Math

• A Course in Python: The Core of the Language

  Roozbeh Hazrat · Book

  A Course in Python: The Core of the Language by Roozbeh Hazrat is a comprehensive textbook that introduces readers to Python programming through clear examples and worked-out exercises. Originally developed for undergraduate students in mathematics, science, engineering, and finance, the book is also suitable for self-study, especially for researchers aiming to use Python as a computational tool. Key Highlights: Structured Learning: Each section begins with a new topic description, followed by basic examples and detailed exercises, facilitating a step-by-step learning process. Practical Application: Emphasizes programming and problem-solving, particularly in numerical problems that don't require advanced mathematics. Comprehensive Coverage: Includes chapters on data handling, calculus, equation solving, and graphics, covering fundamental Python topics. Author's Expertise: Roozbeh Hazrat, a Professor of Pure Mathematics at Western Sydney University, brings extensive teaching experience in computer programming across various institutions. This book serves as an ideal resource for those seeking a hands-on, example-driven introduction to Python, balancing theoretical concepts with practical coding skills.

  Also filed under Computer Science, Python

• A First Course in Monte Carlo Methods

  D. Sanz-Alonso & O. Al-Ghattas · Book

  A First Course in Monte Carlo Methods D. Here, B10000 = 7854 draws fell within the unit circle, leading to an estimate bπ10000 = 3.1416. 2 2.1 A strictly increasing c.d.f. 10 2.2 Inverse transformation method for sampling from an Exponential(1) distribu- tion.

  Also filed under Quant Finance, Statistics

• Adam: A Method for Stochastic Optimization

  Diederik P. Kingma and Jimmy Ba · Report

  The 2015 paper introducing the Adam optimizer, combining adaptive per-parameter learning rates with momentum. Became the default optimizer for deep learning and remains the standard choice for training large models.

  Also filed under AI, Machine Learning

• Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto

  Eric Carter & Matthew Hurst · Book

  Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this ...

  Also filed under Machine Learning, AI

• Applied Machine Learning with Python

  Andrea Giussani · Book

  Via Salasco, 5 - 20136 Milano Tel.

  Also filed under Python, Machine Learning

• Brownian Motion: The Foundation

  Author Unknown · Notes

  Brownian Motion: The Foundation When you look at a stock’s price over time, it doesn't move in a straight line. Instead, it fluctuates, moving up and down in an unpredictable manner. This kind of movement can be modeled using Brownian Motion, a fundamental stochastic process. Brownian Motion models the random movement observed in particles suspended in a fluid, and in finance, it models the random fluctuations of asset prices over time.

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• CS229 Lecture Notes

  Andrew Ng & Tengyu Ma · Report

  The CS229 Lecture Notes are comprehensive materials from Stanford University's Machine Learning course, CS229, primarily authored by Andrew Ng and later updated by Tengyu Ma. These notes serve as a foundational resource for understanding various machine learning algorithms and principles. Key Highlights: Supervised Learning: Linear Regression: Introduces the Least Mean Squares (LMS) algorithm and the normal equations for parameter estimation. Logistic Regression: Discusses classification tasks and the application of logistic functions. Regularization: Explores techniques like L1 and L2 regularization to prevent overfitting. Unsupervised Learning: Clustering: Covers methods such as the k-means algorithm for grouping similar data points. Dimensionality Reduction: Explains Principal Component Analysis (PCA) for reducing feature space while preserving variance. Reinforcement Learning: Markov Decision Processes (MDPs): Provides a framework for modeling decision-making in environments with uncertainty. Policy Gradient Methods: Discusses approaches for optimizing policies in reinforcement learning scenarios. These lecture notes are available on the official CS229 website and have been utilized globally by students and professionals seeking a structured understanding of machine learning concepts. For more detailed information, you can refer to the CS229 Lecture Notes.

