A HANDBOOK OF MATHEMATICAL MODELS WITH PYTHON elevate your machine learning projects with Networkx, PuLP, and linalg

Book Cover
Average Rating
Author
Sarkar, Ranja,
Published
Birmingham, UK : Packt Publishing Ltd., 2023.
Status
Available Online

Description

Master the art of mathematical modeling through practical examples, use cases, and machine learning techniques Key Features Gain a profound understanding of various mathematical models that can be integrated with machine learning Learn how to implement optimization algorithms to tune machine learning models Build optimal solutions for practical use cases Purchase of the print or Kindle book includes a free PDF eBook Book Description Mathematical modeling is the art of transforming a business problem into a well-defined mathematical formulation. Its emphasis on interpretability is particularly crucial when deploying a model to support high-stake decisions in sensitive sectors like pharmaceuticals and healthcare. Through this book, you'll gain a firm grasp of the foundational mathematics underpinning various machine learning algorithms. Equipped with this knowledge, you can modify algorithms to suit your business problem. Starting with the basic theory and concepts of mathematical modeling, you'll explore an array of mathematical tools that will empower you to extract insights and understand the data better, which in turn will aid in making optimal, data-driven decisions. The book allows you to explore mathematical optimization and its wide range of applications, and concludes by highlighting the synergetic value derived from blending mathematical models with machine learning. Ultimately, you'll be able to apply everything you've learned to choose the most fitting methodologies for the business problems you encounter. What you will learn Understand core concepts of mathematical models and their relevance in solving problems Explore various approaches to modeling and learning using Python Work with tested mathematical tools to gather meaningful insights Blend mathematical modeling with machine learning to find optimal solutions to business problems Optimize ML models built with business data, apply them to understand their impact on the business, and address critical questions Apply mathematical optimization for data-scarce problems where the objective and constraints are known Who this book is for If you are a budding data scientist seeking to augment your journey with mathematics, this book is for you. Researchers and R&D scientists will also be able to harness the concepts covered to their full potential. To make the best use of this book, a background in linear algebra, differential equations, basics of statistics, data types, data structures, and numerical algorithms will be useful.

More Details

Format
Edition
1st edition.
Language
English
ISBN
9781804617069, 1804617067

Notes

Description
Master the art of mathematical modeling through practical examples, use cases, and machine learning techniques Key Features Gain a profound understanding of various mathematical models that can be integrated with machine learning Learn how to implement optimization algorithms to tune machine learning models Build optimal solutions for practical use cases Purchase of the print or Kindle book includes a free PDF eBook Book Description Mathematical modeling is the art of transforming a business problem into a well-defined mathematical formulation. Its emphasis on interpretability is particularly crucial when deploying a model to support high-stake decisions in sensitive sectors like pharmaceuticals and healthcare. Through this book, you'll gain a firm grasp of the foundational mathematics underpinning various machine learning algorithms. Equipped with this knowledge, you can modify algorithms to suit your business problem. Starting with the basic theory and concepts of mathematical modeling, you'll explore an array of mathematical tools that will empower you to extract insights and understand the data better, which in turn will aid in making optimal, data-driven decisions. The book allows you to explore mathematical optimization and its wide range of applications, and concludes by highlighting the synergetic value derived from blending mathematical models with machine learning. Ultimately, you'll be able to apply everything you've learned to choose the most fitting methodologies for the business problems you encounter. What you will learn Understand core concepts of mathematical models and their relevance in solving problems Explore various approaches to modeling and learning using Python Work with tested mathematical tools to gather meaningful insights Blend mathematical modeling with machine learning to find optimal solutions to business problems Optimize ML models built with business data, apply them to understand their impact on the business, and address critical questions Apply mathematical optimization for data-scarce problems where the objective and constraints are known Who this book is for If you are a budding data scientist seeking to augment your journey with mathematics, this book is for you. Researchers and R&D scientists will also be able to harness the concepts covered to their full potential. To make the best use of this book, a background in linear algebra, differential equations, basics of statistics, data types, data structures, and numerical algorithms will be useful.
Local note
O'Reilly O'Reilly Online Learning: Academic/Public Library Edition

