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Opening Credits
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2 preface
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3 Introduction to generative ai
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4 History of generative ai
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5 Types of generative models
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6 Types of generative models variational autoencoders (vaes)
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7 Types of generative models generative adversarial networks (gans)
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8 Types of generative models other generative models
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9 Training and fine tuning generative models
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10 Detailed math behind generative ai
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11 detailed math behind generative ai probability distributions
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12 detailed math behind generative ai linear algebra in generative models
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13 detailed math behind generative ai optimization techniques
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14 detailed math behind generative ai advanced mathematics in gans
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15 research papers behind generative ai
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16 research papers behind generative ai gans: generative adversarial networks
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17 research papers behind generative ai vaes: variational autoencoders
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18 research papers behind generative ai recent breakthroughs
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19 generative ai in industry
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20 generative ai tools and frameworks
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21 generative ai tools and frameworks tensorflow for generative ai
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22 generative ai tools and frameworks pytorch for generative ai
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23 generative ai tools and frameworks other tools and libraries
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24 building a generative ai project
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25 applications of generative ai
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26 generative ai in art and creativity
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27 generative ai and human collaboration
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28 Challenges and ethical considerations
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29 Future trends in generative ai
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30 Conclusion
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31 glossary
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32 bibliography
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Ending Credits
1/13/2024