  Also filed under Machine Learning, Computer Science

• Data Science: The Hard Parts. Techniques for Excelling at Data Science

  Daniel Vaughan · Book

  "Data Science: The Hard Parts" by Daniel Vaughan is a comprehensive guide that delves into the more challenging and often overlooked aspects of data science. This book aims to equip readers with the knowledge and techniques required to navigate the complex, real-world challenges faced by data scientists. It goes beyond basic theory and covers essential techniques and strategies that help in tackling the most difficult aspects of data science projects. Key Highlights: Navigating Real-World Data Challenges: Vaughan emphasizes the importance of recognizing and dealing with the inherent complexity of real-world data. He stresses that a large portion of data science is understanding the intricacies and noise in data rather than relying purely on theoretical models. The book explains how to approach messy, incomplete, and imperfect data, which is a significant challenge in most data science tasks. Problem-Solving Framework: The book provides a clear framework for approaching and solving data science problems. It teaches readers how to break down problems into manageable chunks, ensuring that they can tackle complex issues systematically. Vaughan discusses how to structure data science workflows to optimize for success, while keeping the big picture in mind. Exploratory Data Analysis (EDA): The book dives deep into the process of exploratory data analysis, helping readers understand how to approach data, what techniques to use for cleaning and processing, and how to use EDA to find meaningful patterns. It goes beyond basic statistics, introducing methods for identifying hidden structures, correlations, and outliers that may impact model performance. Advanced Machine Learning Techniques: Vaughan covers advanced machine learning techniques, providing insights into more complex models and their real-world applications. The book tackles how to select the appropriate machine learning algorithm for a given problem, optimize model performance, and deal with challenges such as overfitting, underfitting, and bias in data. Model Evaluation and Validation: A significant part of the book is dedicated to model evaluation and validation. Vaughan explores the nuances of cross-validation, model selection, and the process of comparing models to ensure robustness and reliability. He emphasizes the importance of a clear evaluation strategy to avoid common pitfalls such as over-optimism in model performance. Dealing with Ambiguity and Uncertainty: Vaughan recognizes that data science often involves dealing with uncertainty and ambiguity. The book introduces probabilistic thinking and Bayesian techniques, helping readers make informed decisions when faced with incomplete or ambiguous data. This section is particularly useful for those working with predictive modeling and uncertain outcomes. Effective Communication and Presentation: The book highlights the importance of communication in data science. Vaughan discusses how to effectively present complex data insights to non-technical stakeholders, providing guidance on creating clear visualizations and communicating findings in a way that drives decision-making. This includes an emphasis on storytelling with data and visual storytelling. Practical Insights from Real-World Projects: Drawing from Vaughan’s own experience as a data scientist, the book offers real-world case studies and examples that illustrate how the principles discussed can be applied in practice. These examples provide valuable lessons on how to navigate the challenges of data science in different industries.

  Also filed under Computer Science, Data Visualization

• Deep Learning with PyTorch

  Eli Stevens, Luca Antiga, Thomas Viehmann · Book

  "Deep Learning with PyTorch" by Eli Stevens, Luca Antiga, and Thomas Viehmann is an insightful and practical guide to mastering deep learning using the PyTorch framework. The book is designed for those who are familiar with basic machine learning concepts and want to dive deeper into the world of deep learning with a hands-on approach. Key Concepts Covered: Introduction to PyTorch: The book begins with an introduction to the PyTorch framework, highlighting its flexibility, ease of use, and efficient handling of dynamic neural networks. It emphasizes how PyTorch’s tensor operations form the backbone of deep learning models, similar to NumPy but with added support for GPU acceleration. Tensors and Autograd: The authors provide an in-depth exploration of tensors, the fundamental data structure in PyTorch, explaining their creation, manipulation, and efficient use. They also introduce autograd, PyTorch's automatic differentiation library, which simplifies the process of computing gradients for backpropagation in neural networks. Building Neural Networks: The book walks through the steps of constructing various neural network architectures using PyTorch's nn.Module class. It covers feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), providing code examples and explanations on how to define, train, and evaluate these models. Training Neural Networks: A significant portion of the book focuses on model training, including defining loss functions, choosing optimizers (like SGD and Adam), and managing training loops. The authors dive into best practices for handling overfitting, using techniques like dropout, batch normalization, and data augmentation. Working with Real-World Data: The book emphasizes how to preprocess and load real-world data for deep learning tasks, including using PyTorch's DataLoader and Dataset classes. It also discusses the importance of data preprocessing and augmentation for improving model generalization. Transfer Learning: One of the standout sections of the book is on transfer learning. It explains how to leverage pre-trained models (like ResNet, VGG, etc.) for tasks like image classification, speeding up the training process and improving accuracy, especially when data is scarce. Generative Models and Advanced Topics: The book introduces advanced deep learning topics such as Generative Adversarial Networks (GANs), reinforcement learning, and other generative models, helping readers expand their knowledge into more complex areas of deep learning. Practical Considerations: The authors also provide practical insights on model evaluation, deployment, and debugging. They explain how to tune hyperparameters effectively and optimize models for deployment on CPUs and GPUs. Summary: "Deep Learning with PyTorch" is a comprehensive, practical guide that equips readers with the knowledge and tools to build state-of-the-art deep learning models using PyTorch. The book offers clear explanations, hands-on examples, and expert advice, making it an invaluable resource for both beginners and those looking to deepen their understanding of deep learning techniques. By the end of the book, readers will have a solid understanding of PyTorch's core concepts and be able to implement and train various types of deep learning models for real-world applications.