Table of Contents

Preface
Part 1: Mathematical Modeling
1
Introduction to Mathematical Modeling
Mathematical optimization
Understanding the problem
Formulation of the problem
Signal processing
Understanding the problem
Formulation of the problem
Control theory
Understanding the problem
Formulation of the problem
Summary
2
Machine Learning vis-à-vis Mathematical Modeling
ML as mathematical optimization
Example 1
regression
Example 2
neural network
ML
a predictive tool
E-commerce
Sales and marketing
Cybersecurity
Mathematical modeling
a prescriptive tool
Finance
Retail
Energy
Digital advertising
Summary
Part 2: Mathematical Tools
3
Principal Component Analysis
Linear algebra for PCA
Covariance matrix
eigenvalues and eigenvectors
Number of PCs
how to select for a dataset
Feature extraction methods
LDA
the difference from PCA
Applications of PCA
Noise reduction
Anomaly detection
Summary
4
Gradient Descent
Gradient descent variants
Application of gradient descent
Mini-batch gradient descent and stochastic gradient descent
Gradient descent optimizers
Momentum
Adagrad
RMSprop
Adam
Summary
5
Support Vector Machine
Support vectors in SVM
Kernels for SVM
Implementation of SVM
Summary
6
Graph Theory
Types of graphs
Undirected graphs
Directed graphs
Weighted graphs
Optimization use case
Optimization problem
Optimized solution
Graph neural networks
Summary
7
Kalman Filter
Computation of measurements
Filtration of measurements
Implementation of the Kalman filter
Summary
8
Markov Chain
Discrete-time Markov chain
Transition probability
Application of the Markov chain
Markov Chain Monte Carlo
Gibbs sampling algorithm
Metropolis-Hastings algorithm
Illustration of MCMC algorithms
Summary
Part 3: Mathematical Optimization
9
Exploring Optimization Techniques
Optimizing machine learning models
Random search
Grid search
Bayesian optimization
Optimization in operations research
Evolutionary optimization
Summary
10
Optimization Techniques for Machine Learning
General optimization algorithms
First-order algorithms
Second-order algorithms
Complex optimization algorithms
Differentiability of objective functions
Direct and stochastic algorithms
Summary
Epilogue
Index
Other Books You May Enjoy

Discover More

More Like This

  • Interpreting machine learning models: learn model interpretability and explainability methods Cover
  • Pretrain Vision and Large Language Models in Python: End-To-end Techniques for Building and Deploying Foundation Models on AWS Cover
  • BECOMING AN ENTERPRISE DJANGO DEVELOPER: discover best practices, tooling, and solutions for writing and organizing Django applications in production Cover
  • Natural language processing recipes: unlocking text data with machine learning and deep learning using Python Cover
  • POLARS COOKBOOK: over 60 practical recipes to transform, manipulate, and analyze your data using Python Polars 1.x Cover
  • ENTERPRISE ARCHITECTURE PATTERNS WITH PYTHON: how to apply ddd, ports and adapters, and ... enterprise architecture design patterns in a pytho Cover
  • Python made easy: a first course in computer programming using Python Cover
  • Deep learning and its applications using Python Cover
  • Mastering flask web and API development: build and deploy production-ready flask apps seamlessly across web, APIs, and mobile platforms Cover
  • Foundations of Libvirt development: how to set up and maintain a virtual machine environment with Python Cover
  • Deep Learning with PyTorch Cover
  • Pytest quick start guide: write better Python code with simple and maintainable tests Cover
  • Hands-on machine learning for algorithmic trading: design and implement investment strategies based on smart algorithms that learn from data using Python Cover
  • High performance python: practical performant programming for humans Cover
  • Mastering Python networking: your one-stop solution to using Python for network automation, DevOps, and test-driven development Cover
  • Full Stack Python Security Cover
  • The data visualization workshop: a self-paced, practical approach to transforming your complex data into compelling, captivating graphics Cover
  • Optimizing Visual Studio Code for Python development: developing more efficient and effective programs in Python Cover
  • Applied data science with Python and Jupyter Cover
  • Finding ghosts in your data: anomaly detection techniques with examples in Python Cover
  • The absolute beginner's guide to Python programming: a step-by-step guide with examples with lab exercises Cover
  • Saving memory when creating a large number of instances Cover
  • Hands-on data analysis with Pandas: efficiently perform data collection, wrangling, analysis, and visualization using Python Cover
  • Python programming blueprints: build nine projects by leveraging powerful frameworks such as Flask, Nameko, and Django Cover
  • Applied reinforcement learning with Python: with OpenAI Gym, Tensorflow and Keras Cover

Reviews from GoodReads

Loading GoodReads Reviews.

