Python For Scientific Computing And Artificial Intelligence
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Python for Scientific Computation and Artificial Intelligence
"Python for Scientific Computation and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI). This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling. Features: No prior experience of programming is required. Online GitHub repository available with codes for readers to practice. Covers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computing. Full solutions to exercises are available as Jupyter notebooks on the Web"--
Python for Artificial Intelligence and Data Science
Author: Mr.G.Hubert
language: en
Publisher: SK Research Group of Companies
Release Date: 2024-09-10
Mr.G.Hubert, Assistant Professor & Head, Department of Artificial Intelligence, S.I.V.E.T. College, Chennai, Tamil Nadu, India. Dr.Sowmya Naik.P.T, Professor & Head, Department of Computer Science and Engineering, City Engineering College, Bengaluru, Karnataka, India. Dr.Ambika.P.R, Professor, Department of Computer Science and Engineering, City Engineering College, Bengaluru, Karnataka, India. Mrs.Laxmi.M.C, Assistant Professor, Department of Computer Science and Engineering, City Engineering College, Bengaluru, Karnataka, India.
Numerical Python
Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.