Python Programs For Astronomical Solutions


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Python Programs for Astronomical Solutions


Python Programs for Astronomical Solutions

Author: Manohar Narayan Purohit

language: en

Publisher: Notion Press

Release Date: 2020-11-26


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This book gives ready-made scripts of Python coding for the solution to all practical problems in Astronomy such as finding Planetary positions at any instant of time on any date, Detailed calculation of lunar and solar eclipses, past or future, with a production of visual simulations like videos, pictures and maps. It gives insight into the technics of Python-programming and in-depth knowledge of Astronomical calculations. It is a must for every astronomical enthusiast and students of computer programming.

A Primer on Scientific Programming with Python


A Primer on Scientific Programming with Python

Author: Hans Petter Langtangen

language: en

Publisher: Springer

Release Date: 2014-08-01


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The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science. From the reviews: Langtangen ... does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. ... Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011 This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science. Alex Small, IEEE, CiSE Vol. 14 (2), March /April 2012

Ultimate Neural Network Programming with Python


Ultimate Neural Network Programming with Python

Author: Vishal Rajput

language: en

Publisher: Orange Education Pvt Ltd

Release Date: 2023-11-04


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Master Neural Networks for Building Modern AI Systems. KEY FEATURES ● Comprehensive Coverage of Foundational AI Concepts and Theories. ● In-Depth Exploration of Maths Behind Neural Network Mathematics. ● Effective Strategies for Structuring Deep Learning Code. ● Real-World Applications of AI Principles and Techniques. DESCRIPTION This book is a practical guide to the world of Artificial Intelligence (AI), unraveling the math and principles behind applications like Google Maps and Amazon. The book starts with an introduction to Python and AI, demystifies complex AI math, teaches you to implement AI concepts, and explores high-level AI libraries. Throughout the chapters, readers are engaged with the book through practice exercises, and supplementary learnings. The book then gradually moves to Neural Networks with Python before diving into constructing ANN models and real-world AI applications. It accommodates various learning styles, letting readers focus on hands-on implementation or mathematical understanding. This book isn't just about using AI tools; it's a compass in the world of AI resources, empowering readers to modify and create tools for complex AI systems. It ensures a journey of exploration, experimentation, and proficiency in AI, equipping readers with the skills needed to excel in the AI industry. WHAT WILL YOU LEARN ● Leverage TensorFlow and Keras while building the foundation for creating AI pipelines. ● Explore advanced AI concepts, including dimensionality reduction, unsupervised learning, and optimization techniques. ● Master the intricacies of neural network construction from the ground up. ● Dive deeper into neural network development, covering derivatives, backpropagation, and optimization strategies. ● Harness the power of high-level AI libraries to develop production-ready code, allowing you to accelerate the development of AI applications. ● Stay up-to-date with the latest breakthroughs and advancements in the dynamic field of artificial intelligence. WHO IS THIS BOOK FOR? This book serves as an ideal guide for software engineers eager to explore AI, offering a detailed exploration and practical application of AI concepts using Python. AI researchers will find this book enlightening, providing clear insights into the mathematical concepts underlying AI algorithms and aiding in writing production-level code. This book is designed to enhance your skills and knowledge to create sophisticated, AI-powered solutions and advance in the multifaceted field of AI. TABLE OF CONTENTS 1. Understanding AI History 2. Setting up Python Workflow for AI Development 3. Python Libraries for Data Scientists 4. Foundational Concepts for Effective Neural Network Training 5. Dimensionality Reduction, Unsupervised Learning and Optimizations 6. Building Deep Neural Networks from Scratch 7. Derivatives, Backpropagation, and Optimizers 8. Understanding Convolution and CNN Architectures 9. Understanding the Basics of TensorFlow and Keras 10. Building End-to-end Image Segmentation Pipeline 11. Latest Advancements in AI Index