Citations

APA Citation, 7th Edition (style guide)

Sarkar, R. (2023). A HANDBOOK OF MATHEMATICAL MODELS WITH PYTHON: elevate your machine learning projects with Networkx, PuLP, and linalg (1st edition.). Packt Publishing Ltd..

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Sarkar, Ranja. 2023. A HANDBOOK OF MATHEMATICAL MODELS WITH PYTHON: Elevate Your Machine Learning Projects With Networkx, PuLP, and Linalg. Birmingham, UK: Packt Publishing Ltd.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Sarkar, Ranja. A HANDBOOK OF MATHEMATICAL MODELS WITH PYTHON: Elevate Your Machine Learning Projects With Networkx, PuLP, and Linalg Birmingham, UK: Packt Publishing Ltd, 2023.

Harvard Citation (style guide)

Sarkar, R. (2023). A HANDBOOK OF MATHEMATICAL MODELS WITH PYTHON: elevate your machine learning projects with networkx, pulp, and linalg. 1st edn. Birmingham, UK: Packt Publishing Ltd.

MLA Citation, 9th Edition (style guide)

Sarkar, Ranja. A HANDBOOK OF MATHEMATICAL MODELS WITH PYTHON: Elevate Your Machine Learning Projects With Networkx, PuLP, and Linalg 1st edition., Packt Publishing Ltd., 2023.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

Staff View

Grouped Work ID
3632ab31-a730-3fde-01f7-3e37a0569d81-eng
Go To Grouped Work View in Staff Client

Grouping Information

Grouped Work ID3632ab31-a730-3fde-01f7-3e37a0569d81-eng
Full titlehandbook of mathematical models with python elevate your machine learning projects with networkx pulp and linalg
Authorsarkar ranja
Grouping Categorybook
Last Update2025-01-24 12:33:29PM
Last Indexed2025-05-03 03:09:14AM

Book Cover Information

Image Sourcedefault
First LoadedFeb 16, 2025
Last UsedFeb 16, 2025

Marc Record

First DetectedDec 16, 2024 11:27:03 PM
Last File Modification TimeDec 17, 2024 08:26:29 AM
SuppressedRecord had no items