  Also filed under Machine Learning, Computer Science

• Dive into Deep Learning

  Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola · Book

  "Dive into Deep Learning" is an open-source book that provides a comprehensive, hands-on introduction to deep learning techniques. Written by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, the book offers an in-depth exploration of the fundamental principles and applications of deep learning, with a focus on practical implementation using modern deep learning frameworks like Apache MXNet and PyTorch. Key Highlights: Introduction to Deep Learning: The book starts with an introduction to deep learning, explaining its significance and how it differs from traditional machine learning techniques. It covers the historical context of neural networks and their evolution into deep learning, highlighting key breakthroughs such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Mathematical Foundations: The authors provide a solid mathematical foundation for understanding deep learning. Topics like linear algebra, probability, optimization, and calculus are discussed, as they form the backbone of many deep learning algorithms. This foundational knowledge is essential for readers to grasp how deep learning models work under the hood. Neural Networks and Backpropagation: The book thoroughly explains the architecture of neural networks, covering perceptrons, multi-layer perceptrons (MLPs), and how backpropagation works to update model weights during training. The authors emphasize understanding how the model learns and how to compute gradients using the chain rule to minimize the loss function. Training Deep Networks: In this section, the book delves into the critical aspects of training deep learning models, including optimization algorithms (such as gradient descent), weight initialization, regularization techniques (dropout, batch normalization), and activation functions. The authors also discuss challenges in training deep models, such as vanishing and exploding gradients. Convolutional Neural Networks (CNNs): CNNs are covered in depth, as they are essential for tasks like image recognition and computer vision. The book explains convolution layers, pooling layers, and how CNNs can be used to automatically learn hierarchical feature representations from raw input data. It also provides hands-on examples for building and training CNNs for image classification tasks. Recurrent Neural Networks (RNNs) and Sequence Models: The book introduces RNNs and their variants, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), which are used for sequential data. It explores how RNNs can model time-dependent processes, such as speech recognition, language modeling, and machine translation. Generative Models: The authors introduce generative models, particularly Generative Adversarial Networks (GANs), which are used to generate new data samples that resemble a given dataset. GANs are explored with practical examples, showing their potential in generating images, videos, and even music. Transfer Learning: Transfer learning is another key topic in the book, where pre-trained models are fine-tuned for new tasks. The authors show how leveraging models that have been trained on large datasets can help with tasks where data is limited, improving model performance and reducing training time. Advanced Topics: The book also touches on more advanced topics in deep learning, such as reinforcement learning, attention mechanisms, and transformers, which have been key in the development of state-of-the-art models for tasks like natural language processing (NLP) and image generation.

