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With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning.
You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.
In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning.
You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.
In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
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
| Erscheinungsjahr: | 2021 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 316 |
| ISBN-13: | 9781718501904 |
| ISBN-10: | 1718501900 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Kneusel, Ronald T. |
| Hersteller: |
Random House LLC US
No Starch Press |
| Maße: | 232 x 177 x 24 mm |
| Von/Mit: | Ronald T. Kneusel |
| Erscheinungsdatum: | 07.12.2021 |
| Gewicht: | 0,636 kg |
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
| Erscheinungsjahr: | 2021 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 316 |
| ISBN-13: | 9781718501904 |
| ISBN-10: | 1718501900 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Kneusel, Ronald T. |
| Hersteller: |
Random House LLC US
No Starch Press |
| Maße: | 232 x 177 x 24 mm |
| Von/Mit: | Ronald T. Kneusel |
| Erscheinungsdatum: | 07.12.2021 |
| Gewicht: | 0,636 kg |
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