This guide introduces the capabilities of Generative AI, showcasing various models that can create new content, from realistic text-to-video scenes to strategic business materials. Readers will explore the diverse and rapidly growing enterprise applications of this technology, learning how businesses leverage it for content generation, personalized marketing, customer service, risk management, and software development. The text also addresses critical challenges in implementing Generative AI, such as managing AI hallucinations.
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Chapters
- 137 Generating Videos (Free teaser)
- 14 Enterprise Use Cases ($0.25)
- 19 So Your Enterprise Wants To Use Generative Ai Now What ($0.25)
- 21 Overview Of Foundational Models ($0.25)
- 24 Training Cutoff ($0.25)
- 26 Small Language Models ($0.25)
- 271 Commercial Llms ($0.25)
- 28 Key Concepts Of Llms ($0.25)
- An Example ($0.25)
- Here Are The Cons ($0.25)
- 289 Emergent Behavior ($0.25)
- This Chapter Covers ($0.25)
- 311 Dependencies ($0.25)
- 312 Listing Models ($0.25)
- 321 Expanding Completions ($0.25)
- 324 Controlling Randomness ($0.25)
- 332 Influencing Token Probabilities Logitbias ($0.25)
- 334 Log Probabilities ($0.25)
- 341 System Role ($0.25)
- 344 Managing Conversation ($0.25)
- 346 Additional Llm Providers ($0.25)
- 41 Vision Models ($0.25)
- 412 Generative Adversarial Networks ($0.25)
- 42 Image Generation With Stable Diffusion ($0.25)
- 44 Editing And Enhancing Images Using Stable Diffusion ($0.25)
- 442 Using The Masking Api ($0.25)
- 51 Code Generation ($0.25)
- 523 Code Referencing ($0.25)
- 531 Amazon Codewhisperer ($0.25)
- 535 Best Practices For Code Generation ($0.25)
- Text Generation ($0.25)
- 64 Prompt Engineering Techniques ($0.25)
- 644 Making In Context Learning Work ($0.25)
- 71 What Is Rag ($0.25)
- What Is Data Grounding ($0.25)
- 752 Vector Search ($0.25)
- 772 Factors Affecting Chunking Strategies ($0.25)
- 774 Chunking Sentences ($0.25)
- Using Spacy ($0.25)
- Handling Tables And Images In Pdf ($0.25)
- 812 Building A Chat Application Using Our Data ($0.25)
- Continued ($0.25)
- Tagfield Vs Textfield ($0.25)
- 85 Search Using Redis ($0.25)
- 912 Advantages And Challenges For Enterprises ($0.25)
- 921 Key Stages Of Fine Tuning An Llm ($0.25)
- Listing 93 Dataset Validation Checking For Format ($0.25)
- 932 Llm Evaluation ($0.25)
- Choosing Appropriate Metrics ($0.25)
- Fine Tuning Using The Sdk ($0.25)
- 934 Fine Tuning Training Metrics ($0.25)
- 941 Inference Fine Tuned Model ($0.25)
- 96 Model Adaptation Techniques ($0.25)
- Quantization ($0.25)
- 97 Rlhf Overview ($0.25)
- 972 Scaling An Rlhf Implementation ($0.25)
- 101 Generative Ai Application Architecture ($0.25)
- 102 Generative Ai Application Stack ($0.25)
- 1022 Genai Architecture Principles ($0.25)
- Orchestration Layer ($0.25)
- 103 Orchestration Layer ($0.25)
- 1032 Orchestration Frameworks ($0.25)
- Building Your Own Orchestrator Framework ($0.25)
- Sk Example ($0.25)
- Langchain ($0.25)
- Llamaindex ($0.25)
- 1034 Prompt Management ($0.25)
- 1051 Model Ensemble Architecture ($0.25)
- 1142 Latency ($0.25)
- 1153 Caching ($0.25)
- When We Run This An Example Output Is ($0.25)
- 1222 Rouge ($0.25)
- 1231 G Eval A Measuring Approach For Nlg Evaluation ($0.25)
- 1232 An Example Of Llm Based Evaluation Metrics ($0.25)
- 1233 Helm ($0.25)
- 1236 Massive Multitask Language Understanding ($0.25)
- B3 Hax Toolkit ($0.25)
- B5 Learning Interpretability Tool Lit ($0.25)
- Chapter 9 ($0.25)
- Chapter 13 ($0.25)