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Inside AI, Video Edition

سرفصل های دوره
  • Appendix A Tracing the roots From mechanical calculators to digital dreams
  • Appendix B Algorithms and programming languages
  • Appendix B. Programming languages
  • Chapter 1. Chatbots
  • Chapter 1. Error-prone intelligence
  • Chapter 1. Looking ahead
  • Chapter 1. Summary
  • Chapter 1. The AI revolution
  • Chapter 1. The rise of machine intelligence
  • Chapter 2. AI mastery Essential techniques, Part 1
  • Chapter 2. Case-based reasoning
  • Chapter 2. Expert systems
  • Chapter 2. Fuzzy logic
  • Chapter 2. Genetic algorithms
  • Chapter 2. Summary
  • Chapter 3. AI mastery Essential techniques, Part 2
  • Chapter 3. Artificial neural networks
  • Chapter 3. Bayesian networks
  • Chapter 3. Decision trees for fraud prevention
  • Chapter 3. Deep learning
  • Chapter 3. So, what is artificial intelligence
  • Chapter 3. Summary
  • Chapter 3. Unsupervised learning
  • Chapter 4. Smart agent technology
  • Chapter 4. Summary
  • Chapter 5. AI does not understand
  • Chapter 5. Bard
  • Chapter 5. Benefits of LLMs
  • Chapter 5. ChatGPT
  • Chapter 5. Generative AI and intellectual property
  • Chapter 5. Humans vs. LLMs
  • Chapter 5. LLMs and the Illusion of Understanding
  • Chapter 5. LLM limits
  • Chapter 5. Large language models
  • Chapter 5. Risks of generative AI
  • Chapter 5. Summary
  • Chapter 5. Generative AI and large language models
  • Chapter 6. Human vs. machine
  • Chapter 6. Summary
  • Chapter 6. Human vision vs. computer vision
  • Chapter 7. Lack of generalization
  • Chapter 7. Summary
  • Chapter 7. AI doesn t turn data into intelligence
  • Chapter 8. AI doesn t threaten our jobs
  • Chapter 8. Summary
  • Chapter 9. Merging human with machine
  • Chapter 9. Science fiction vs. reality
  • Chapter 9. Summary
  • Chapter 9. Technological singularity is absurd
  • Chapter 9. The truth about the evolution of robotics
  • Chapter 10. AI failures
  • Chapter 10. AI misuse
  • Chapter 10. AI model lifecycle management
  • Chapter 10. Guiding principles for successful AI projects
  • Chapter 10. How to set your AI project up for success
  • Chapter 10. Learning from successful and failed applications of AI
  • Chapter 10. Summary
  • Chapter 11. AI factory
  • Chapter 11. Adaptability
  • Chapter 11. Analogical reasoning and transferability
  • Chapter 11. Causality inference
  • Chapter 11. Contextual reasoning
  • Chapter 11. Data coherence
  • Chapter 11. Deployability and interoperability
  • Chapter 11. Effective data storage and processing
  • Chapter 11. Elimination of irrelevant attributes
  • Chapter 11. Explicability
  • Chapter 11. Feature engineering
  • Chapter 11. Human machine collaboration
  • Chapter 11. Lack of bias in data and algorithms
  • Chapter 11. Next-generation AI
  • Chapter 11. Personalization
  • Chapter 11. Prediction reliability
  • Chapter 11. Privacy
  • Chapter 11. Quality Assurance
  • Chapter 11. Resilience and robustness
  • Chapter 11. Sampling
  • Chapter 11. Scalability
  • Chapter 11. Security
  • Chapter 11. Summary
  • Chapter 11. Sustainable AI
  • Chapter 11. Technique combination
  • Chapter 11. Temporal reasoning
  • Chapter 11. Traceability and monitoring
  • Chapter 11. Unsupervised learning
  • epilogue
  • foreword
  • preface
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    تاریخ انتشار: 15 آبان 1403
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