AN INTRODUCTION TO CONTROL SYSTEMS (2版)
相關熱銷的書籍推薦給您
This significantly revised edition presents a broad introduction to Control Systems and balances new, modern methods with the more classical. It is an excellent text for use as a first course in Control Systems by undergraduate students in all branches of engineering and applied mathematics. The book contains: A comprehensive coverage of automatic control, integrating digital and computer control techniques and their implementations, the practical issues and problems in Control System design; the three-term PID controller, the most widely used controller in industry today; numerous in-chapter worked examples and end-of-chapter exercises. This second edition also includes an introductory guide to some more recent developments, namely fuzzy logic control and neural networks.
立即查看
Machine Learning with Python: Theory and Applications
類似書籍推薦給您
Machine Learning (ML) has become a very important area of research widely used in various industries.
This compendium introduces the basic concepts, fundamental theories, essential computational techniques, codes, and applications related to ML models. With a strong foundation, one can comfortably learn related topics, methods, and algorithms. Most importantly, readers with strong fundamentals can even develop innovative and more effective machine models for his/her problems. The book is written to achieve this goal.
The useful reference text benefits professionals, academics, researchers, graduate and undergraduate students in AI, ML and neural networks.
Request Inspection Copy
Sample Chapter(s)
Chapter 1: Introduction
Contents:
Introduction
Basics of Python
Basic Mathematical Computations
Statistics and Probability-based Learning Model
Prediction Function and Universal Prediction Theory
The Perceptrons and SVM
Activation Functions and Universal Approximation Theory
Automatic Differentiation and Autograd
Solution Existence Theory and Optimization Techniques
Loss Functions for Regression
Loss Functions and Models for Classification
Multiclass Classification
Multilayer Perceptron (MLP) for Regression and Classification
Overfitting and Regularization
Convolutional Neutral Network (CNN) for Classification and Object Detection
Recurrent Neural Network (RNN)and Sequence Feature Models
Unsupervised Learning Techniques
Reinforcement Learning (RL)
Readership: Researchers, professionals, academics, undergraduate and graduate students in AI and machine learning.
立即查看
華通書坊
