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Beschreibung
To truly understand the power of deel learning, you need to grasp the mathematical concepts that make it tick. "Math for deep learning" will give you a working knowledge of probability, statistics, linear algebra, and differential calculus-- the essential math subfields required to practice deep learning successfully. Each subfield is explained with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. The book begins with fundamentals such as Bayes' theorem before progressing to more advanced concepts like training neural networks using vectors, matrices, and derivatives of functions. You'll then put all this math to use as you explore and implement backpropagation and gradient descent-- the foundational algorithms that have enabled the AI revolution.
To truly understand the power of deel learning, you need to grasp the mathematical concepts that make it tick. "Math for deep learning" will give you a working knowledge of probability, statistics, linear algebra, and differential calculus-- the essential math subfields required to practice deep learning successfully. Each subfield is explained with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. The book begins with fundamentals such as Bayes' theorem before progressing to more advanced concepts like training neural networks using vectors, matrices, and derivatives of functions. You'll then put all this math to use as you explore and implement backpropagation and gradient descent-- the foundational algorithms that have enabled the AI revolution.
Über den Autor
Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder. He has over 20 years of machine learning industry experience. Kneusel is also the author of Numbers and Computers (2nd ed., Springer 2017), Random Numbers and Computers (Springer 2018), and Practical Deep Learning: A Python-Based Introduction (No Starch Press 2021).
Inhaltsverzeichnis
Introduction
Chapter 1: Setting the Stage
Chapter 2: Probability
Chapter 3: More Probability
Chapter 4: Statistics
Chapter 5: Linear Algebra
Chapter 6: More Linear Algebra
Chapter 7: Differential Calculus
Chapter 8: Matrix Calculus
Chapter 9: Data Flow in Neural Networks
Chapter 10: Backpropagation
Chapter 11: Gradient Descent
Appendix: Going Further
Chapter 1: Setting the Stage
Chapter 2: Probability
Chapter 3: More Probability
Chapter 4: Statistics
Chapter 5: Linear Algebra
Chapter 6: More Linear Algebra
Chapter 7: Differential Calculus
Chapter 8: Matrix Calculus
Chapter 9: Data Flow in Neural Networks
Chapter 10: Backpropagation
Chapter 11: Gradient Descent
Appendix: Going Further
Details
| Erscheinungsjahr: | 2021 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | Einband - flex.(Paperback) |
| ISBN-13: | 9781718501904 |
| ISBN-10: | 1718501900 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Kneusel, Ronald T. |
| Hersteller: |
Random House LLC US
No Starch Press |
| Verantwortliche Person für die EU: | Springer Fachmedien Wiesbaden GmbH, Postfach:15 46, D-65189 Wiesbaden, info@bod.de |
| Maße: | 232 x 177 x 24 mm |
| Von/Mit: | Ronald T. Kneusel |
| Erscheinungsdatum: | 07.12.2021 |
| Gewicht: | 0,636 kg |
Über den Autor
Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder. He has over 20 years of machine learning industry experience. Kneusel is also the author of Numbers and Computers (2nd ed., Springer 2017), Random Numbers and Computers (Springer 2018), and Practical Deep Learning: A Python-Based Introduction (No Starch Press 2021).
Inhaltsverzeichnis
Introduction
Chapter 1: Setting the Stage
Chapter 2: Probability
Chapter 3: More Probability
Chapter 4: Statistics
Chapter 5: Linear Algebra
Chapter 6: More Linear Algebra
Chapter 7: Differential Calculus
Chapter 8: Matrix Calculus
Chapter 9: Data Flow in Neural Networks
Chapter 10: Backpropagation
Chapter 11: Gradient Descent
Appendix: Going Further
Chapter 1: Setting the Stage
Chapter 2: Probability
Chapter 3: More Probability
Chapter 4: Statistics
Chapter 5: Linear Algebra
Chapter 6: More Linear Algebra
Chapter 7: Differential Calculus
Chapter 8: Matrix Calculus
Chapter 9: Data Flow in Neural Networks
Chapter 10: Backpropagation
Chapter 11: Gradient Descent
Appendix: Going Further
Details
| Erscheinungsjahr: | 2021 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | Einband - flex.(Paperback) |
| ISBN-13: | 9781718501904 |
| ISBN-10: | 1718501900 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Kneusel, Ronald T. |
| Hersteller: |
Random House LLC US
No Starch Press |
| Verantwortliche Person für die EU: | Springer Fachmedien Wiesbaden GmbH, Postfach:15 46, D-65189 Wiesbaden, info@bod.de |
| Maße: | 232 x 177 x 24 mm |
| Von/Mit: | Ronald T. Kneusel |
| Erscheinungsdatum: | 07.12.2021 |
| Gewicht: | 0,636 kg |
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