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.
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.
