MARC 닫기

• LDR

  05097cam a2200493Mi 4500

• 001

  000001333072

• 005

  20210114162028

• 006

  m d

• 007

  cr |n|---|||||

• 008

  190413s2019 enk o 000 0 eng d

• 020

  ▼a  1789533341

• 020

  ▼a  9781789533347
  ▼q  (electronic bk.)

• 035

  ▼a  2094778
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1096530010

• 040

  ▼a  EBLCP
  ▼b  eng
  ▼e  pn
  ▼c  EBLCP
  ▼d  UKAHL
  ▼d  CHVBK
  ▼d  OCLCQ
  ▼d  VT2
  ▼d  OCLCQ
  ▼d  N$T
  ▼d  248023

• 050

  ▼a  QA76.73.P98
  ▼b  .P875 2019

• 082

  ▼a  005.133
  ▼2  23

• 100

  ▼a  Purkait, Niloy.

• 245

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

• 260

  ▼a  Birmingham:
  ▼b  Packt Publishing Ltd,
  ▼c  2019.

• 300

  ▼a  1 online resource (450 pages).

• 336

  ▼a  text
  ▼b  txt
  ▼2  rdacontent

• 337

  ▼a  computer
  ▼b  c
  ▼2  rdamedia

• 338

  ▼a  online resource
  ▼b  cr
  ▼2  rdacarrier

• 500

  ▼a  Boston Housing Prices dataset

• 505

  ▼a  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

• 505

  ▼a  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

• 505

  ▼a  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

• 505

  ▼a  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

• 505

  ▼a  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

• 520

  ▼a  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 and industry level tools available in the market.

• 588

  ▼a  Print version record.

• 590

  ▼a  Added to collection customer.56279.3

• 650

  ▼a  Python.

• 650

  ▼a  Neural networks.

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Purkait, Niloy.
  ▼t  Hands-On Neural Networks with Keras : Design and Create Neural Networks Using Deep Learning and Artificial Intelligence Principles.
  ▼d  Birmingham : Packt Publishing Ltd, ©2019,
  ▼z  9781789536089

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2094778

• 938

  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  AH36147898

• 938

  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL5744459

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2094778

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T