Hands-On Neural Networks with Keras : Design and Create Neural Networks Using Deep Learning and Artificial Intelligence Principles.

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This book will intuitively build on the fundamentals of neural networks, deep learning and thoughtfully guide the readers through real-world use cases. You will learn to implement neural networks as well as how to develop and embed intelligence in products and services using the latest open source a...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Purkait, Niloy
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing Ltd, 2019.
Temas:
Python.
Neural networks.
Mathematical theory of computation.
Machine learning.
Neural networks & fuzzy systems.
Computers > Machine Theory.
Computers > Neural Networks.
Computers > Image Processing.
Artificial intelligence.
Neural networks (Computer science)
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro; Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Fundamentals of Neural Networks; Chapter 1: Overview of Neural Networks; Defining our goal; Knowing our tools; Keras; TensorFlow; The fundamentals of neural learning; What is a neural network?; Observing the brain; Building a biological brain; The physiology of a neuron; Representing information; The mysteries of neural encoding; Distributed representation and learning; The fundamentals of data science; Information theory; Entropy; Cross entropy; The nature of data processing
  • From data science to MLModeling data in high-dimensional spaces; The curse of dimensionality; Algorithmic computation and predictive models; Matching a model to use cases; Functional representations; The pitfalls of ML; Unbalanced class priors; Underfitting; Overfitting; Bad data; Irrelevant features and labels; Summary; Further reading; Chapter 2: A Deeper Dive into Neural Networks; From the biological to the artificial neuron
  • the perceptron; Building a perceptron; Input; Weights; Summation; Introducing non-linearity; Activation functions; Understanding the role of the bias term; Output
  • Learning through errorsThe mean squared error loss function; Training a perceptron; Quantifying loss; Loss as a function of model weights; Backpropagation; Computing the gradient; The learning rate; Scaling the perceptron; A single layered network; Experimenting with TensorFlow playground; Capturing patterns heirarchically; Steps ahead; Summary; Chapter 3: Signal Processing
  • Data Analysis with Neural Networks; Processing signals; Representational learning; Avoiding random memorization; Representing signals with numbers; Images as numbers; Feeding a neural network; Examples of tensors
  • Dimensionality of dataMaking some imports; Keras's sequential API; Loading the data; Checking the dimensions; Building a model; Introducting Keras layers; Initializing weights; Keras activations; Summarizing your model visually; Compiling the model; Fitting the model; Evaluating model performance; Regularization; Adjusting network size; Size experiments; Regularizing the weights; Using dropout layers; Thinking about dropout intuitively; Implementing weight regularization in Keras; Weight regularization experiments; Implementing dropout regularization in Keras
  • Dropout regularization experimentsComplexity and time; A summary of MNIST; Language processing; Sentiment analysis; The internet movie reviews dataset; Loading the dataset; Checking the shape and type; Plotting a single training instance; Decoding the reviews; Preparing the data; One-hot encoding; Vectorizing features; Vectorizing labels; Building a network; Compiling the model; Fitting the model; Validation data; Callbacks; Early stopping and history callbacks; Choosing a metric to monitor; Accessing model predictions; Probing the predictions; Summary of IMDB; Predicting continuous variables

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