|Book Title||CONCEPTS AND PROGRAMMING IN PyTorch|
|Date Published||Jun 20, 2018|
Author: Chitra Vasudevan
Edition: 2018 Paperback
Price: Rs. 249/-
The book has been written in such a way that the concepts are explained in detail, giving adequate emphasis on examples. To make clarity of the programming examples, logic is explained properly as well as discussed by using comments in the program itself. The book covers the topics right from the start of the software by using coding in software and writing programs into it.
The book features more on practical approach with more examples covering topics from simple to complex one addressing many of the core concepts and advanced topics also.
- Basics concepts of PyTorch like CNN architecture, RNN architecture are discussed in a detailed manner.
- The worked out case studies are also dealt in a detailed manner.
- Each and every chapter concludes with the observations of PyTorch to facilitate a better understanding of PyTorch
- Abundant worked out coding examples.
- Highly self-explanatory and user-friendly approach
This book will “need to have” title for various reasons as articulated below.
- Gaining Customers by adopting and implementing PyTorch in / projects/programs and in Research Departments.
- Help in sustaining Customer Relationships as the core of all successful working relationships are two essential characteristics: trust and commitment. To demonstrate their trustworthiness and commitment to customers, progressive suppliers periodically provide evidence to customers of their accomplishments.
- Help in delivering “Superior Value and Getting an Equitable Return” as an understanding value in business markets and doing business based on value delivered gives suppliers the means to get an equitable return for their efforts. The essence of customer value management is to deliver superior value and get an equitable return for it, both of which depend on the value of assessment.
- Introduction to PyTorch
- Linear Regression
- Convolution Neural Network (CNN)
- Recurrent Neural Networks (RNN)
- PyTorch Datasets