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PYTORCH RECIPES: A PROBLEM-SOLUTION APPROACH By Pradeepta Mishra

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eBay item number:226784262172

Item specifics

Condition
Like New: A book that has been read, but looks new. The book cover has no visible wear, and the dust ...
Book Title
PyTorch Recipes: A Problem-Solution Approach
ISBN-10
1484242572
ISBN
9781484242575

About this product

Product Identifiers

Publisher
Apress L. P.
ISBN-10
1484242572
ISBN-13
9781484242575
eBay Product ID (ePID)
16038723574

Product Key Features

Number of Pages
Xx, 184 Pages
Language
English
Publication Name
Pytorch Recipes : a Problem-Solution Approach
Subject
Industries / Computers & Information Technology, Neural Networks, Databases / General, Programming Languages / Python
Publication Year
2019
Type
Textbook
Subject Area
Computers, Business & Economics
Author
Pradeepta Mishra
Format
Trade Paperback

Dimensions

Item Weight
16 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
Chapter 1: Introduction PyTorch, Tensors, Tensor Operations and Basics.- Chapter 2: Probability distributions using PyTorch.- Chapter 3: Convolutional Neural Network and RNN using PyTorch.- Chapter 4: Introduction to Neural Networks, Tensor Differentiation .- Chapter 5: Supervised Learning using PyTorch.- Chapter 6: Fine Tuning Deep Learning Algorithms using PyTorch.- Chapter 7: NLP and Text Processing using PyTorch.-
Synopsis
Adopts a problem-solution approach to PyTorch programming Includes Deep Q Learning Algorithms with PyTorch Covers Natural Language Processing and Text processing, 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. What You Will Learn 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 Who This Book Is For Readers wanting to dive straight into programming PyTorch.
LC Classification Number
QA76.73.P98

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