Deep Learning Quick Reference : Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras.

Mostrar otras versiones (1)

This book is a practical guide to applying deep neural networks including MLPs, CNNs, LSTMs, and more in Keras and TensorFlow. Packed with useful hacks to solve real-world challenges along with the supported math and theory around each topic, this book will be a quick reference for training and opti...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Bernico, Michael
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, 2018.
Temas:
Open source software > Library applications.
Logiciels libres dans les bibliothèques.
Open source software > Library applications
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover; Copyright and Credits; Dedication; Packt Upsell; Foreword; Contributors; Table of Contents; Preface; Chapter 1: The Building Blocks of Deep Learning; The deep neural network architectures; Neurons; The neuron linear function; Neuron activation functions; The loss and cost functions in deep learning; The forward propagation process; The back propagation function; Stochastic and minibatch gradient descents; Optimization algorithms for deep learning; Using momentum with gradient descent; The RMSProp algorithm; The Adam optimizer; Deep learning frameworks; What is TensorFlow?
  • What is Keras?Popular alternatives to TensorFlow; GPU requirements for TensorFlow and Keras; Installing Nvidia CUDA Toolkit and cuDNN; Installing Python; Installing TensorFlow and Keras; Building datasets for deep learning; Bias and variance errors in deep learning; The train, val, and test datasets; Managing bias and variance in deep neural networks; K-Fold cross-validation; Summary; Chapter 2: Using Deep Learning to Solve Regression Problems; Regression analysis and deep neural networks; Benefits of using a neural network for regression.
  • Drawbacks to consider when using a neural network for regressionUsing deep neural networks for regression; How to plan a machine learning problem; Defining our example problem; Loading the dataset; Defining our cost function; Building an MLP in Keras; Input layer shape; Hidden layer shape; Output layer shape; Neural network architecture; Training the Keras model; Measuring the performance of our model; Building a deep neural network in Keras; Measuring the deep neural network performance; Tuning the model hyperparameters; Saving and loading a trained Keras model; Summary.
  • Chapter 3: Monitoring Network Training Using TensorBoardA brief overview of TensorBoard; Setting up TensorBoard; Installing TensorBoard; How TensorBoard talks to Keras/TensorFlow; Running TensorBoard; Connecting Keras to TensorBoard; Introducing Keras callbacks; Creating a TensorBoard callback; Using TensorBoard; Visualizing training; Visualizing network graphs; Visualizing a broken network; Summary; Chapter 4: Using Deep Learning to Solve Binary Classification Problems; Binary classification and deep neural networks; Benefits of deep neural networks; Drawbacks of deep neural networks.
  • Case study â#x80;#x93; epileptic seizure recognitionDefining our dataset; Loading data; Model inputs and outputs; The cost function; Using metrics to assess the performance; Building a binary classifier in Keras; The input layer; The hidden layers; What happens if we use too many neurons?; What happens if we use too few neurons?; Choosing a hidden layer architecture; Coding the hidden layers for our example; The output layer; Putting it all together; Training our model; Using the checkpoint callback in Keras; Measuring ROC AUC in a custom callback; Measuring precision, recall, and f1-score; Summary.

Ejemplares similares