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The ISSAC meeting is a showcase for original research contributions on all aspects of computer algebra and symbolic mathematical computation, including: Algorithmic aspects: •Exact and symbolic linear, polynomial and differential algebra •Symbolic-numeric, homotopy, perturbation and series methods •Computational algebraic geometry, group theory and number theory, quantifier elimination and logic •Computer arithmetic •Summation, recurrence equations, integration, solution of ODEs & PDEs •Symbolic methods in other areas of pure and applied mathematics •Complexity of algebraic algorithms and algebraic complexity Software aspects: •Design of symbolic computation packages and systems •Language design and type systems for symbolic computation •Data representation •Considerations for modern hardware •Algorithm implementation and performance tuning •Mathematical user interfaces •Use with systems for, e.g., digital libraries, course-ware, simulation and optimization, automated theoremproving, computer-aided design, and automatic differentiation Application aspects: •Applications that stretch the current limits of computer algebra algorithms or systems, use computer algebra in new areas or new ways, or apply it in situations with broad impact.

Computer algorithms.

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Computer algorithms

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Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications Book Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learn Understand the characteristics of a machine learning algorithm Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains Learn how regression works in time-series analysis and risk prediction Create, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANs Who this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.

Machine learning. --- Computer algorithms.

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The conference proceedings include 18 selected papers contributed from universities, research institutions, and industries. Each paper had been peer-reviewed by organizing and technical committee members as well as invited experts in related fields. The papers cover the research areas of intelligent calculations and algorithms, software and information security, and computer theory and information engineering. These valuable and insightful papers will guide the community to a better understanding of the Algorithms, Computing and Systems, and of the challenges our community should focus and collaborate on in the near future.

Electronic data processing --- Computer algorithms

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Flow charts. --- Computer programming. --- Computer algorithms.