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Deep Learning Fundamentals
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A practical deep learning book covering neural networks, training, model evaluation, CNNs, RNNs, transformers, deployment, and MLOps fundamentals.
Level: beginnerLanguage: EN EnglishRating: 0Reviews: 0
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01Chapter 1: Introduction To Deep Learning02Chapter 2: Mathematical Foundations03Chapter 3: Python For Deep Learning04Chapter 4: Neural Networks Basics05Chapter 5: Training Neural Networks06Chapter 6: Improving Neural Networks07Chapter 7: Deep Feedforward Networks Dnns08Chapter 7: Deep Feedforward Networks09Chapter 8: Convolutional Neural Networks Cnns10Chapter 8: Convolutional Neural Networks11Chapter 9: Recurrent Neural Networks Rnns12Chapter 9: Recurrent Neural Networks13Chapter 10: Deep Learning Frameworks14Chapter 11: Data Preparation And Pipelines15Chapter 11: Data Preparation Pipelines16Chapter 12: Model Evaluation17Chapter 13: Transfer Learning18Chapter 14: Generative Models19Chapter 15: Natural Language Processing Nlp20Chapter 15: Natural Language Processing21Chapter 16: Attention And Transformers22Chapter 16: Attention Transformers23Chapter 17: Model Deployment24Chapter 17: Model Deployment25Chapter 18: Performance Optimization26Chapter 18: Performance Optimization27Chapter 19: Mlops Fundamentals28Chapter 19: Mlops Fundamentals29Chapter 20: Deep Learning Trends30Appendix E: Deeper Practice and Extra Examples
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