عنوان

PyTorch recipes :

(Number (ISBN

1484242580

(Number (ISBN

1484242599

(Number (ISBN

9781484242582

(Number (ISBN

9781484242599

Erroneous ISBN

1484242572

Erroneous ISBN

9781484242575

Title Proper

PyTorch recipes :

General Material Designation

[Book]

Other Title Information

a problem-solution approach /

First Statement of Responsibility

Pradeepta Mishra.

Place of Publication, Distribution, etc.

[California] :

Name of Publisher, Distributor, etc.

Apress,

Date of Publication, Distribution, etc.

[2019]

Date of Publication, Distribution, etc.

©2019

Specific Material Designation and Extent of Item

1 online resource

Text of Note

Includes index.

Text of Note

Introduction to PyTorch, Tensors, and Tensor operations -- Probability distributions using PyTorch -- CNN and RNN using PyTorch -- Introduction to neural networks using PyTorch -- Supervised learning using PyTorch -- Fine-tuning deep learning models using PyTorch -- Natural language processing using PyTorch.

0

Text of Note

Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into transformations and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them. Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you will get acquainted with natural language processing and text processing using PyTorch. You will: Master tensor operations for dynamic graph-based calculations using PyTorch Create PyTorch transformations and graph computations for neural networks Carry out supervised and unsupervised learning using PyTorch Work with deep learning algorithms such as CNN and RNN Build LSTM models in PyTorch Use PyTorch for text processing.

Source for Acquisition/Subscription Address

OverDrive, Inc.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

8D5A646D-30CC-4EB4-96E5-97EA59625C01

Stock Number

com.springer.onix.9781484242582

International Standard Book Number

9781484242575

Machine learning.

Neural networks (Computer science)

Python (Computer program language)

COMPUTERS-- General.

Machine learning.

Neural networks (Computer science)

Python (Computer program language)

COM-- 000000

UMX

UMX

Number

Edition

Class number

Mishra, Pradeepta

Date of Transaction

20200823032644.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y