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Modelling Financial Derivatives with MATHEMATICA ®
Modelling Financial Derivatives with MATHEMATICA ®

One of the most important tasks in finance is to find good mathematical models for financial products, in particular derivatives. However, the more realistic the model, the more practitioners face still-unsolved problems in rigorous mathematics and econometrics, in addition to serious numerical difficulties. The idea behind this book is to...

Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist
Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.

Beginning Data Science in R...
Computational Optimization, Methods and Algorithms (Studies in Computational Intelligence)
Computational Optimization, Methods and Algorithms (Studies in Computational Intelligence)

Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for...

Robustness and Complex Data Structures
Robustness and Complex Data Structures
Our journey towards this Festschrift started when realizing that our teacher, mentor, and friend Ursula Gather was going to celebrate her 60th birthday soon. As a researcher, lecturer, scientific advisor, board member, reviewer, editor, Ursula has had a wide impact...
Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB
Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable...
Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras
Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras

Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms.

Key Features

  • Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and...
Hands-On Data Science with Anaconda: Utilize the right mix of tools to create high-performance data science applications
Hands-On Data Science with Anaconda: Utilize the right mix of tools to create high-performance data science applications

Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda

Key Features

  • Use Anaconda to find solutions for clustering, classification, and linear regression
  • Analyze your data efficiently with the most powerful data...
Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems
Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems

Insightful projects to master deep learning and neural network architectures using Python and Keras

Key Features

  • Explore deep learning across computer vision, natural language processing (NLP), and image processing
  • Discover best practices for the training of deep neural...
Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala
Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.

Key Features

  • Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j
  • ...
Machine Learning with AWS: Explore the power of cloud services for your machine learning and artificial intelligence projects
Machine Learning with AWS: Explore the power of cloud services for your machine learning and artificial intelligence projects

Use artificial intelligence and machine learning on AWS to create engaging applications

Key Features

  • Explore popular AI and ML services with their underlying algorithms
  • Use the AWS environment to manage your AI workflow
  • Reinforce key concepts with hands-on...
Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library
Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library

Write modern natural language processing applications using deep learning algorithms and TensorFlow

Key Features

  • Focuses on more efficient natural language processing using TensorFlow
  • Covers NLP as a field in its own right to improve understanding for choosing TensorFlow...
Stochastic Approximation and Recursive Algorithms and Applications (Stochastic Modelling and Applied Probability) (v. 35)
Stochastic Approximation and Recursive Algorithms and Applications (Stochastic Modelling and Applied Probability) (v. 35)

This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. This second edition is a thorough revision, although the main features and structure remain unchanged. It contains many additional applications and results as...

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