Cart
Sign In
Fast Delivery
Multiple courier options
Rs.879
Rs. 1,099
20% OFF
Inclusive all taxes
This Product is out of stock
Description
To construct a system that may be referred to as having ?Artificial Intelligence,? it is important to develop the capacity to design algorithms capable of performing data-based automated decision-making in conditions of uncertainty. Now, to accomplish this goal, one needs to have an in-depth understanding of the more sophisticated components of linear algebra, vector calculus, probability, and statistics. This book walks you through every mathematical algorithm, as well as its architecture, its operation, and its design. This book will teach you the common terminologies used in artificial intelligence such as models, data, parameters of models, and dependent and independent variables. The Bayesian linear regression, the Gaussian mixture model, the stochastic gradient descent, and the backpropagation algorithms are explored with implementation beginning from scratch. The vast majority of the sophisticated mathematics required for complicated AI computations such as autoregressive models, cycle GANs, and CNN optimization are explained and compared.
Additional Info
| Author | Tamoghna Ghosh, Shravan Kumar Belagal Math |
| Publisher | BPB Publications |
| Language | English |
| Binding Type | Paper Back |
| Main Category | Science & Mathematics |
| Sub Category | Mathematics |
| ISBN13 | 9789355511935 |
| SKU | BK 0179129 |
You may also be interested in
Let Us C : Authentic Guide To C Programming Language (21st Edition)
Yashavant Kanetkar
Rs. 399
Rs. 339
15% OFF
Problem Solving And Programming In C : A Computational Thinking Approach
S. Sridhar
Rs. 699
Rs. 559
20% OFF
Replacement
Within 15 Days
Secure Payment
100% Secure Payment
24/7 Support
Within 1 Business Day
