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Hands-On Deep Learning with TensorFlow.
This book is your guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data. About This Book* Expl...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Author; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Getting Started; Installing TensorFlow; TensorFlow
- main page; TensorFlow
- the installation page; Installing via pip; Installing via CoCalc; Simple computations; Defining scalars and tensors; Computations on tensors; Doing computation; Variable tensors; Viewing and substituting intermediate values; Logistic regression model building; Introducing the font classification dataset; Logistic regression; Getting data ready; Building a TensorFlow model.
- Logistic regression trainingDeveloping the loss function; Training the model; Evaluating the model accuracy; Summary; Chapter 2: Deep Neural Networks; Basic neural networks; Log function; Sigmoid function; Single hidden layer model; Exploring the single hidden layer model; Backpropagation; Single hidden layer explained; Understanding weights of the model; The multiple hidden layer model; Exploring the multiple hidden layer model; Results of the multiple hidden layer; Understanding the multiple hidden layers graph; Summary; Chapter 3: Convolutional Neural Networks.
- Convolutional layer motivationMultiple features extracted; Convolutional layer application; Exploring the convolution layer; Pooling layer motivation; Max pooling layers; Pooling layer application; Deep CNN; Adding convolutional and pooling layer combo; CNN to classify our fonts; Deeper CNN; Adding a layer to another layer of CNN; Wrapping up deep CNN; Summary; Chapter 4: Introducing Recurrent Neural Networks; Exploring RNNs; Modeling the weights; Understanding RNNs; TensorFlow learn; Setup; Logistic regression; DNNs; Convolutional Neural Networks (CNNs) in Learn; Extracting weights; Summary.
- Chapter 5: Wrapping UpResearch evaluation; A quick review of all the models; The logistic regression model; The single hidden layer neural network model; Deep neural network; Convolutional neural network; Deep convolutional neural network; The future of TensorFlow; Some more TensorFlow projects; Summary; Index.