에너지 공정혁신을 위한 기계학습
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출판사 리뷰
출판사 리뷰
목차
목차
Chapter 1. Linear Algebra 3
Chapter 2. Probability Theory 23
Chapter 3. Vector Calculus 55
Chapter 4. Course Introduction 73
Chapter 5. Preliminaries 83
Chapter 6. Linear Neural Networks for Regression 103
Chapter 7. Linear Neural Networks for Classifications 123
Chapter 8. Multilayer Perceptrons 137
Chapter 9. Builders' Guide 159
Chapter 10. Optimization 175
Chapter 11. Convolutional Neural Networks 213
Chapter 12. Recurrent Neural Networks 249
Chapter 13. Overview, Application, and Evaluation of Generative Model(GM) 345
Chapter 14. Variational Autoencoder 373
Chapter 15. Diffusion Model 395
Chapter 16. Diffusion Model for Text to Image 415
Chapter 2. Probability Theory 23
Chapter 3. Vector Calculus 55
Chapter 4. Course Introduction 73
Chapter 5. Preliminaries 83
Chapter 6. Linear Neural Networks for Regression 103
Chapter 7. Linear Neural Networks for Classifications 123
Chapter 8. Multilayer Perceptrons 137
Chapter 9. Builders' Guide 159
Chapter 10. Optimization 175
Chapter 11. Convolutional Neural Networks 213
Chapter 12. Recurrent Neural Networks 249
Chapter 13. Overview, Application, and Evaluation of Generative Model(GM) 345
Chapter 14. Variational Autoencoder 373
Chapter 15. Diffusion Model 395
Chapter 16. Diffusion Model for Text to Image 415
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