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• LDR

  04809cam a2200445Mu 4500

• 001

  000000534914

• 005

  20210114163640

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  m d

• 007

  cr cnu---unuuu

• 008

  200808s2020 xx o ||| 0 eng d

• 020

  ▼a  1838981845

• 020

  ▼a  9781838981846
  ▼q  (electronic bk.)

• 035

  ▼a  2532423
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1181838226

• 040

  ▼a  EBLCP
  ▼b  eng
  ▼c  EBLCP
  ▼d  N$T
  ▼d  248023

• 050

  ▼a  Q325.5

• 082

  ▼a  006.31
  ▼2  23

• 100

  ▼a  Saleh, Hyatt.

• 245

  ▼a  The the Deep Learning with Pytorch Workshop
  ▼h  [electronic resource]:
  ▼b  Build Deep Neural Networks and Artificial Intelligence Applications with Pytorch.

• 260

  ▼a  Birmingham:
  ▼b  Packt Publishing, Limited,
  ▼c  2020.

• 300

  ▼a  1 online resource (329 p.).

• 500

  ▼a  Description based upon print version of record.

• 500

  ▼a  Exercise 4.02: Calculating the Output Shape of a Set of Convolutional and Pooling Layers

• 505

  ▼a  Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Deep Learning and PyTorch -- Introduction -- Why Deep Learning? -- Applications of Deep Learning -- Introduction to PyTorch -- GPUs in PyTorch -- What Are Tensors? -- Exercise 1.01: Creating Tensors of Different Ranks Using PyTorch -- Advantages of Using PyTorch -- Disadvantages of Using PyTorch -- Key Elements of PyTorch -- The PyTorch autograd Library -- The PyTorch nn Module -- Exercise 1.02: Defining a Single-Layer Architecture -- The PyTorch optim Package -- Exercise 1.03: Training a Neural Network

• 505

  ▼a  Activity 1.01: Creating a Single-Layer Neural Network -- Summary -- Chapter 2: Building Blocks of Neural Networks -- Introduction -- Introduction to Neural Networks -- What Are Neural Networks? -- Exercise 2.01: Performing the Calculations of a Perceptron -- Multi-Layer Perceptron -- The Learning Process of a Neural Network -- Forward Propagation -- The Calculation of Loss Functions -- Backward Propagation -- Gradient Descent -- Advantages and Disadvantages -- Advantages -- Disadvantages -- Introduction to Artificial Neural Networks -- Introduction to Convolutional Neural Networks

• 505

  ▼a  Introduction to Recurrent Neural Networks -- Data Preparation -- Dealing with Messy Data -- Exercise 2.02: Dealing with Messy Data -- Data Rescaling -- Exercise 2.03: Rescaling Data -- Splitting the Data -- Exercise 2.04: Splitting a Dataset -- Disadvantages of Failing to Prepare Your Data -- Activity 2.01: Performing Data Preparation -- Building a Deep Neural Network -- Exercise 2.05: Building a Deep Neural Network Using PyTorch -- Activity 2.02: Developing a Deep Learning Solution for a Regression Problem -- Summary -- Chapter 3: A Classification Problem Using DNN -- Introduction

• 505

  ▼a  Problem Definition -- Deep Learning in Banking -- Exploring the Dataset -- Data Preparation -- Building the Model -- ANNs for Classification Tasks -- A Good Architecture -- PyTorch Custom Modules -- Exercise 3.01: Defining a Model's Architecture Using Custom Modules -- Defining the Loss Function and Training the Model -- Activity 3.01: Building an ANN -- Dealing with an Underfitted or Overfitted Model -- Error Analysis -- Exercise 3.02: Performing Error Analysis -- Activity 3.02: Improving a Model's Performance -- Deploying Your Model -- Saving and Loading Your Model

• 505

  ▼a  PyTorch for Production in C++ -- Building an API -- Exercise 3.03: Creating a Web API -- Activity 3.03: Making Use of Your Model -- Summary -- Chapter 4: Convolutional Neural Networks -- Introduction -- Building a CNN -- Why Are CNNs Used for Image Processing? -- The Image as Input -- Applications of CNNs -- Classification -- Localization -- Detection -- Segmentation -- The Building Blocks of CNNs -- Convolutional Layers -- Exercise 4.01: Calculating the Output Shape of a Convolutional Layer -- Pooling Layers

• 520

  ▼a  With this hands-on, self-paced guide, you'll explore crucial deep learning topics and discover the structure and syntax of PyTorch. Challenging activities and interactive exercises will keep you motivated and encourage you to build intelligent applications effectively.

• 590

  ▼a  Master record variable field(s) change: 050, 082, 650

• 650

  ▼a  Machine learning.

• 650

  ▼a  Python (Computer program language)

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Saleh, Hyatt
  ▼t  The the Deep Learning with Pytorch Workshop : Build Deep Neural Networks and Artificial Intelligence Applications with Pytorch.
  ▼d  Birmingham : Packt Publishing, Limited,c2020

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2532423

• 938

  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL6269368

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2532423

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T