  Also filed under Machine Learning, Computer Science

• Exercises in Machine Learning

  Michael U. Gutmann · Book

  "Exercises in Machine Learning" by Michael U. Gutmann is a comprehensive and hands-on resource for anyone seeking to deepen their understanding of machine learning algorithms through practical exercises. Designed for students, practitioners, and machine learning enthusiasts, the book emphasizes applying theoretical knowledge to real-world problems, ensuring readers not only understand key concepts but also know how to implement them effectively. Key Highlights: Comprehensive Coverage of Machine Learning Topics: This book covers a broad range of machine learning techniques, offering exercises on both supervised and unsupervised learning, model evaluation, and optimization. It helps readers understand the core principles of machine learning and how to implement them practically. Supervised Learning Techniques: Key algorithms such as linear regression, logistic regression, support vector machines (SVM), and decision trees are explored in detail. Each exercise provides hands-on experience, allowing readers to apply these models effectively to real datasets, making complex topics more accessible. Unsupervised Learning Methods: Unsupervised techniques like k-means clustering, hierarchical clustering, and principal component analysis (PCA) are covered with practical exercises to help readers work with unlabelled data and uncover hidden patterns. Model Evaluation and Tuning: The book introduces essential model evaluation methods such as cross-validation, bias-variance tradeoff, and overfitting, helping readers understand how to assess model performance. Additionally, it covers hyperparameter tuning, including techniques like grid search and random search to enhance model accuracy. Advanced Topics: For those looking to explore more advanced machine learning techniques, the book touches on deep learning, reinforcement learning, and Bayesian methods, providing exercises that introduce these complex methods in a manageable and practical way. Python-Based Implementation: Each exercise is designed with Python in mind, using libraries such as NumPy, scikit-learn, and TensorFlow. This allows readers to apply machine learning concepts using the same tools that data scientists and machine learning engineers use in the industry.

  Also filed under Machine Learning, Computer Science

• Finite Difference Methods (FDM)

  rearranging terms and ignoring higher-order terms, we can approximate the first derivative as: · Report

  Introduction This chapter introduces the fundamental terminologies of Finite Difference Methods (FDM), provid- ing the reader with the essential context. It covers key approximation techniques, including forward, backward, and central difference methods. 1.1 Introduction to FDM Finite Difference Methods (FDM) are widely used numerical techniques for solving differential equa- tions. By approximating derivatives using finite differences, these methods convert differential equa- tions into systems of algebraic equations that can be solved numerically.

  Also filed under Quant Finance, Computer Science

• Foundations of Machine learning - Lecture Notes

  Ajay Nagesh · Report

  "Foundations of Machine Learning - Lecture Notes" by Ajay Nagesh is an in-depth guide to the foundational concepts of machine learning, offering a comprehensive overview of the theoretical underpinnings and practical applications of the field. The book is designed for students, professionals, and researchers who seek to understand the core principles of machine learning and their real-world applications. Key Highlights: Introduction to Machine Learning Concepts: The book starts with a strong introduction to machine learning, covering its history, evolution, and various paradigms such as supervised learning, unsupervised learning, and reinforcement learning. It establishes a solid theoretical foundation for understanding how machines can learn from data. Mathematical Foundations: The book dives into the key mathematical concepts that form the backbone of machine learning, such as linear algebra, probability theory, optimization, and statistics. These mathematical tools are essential for building, understanding, and analyzing machine learning models. Supervised Learning: A significant portion of the book focuses on supervised learning algorithms. The text explains the mechanics and applications of popular models, such as linear regression, logistic regression, decision trees, and support vector machines (SVMs). Each model is accompanied by examples, explaining the mathematical formulation, training process, and how to evaluate model performance. Unsupervised Learning: The book covers unsupervised learning techniques like clustering, dimensionality reduction, and anomaly detection. It introduces methods such as k-means clustering, hierarchical clustering, principal component analysis (PCA), and t-SNE, providing a solid understanding of how these techniques can be applied to real-world data without labeled outputs. Optimization and Model Evaluation: A critical section of the book is dedicated to optimization techniques used in machine learning, including gradient descent, stochastic gradient descent, and regularization methods. It also explores various performance metrics like accuracy, precision, recall, F1-score, and cross-validation, offering insights into how to evaluate model performance effectively. Deep Learning Basics: While the book focuses on foundational machine learning techniques, it also introduces readers to deep learning, discussing neural networks, backpropagation, and architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). This section serves as a bridge between classical machine learning and advanced deep learning techniques. Practical Applications: The book is rich with real-world examples and case studies, demonstrating how machine learning concepts can be applied to a wide range of fields, such as healthcare, finance, natural language processing (NLP), and computer vision. It highlights how machine learning models can be built and deployed to solve practical problems in these domains. Ethical Considerations: In addition to technical content, the book touches upon the ethical implications of machine learning, such as bias in data, fairness in algorithms, and the responsible use of AI. It discusses how to build ethical models and the importance of transparency in machine learning systems. Hands-On Learning: The book encourages hands-on learning by providing coding exercises and examples in Python, using libraries such as scikit-learn, TensorFlow, and Keras. The practical approach allows readers to implement and test the models they learn about in the book, reinforcing the theoretical concepts. Future Trends in Machine Learning: The book concludes with a discussion on emerging trends in machine learning, such as transfer learning, explainable AI (XAI), and the integration of machine learning with other fields like quantum computing. It helps readers understand where the field is headed and how to stay up-to-date with the latest advancements.