MARC Record

LEADER06730cam a22004577a 4500
001on1395885681
003OCoLC
00520241217082410.0
006m     o  d        
007cr |n|||||||||
008230902s2023    enk     o     000 0 eng d
019 |a 1396063929
020 |a 9781804617069|q (electronic bk.)
020 |a 1804617067|q (electronic bk.)
035 |a (OCoLC)1395885681|z (OCoLC)1396063929
037 |a 9781804616703|b O'Reilly Media
040 |a YDX|b eng|c YDX|d ORMDA|d EBLCP|d OCLCO|d OCLCQ|d UKAHL|d OCLCF|d OCLCO
049 |a MAIN
050 4|a Q325.5
08204|a 006.3/1|2 23/eng/20230906
1001 |a Sarkar, Ranja,|e author.
24512|a A HANDBOOK OF MATHEMATICAL MODELS WITH PYTHON|h [electronic resource] :|b elevate your machine learning projects with Networkx, PuLP, and linalg /|c Dr. Ranja Sarkar.
250 |a 1st edition.
260 |a Birmingham, UK :|b Packt Publishing Ltd.,|c 2023.
300 |a 1 online resource
5050 |a Preface -- Part 1: Mathematical Modeling -- 1 -- Introduction to Mathematical Modeling -- Mathematical optimization -- Understanding the problem -- Formulation of the problem -- Signal processing -- Understanding the problem -- Formulation of the problem -- Control theory -- Understanding the problem -- Formulation of the problem -- Summary -- 2 -- Machine Learning vis-à-vis Mathematical Modeling -- ML as mathematical optimization -- Example 1 -- regression -- Example 2 -- neural network -- ML -- a predictive tool -- E-commerce -- Sales and marketing -- Cybersecurity
5058 |a Mathematical modeling -- a prescriptive tool -- Finance -- Retail -- Energy -- Digital advertising -- Summary -- Part 2: Mathematical Tools -- 3 -- Principal Component Analysis -- Linear algebra for PCA -- Covariance matrix -- eigenvalues and eigenvectors -- Number of PCs -- how to select for a dataset -- Feature extraction methods -- LDA -- the difference from PCA -- Applications of PCA -- Noise reduction -- Anomaly detection -- Summary -- 4 -- Gradient Descent -- Gradient descent variants -- Application of gradient descent -- Mini-batch gradient descent and stochastic gradient descent
5058 |a Gradient descent optimizers -- Momentum -- Adagrad -- RMSprop -- Adam -- Summary -- 5 -- Support Vector Machine -- Support vectors in SVM -- Kernels for SVM -- Implementation of SVM -- Summary -- 6 -- Graph Theory -- Types of graphs -- Undirected graphs -- Directed graphs -- Weighted graphs -- Optimization use case -- Optimization problem -- Optimized solution -- Graph neural networks -- Summary -- 7 -- Kalman Filter -- Computation of measurements -- Filtration of measurements -- Implementation of the Kalman filter -- Summary -- 8 -- Markov Chain -- Discrete-time Markov chain
5058 |a Transition probability -- Application of the Markov chain -- Markov Chain Monte Carlo -- Gibbs sampling algorithm -- Metropolis-Hastings algorithm -- Illustration of MCMC algorithms -- Summary -- Part 3: Mathematical Optimization -- 9 -- Exploring Optimization Techniques -- Optimizing machine learning models -- Random search -- Grid search -- Bayesian optimization -- Optimization in operations research -- Evolutionary optimization -- Summary -- 10 -- Optimization Techniques for Machine Learning -- General optimization algorithms -- First-order algorithms -- Second-order algorithms
5058 |a Complex optimization algorithms -- Differentiability of objective functions -- Direct and stochastic algorithms -- Summary -- Epilogue -- Index -- Other Books You May Enjoy
520 |a Master the art of mathematical modeling through practical examples, use cases, and machine learning techniques Key Features Gain a profound understanding of various mathematical models that can be integrated with machine learning Learn how to implement optimization algorithms to tune machine learning models Build optimal solutions for practical use cases Purchase of the print or Kindle book includes a free PDF eBook Book Description Mathematical modeling is the art of transforming a business problem into a well-defined mathematical formulation. Its emphasis on interpretability is particularly crucial when deploying a model to support high-stake decisions in sensitive sectors like pharmaceuticals and healthcare. Through this book, you'll gain a firm grasp of the foundational mathematics underpinning various machine learning algorithms. Equipped with this knowledge, you can modify algorithms to suit your business problem. Starting with the basic theory and concepts of mathematical modeling, you'll explore an array of mathematical tools that will empower you to extract insights and understand the data better, which in turn will aid in making optimal, data-driven decisions. The book allows you to explore mathematical optimization and its wide range of applications, and concludes by highlighting the synergetic value derived from blending mathematical models with machine learning. Ultimately, you'll be able to apply everything you've learned to choose the most fitting methodologies for the business problems you encounter. What you will learn Understand core concepts of mathematical models and their relevance in solving problems Explore various approaches to modeling and learning using Python Work with tested mathematical tools to gather meaningful insights Blend mathematical modeling with machine learning to find optimal solutions to business problems Optimize ML models built with business data, apply them to understand their impact on the business, and address critical questions Apply mathematical optimization for data-scarce problems where the objective and constraints are known Who this book is for If you are a budding data scientist seeking to augment your journey with mathematics, this book is for you. Researchers and R&D scientists will also be able to harness the concepts covered to their full potential. To make the best use of this book, a background in linear algebra, differential equations, basics of statistics, data types, data structures, and numerical algorithms will be useful.
590 |a O'Reilly|b O'Reilly Online Learning: Academic/Public Library Edition
650 0|a Machine learning|x Mathematical models.
650 0|a Python (Computer program language)|9 71333
77608|i Print version:|z 1804616702|z 9781804616703|w (OCoLC)1395538760
85640|u https://library.access.arlingtonva.us/login?url=https://learning.oreilly.com/library/view/~/9781804616703/?ar|x O'Reilly|z eBook
938 |a Askews and Holts Library Services|b ASKH|n AH41688727
938 |a YBP Library Services|b YANK|n 305675045
938 |a ProQuest Ebook Central|b EBLB|n EBL30723521
994 |a 92|b VIA
999 |c 359713|d 359713