PyTorch Recipes
A Problem-Solution Approach
Published on 28. January 2019
XX, 184 pages
978-1-4842-4258-2 (ISBN)
Description
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
Readers wanting to dive straight into programming PyTorch.
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
Readers wanting to dive straight into programming PyTorch.
More details
Series
Edition
First Edition
Language
English
Place of publication
Berkeley
United States
Publishing group
APRESS
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XX, 184 p. 280 illus.
File size
15,43 MB
ISBN-13
978-1-4842-4258-2 (9781484242582)
DOI
10.1007/978-1-4842-4258-2
Schweitzer Classification
Other editions
New editions
Pradeepta Mishra
PyTorch Recipes
A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
E-Book
12/2022
2nd Edition
APress
€62.99
Available for download
Additional editions
Book
01/2019
Apress
€40.65
Article exhausted; check for reprint
Person
Pradeepta Mishra is a data scientist and artificial intelligence researcher by profession, currently head of NLP, ML, and AI at Lymbyc, has expertise in designing artificial intelligence systems for performing tasks such as understanding natural language and giving recommendations based on natural language processing. He has filed two patents as an inventor, has written two books:
R Data Mining Blueprints
and
R: Mining Spatial, Text, Web, and Social Media Data
. There are two courses available on Udemy from his books. He has delivered a talk at the Global Data Science conference 2018, at Santa Clara, CA, USA on applications of bi-directional LSTM for time series forecasting. One of his books has been a recommended text at the HSLS Center, University of Pittsburgh, PA, USA. He has delivered a TEDx talk on 'Can Machines Think?', a session on the power of artificial intelligence in transforming different industries and changing job roles across industries. He has delivered 50+ tech talks on data science, machine learning, and artificial intelligence in various meet-ups, technical institutions, universities, and community arranged forums.
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.
