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Hands-On Neural Network Programming with C#

You're reading from  Hands-On Neural Network Programming with C#

Product type Book
Published in Sep 2018
Publisher Packt
ISBN-13 9781789612011
Pages 328 pages
Edition 1st Edition
Languages
Author (1):
Matt Cole Matt Cole
Profile icon Matt Cole

Table of Contents (16) Chapters

Preface 1. A Quick Refresher 2. Building Our First Neural Network Together 3. Decision Trees and Random Forests 4. Face and Motion Detection 5. Training CNNs Using ConvNetSharp 6. Training Autoencoders Using RNNSharp 7. Replacing Back Propagation with PSO 8. Function Optimizations: How and Why 9. Finding Optimal Parameters 10. Object Detection with TensorFlowSharp 11. Time Series Prediction and LSTM Using CNTK 12. GRUs Compared to LSTMs, RNNs, and Feedforward networks 13. Activation Function Timings
14. Function Optimization Reference 15. Other Books You May Enjoy

Working with Tensors

Let's set the stage by talking about exactly what a Tensor is. To do so, we should also talk a little bit about vectors and matrices as well. You can skip this section if you are already familiar, but it is short and if you already know about matrices and vectors, who knows, you might remember something you've forgotten! So go ahead and read it anyway!

Now, before we talk, let me show you a graphic that may make things a tad easier to visualize:

A vector is an array of numbers, as you can see here:

A matrix is a grid of n x m numbers, a two-dimensional array. We can do all kinds of neat operations on a matrix, such as addition and subtraction, so long as the sizes are compatible:

We can multiply matrices if we so desire, like this:

And matrices can be added together, like this:

In both cases, we are working within a two-dimensional space. So...

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