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| Der Titel des Buches | Neural Networks and Deep Learning: A Textbook |
| Veröffentlichungsdatum | |
| Sprache | Deutsch |
| ISBN-10 | 2791256554-TGV |
| Digital ISBN | 060-0679128155-NGY |
| Autor | Iris Werfel |
| Übersetzer | Udonna Faakhir |
| Seitenzahl | 971 Pages |
| Editor | Erwin Dahn |
| Datentyp | EPub PDF AMZ HWP WRD |
| Dateigröße | 5.61 MB |
| Dateinamen | Neural Networks and Deep Learning: A Textbook.pdf |
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Neural Networks and Deep Learning is a free online book. The book will teach you about: Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide the best solutions to many problems in image ...
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like ...
Overview. Welcome to Part 4 of Applied Deep Learning series. Part 1 was a hands-on introduction to Artificial Neural Networks, covering both the theory and application with a lot of code examples and visualization. In Part 2 we applied deep learning to real-world datasets, covering the 3 most commonly encountered problems as case studies: binary classification, multiclass classification and ...
Wide and deep neural networks, and neural networks with exotic wiring, are the Hot Thing right now in machine learning. But these networks didn't spring fully-formed into existence; their designers built up to them from smaller units. First, build a small network with a single hidden layer and verify that it works correctly. Then incrementally add additional model complexity, and verify that ...
Deep learning neural network models learn a mapping from input variables to an output variable. As such, the scale and distribution of the data drawn from the domain may be different for each variable. Input variables may have different units ( feet, kilometers, and hours) that, in turn, may mean the variables have different scales.
How to Choose Loss Functions When Training Deep Learning Neural Networks; We will define the optimizer as the efficient stochastic gradient descent algorithm “adam“. This is a popular version of gradient descent because it automatically tunes itself and gives good results in a wide range of problems. To learn more about the Adam version of stochastic gradient descent see the post: Gentle ...
As a final note, the understanding of RF in convolutional neural networks is an open research topic that will provide a lot of insights on why deep convolutional networks work so damn awesomely. Additional material. As an additional resource on the interpretation and visualization of RF, I would advise you to take a look at Kobayashi et al ...
It incorporates both machine learning and deep learning in a single course, covering topics like random forests, gradient boosting, test and validation sets, and p values, which previously were in a separate machine learning course. In addition, production and deployment are also covered, including material on developing a web-based GUI for our own deep learning powered apps. The only ...
My second theory-based deep learning (e)book recommendation is Neural Networks and Deep Learning by Michael Nielsen. The book does include some code but it’s important to underline the “some” — there are a total of seven Python scripts accompanying the book, all discussing a various fundamental machine learning, neural network, or deep learning technique on the MNIST dataset.
Linear algebra. As I mentioned, neural networks are essentially functions, which are trained using the tools of calculus. However, they are described with linear algebraic concepts like matrix multiplication.. Linear algebra is a vast subject with many essential aspects of machine learning, so this will be a significant segment.