The best thing about the content is that it has and will always be extended, meaning the information will stay up-to-date. Each chapter also gives a profound insight into the paradigm of neural networks, including LATEX.
The authors focus on supervised learning (prediction) and unsupervised learning, covering topics like neural networks, classification trees, support vector machines, and boosting the first comprehensive treatment.
This book introduces the reader to the basic math used for neural network calculation. This book assumes the reader has only knowledge of college algebra and computer programming. This book begins by showing how to calculate output of a neural network and moves on to more advanced training methods such as backpropagation, resilient propagation and Levenberg Marquardt optimization. The mathematics needed by these techniques is also introduced. Mathematical topics covered by this book include first, second, Hessian matrices, gradient descent and partial derivatives. All mathematical notation introduced is explained. Neural networks covered include the feedforward neural network and the self organizing map. This book provides an ideal supplement to our other neural books. This book is ideal for the reader, without a formal mathematical background, that seeks a more mathematical description of neural networks.
TensorFlow is a free and open-source software library for machine learning. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.This book is a somewhat intermediate-level introduction to Tensorflow 2. We will eventually cover everything tf.keras, but no so fast until we imple...
Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow is the machine learning library of choice for ...
Description:Text and illustrations should be memorable and easy to understand to offer as many people as possible access to the field of neural networks. The chapters are individually accessible to readers with little previous knowledge.
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
Gain a fundamental understanding of neural networks, before tackling deep neural networks, convolutional neural networks, and recurrent neural networks. Learn tips and tricks for dealing with commonly encountered issues such as overfitting and underfitting, dropout and weight regularization, and discover how to apply machine learning to autoencoders.
Part III focuses on dense neural networks (DNNs) and recurrent neural networks (RNNs) as two standard types of neural networks. The charm of DNNs lies in the fact that they are good universal approximators. The examples in the book for reinforcement learning, for instance, make use of DNNs to approximate the optimal action policy. On the other hand, RNNs are specifically designed to handle sequential data, such as time series data. This is helpful when trying, for example, to predict future values of financial time series.
However, convolutional neural networks (CNNs) are another standard type of neural network that is widely used in practice. They have been particularly successful, among other domains, in computer vision. CNNs were able to set new benchmarks in a number of standard tests and challenges, such as the ImageNet Challenge; for more on this, see The Economist (2016) or Gerrish (2018). Computer vision in turn is important in such domains as autonomous vehicles or security and surveillance. 2b1af7f3a8