Book Image

The TensorFlow Workshop

By : Matthew Moocarme, Abhranshu Bagchi, Anthony So, Anthony Maddalone
Book Image

The TensorFlow Workshop

By: Matthew Moocarme, Abhranshu Bagchi, Anthony So, Anthony Maddalone

Overview of this book

Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running. You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.
Table of Contents (13 chapters)
Preface

Tensor Multiplication

Tensor multiplication is another fundamental operation that is used frequently in the process of building and training ANNs since information propagates through the network from the inputs to the result via a series of additions and multiplications. While the rules for addition are simple and intuitive, the rules for tensors are more complex. Tensor multiplication involves more than simple element-wise multiplication of the elements. Rather, a more complicated procedure is implemented that involves the dot product between the entire rows/columns of each of the tensors to calculate each element of the resulting tensor. This section will explain how multiplication works for two-dimensional tensors or matrices. However, tensors of higher orders can also be multiplied.

Given a matrix, X = [xij]m x n, and another matrix, Y = [yij]n x p, the product of the two matrices is Z = XY = [zij]m x p, and each element, zij, is defined element-wise as Formula. The shape of the resultant...