in-context shots, text classification, and various use cases.
Chapter 10: Content Filtering - Examining content filtering goals, methods, and applications.
Chapter 11: Hallucination and Fine-Tuning - Understanding hallucination, fine-tuning, and their best practices.
Chapter 12: Use Cases - Installation instructions and examples of interview question generators, stock market recommendations, and building chatbots.
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