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.

Machine Learning with Neural Networks

類似書籍推薦給您

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.