"Deep Learning with Python" - Information and Links:

Deep Learning with Python - Info and Reading Options

"Deep Learning with Python" was published by Manning Publications Co. LLC in 2017 - New York, it has 384 pages and the language of the book is English.


“Deep Learning with Python” Metadata:

  • Title: Deep Learning with Python
  • Author:
  • Language: English
  • Number of Pages: 384
  • Publisher: Manning Publications Co. LLC
  • Publish Date:
  • Publish Location: New York

“Deep Learning with Python” Subjects and Themes:

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"Deep Learning with Python" Description:

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Intro -- Deep Learning with Python -- François Chollet -- Copyright -- Brief Table of Contents -- Table of Contents -- Preface -- Acknowledgments -- About this Book -- Who should read this book -- Roadmap -- Software/hardware requirements -- Source code -- Book forum -- About the Author -- About the Cover -- Part 1. Fundamentals of deep learning -- Chapter 1. What is deep learning? -- 1.1. Artificial intelligence, machine learning, and deep learning -- 1.1.1. Artificial intelligence -- 1.1.2. Machine learning -- 1.1.3. Learning representations from data -- 1.1.4. The "deep" in deep learning -- 1.1.5. Understanding how deep learning works, in three figures -- 1.1.6. What deep learning has achieved so far -- 1.1.7. Don't believe the short-term hype -- 1.1.8. The promise of AI -- 1.2. Before deep learning: a brief history of machine learning -- 1.2.1. Probabilistic modeling -- 1.2.2. Early neural networks -- 1.2.3. Kernel methods -- 1.2.4. Decision trees, random forests, and gradient boosting machines -- 1.2.5. Back to neural networks -- 1.2.6. What makes deep learning different -- 1.2.7. The modern machine-learning landscape -- 1.3. Why deep learning? Why now? -- 1.3.1. Hardware -- 1.3.2. Data -- 1.3.3. Algorithms -- 1.3.4. A new wave of investment -- 1.3.5. The democratization of deep learning -- 1.3.6. Will it last? -- Chapter 2. Before we begin: the mathematical building blocks of neural networks -- 2.1. A first look at a neural network -- 2.2. Data representations for neural networks -- 2.2.1. Scalars (0D tensors) -- 2.2.2. Vectors (1D tensors) -- 2.2.3. Matrices (2D tensors) -- 2.2.4. 3D tensors and higher-dimensional tensors -- 2.2.5. Key attributes -- 2.2.6. Manipulating tensors in Numpy -- 2.2.7. The notion of data batches -- 2.2.8. Real-world examples of data tensors -- 2.2.9. Vector data -- 2.2.10. Timeseries data or sequence data

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