  Also filed under Machine Learning, Computer Science

• Fundamentals of Actuarial Mathematics

  S. David Promislow · Book

  The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books.

  Also filed under Quant Finance, Finance

• Fundamentals of Machine Learning

  Roozbeh Sanaei · Report

  11 3.1.2 Different Algorithms in ICA . 14 3.1.5 Fast Independent Component Analysis . 17 3.2.2 Comparative Analysis of SNE, t-SNE, and UMAP . 18 3.2.3 SNE, t-SNE and UMAP Comparison .

  Also filed under Machine Learning, Computer Science

• Interpolation Methods For Curve Construction

  Pat Hagan & Graeme West · Report

  "Interpolation Methods for Curve Construction" is a specialized mathematical and financial guide that explores various interpolation techniques used in curve construction. The book is particularly relevant for quantitative analysts, financial engineers, and mathematicians working in areas such as yield curve modeling, interest rate derivatives, and risk management. It provides a deep understanding of interpolation methods, their properties, and their impact on numerical stability and financial applications. Key Topics Covered: Fundamentals of Interpolation: Introduction to interpolation concepts, including polynomial, piecewise, and spline interpolation. Linear and Polynomial Interpolation: Discusses basic techniques such as linear interpolation and Lagrange polynomials, with applications in finance. Spline Interpolation Methods: Covers cubic splines, B-splines, and natural splines, explaining how they ensure smooth and stable curve construction. Monotonicity and Shape-Preserving Interpolation: Examines methods that prevent oscillations and ensure realistic financial curve modeling. Hermite and Rational Interpolation: Explores advanced techniques that improve curve smoothness and numerical stability. Applications in Finance: Discusses how interpolation methods are used for yield curves, discount factors, option pricing, and bootstrapping interest rate curves. Error Analysis and Stability Considerations: Evaluates the numerical accuracy, computational efficiency, and robustness of different interpolation techniques. This book serves as an essential reference for professionals and researchers who need to construct smooth, reliable curves for financial modeling. With a balance of theory, practical implementation, and real-world financial applications, it provides valuable insights into interpolation techniques critical for modern quantitative finance.

  Also filed under Quant Finance, Finance

• Introduction to Statistical and Machine Learning Methods for Data Science

  Carlos Andre Reis Pinheiro & Mike Patetta · Book

  "Introduction to Statistical and Machine Learning Methods for Data Science" by Carlos Andre Reis Pinheiro & Mike Patetta provides a comprehensive guide to the foundational techniques used in modern data science. The book bridges the gap between traditional statistical approaches and contemporary machine learning methods, offering readers a well-rounded understanding of data-driven decision-making. Key Topics Covered: Fundamentals of Statistical Methods: Covers core concepts such as probability distributions, hypothesis testing, regression analysis, and statistical inference. Machine Learning Techniques: Introduces supervised and unsupervised learning algorithms, including decision trees, random forests, support vector machines (SVMs), and neural networks. Feature Engineering & Data Preprocessing: Explores methods to clean, transform, and optimize data for machine learning models. Model Evaluation & Validation: Discusses key metrics like accuracy, precision, recall, and ROC curves to assess model performance. Real-World Applications: Demonstrates how statistical and machine learning methods are applied in finance, healthcare, and business analytics. This book is designed for data scientists, analysts, and professionals looking to enhance their knowledge of both classical statistical methods and modern machine learning techniques. With practical examples and code implementations, it serves as an essential resource for anyone aiming to leverage data science in their field.

  Also filed under Machine Learning, Statistics

• Learn More Python 3 the Hard Way

  Zed A. Shaw · Book

  "Learn More Python 3 the Hard Way" by Zed A. Shaw is a continuation of his instructional series, designed for individuals who have a foundational understanding of Python and wish to deepen their programming skills. This book emphasizes hands-on learning through a series of 52 meticulously crafted exercises, each aimed at building practical capabilities in Python programming. Key Features: Project-Based Learning: The book adopts a project-centric approach, guiding readers through exercises that involve analyzing problems, designing solutions, and implementing them in Python. This methodology fosters a deeper understanding of programming concepts and enhances problem-solving skills. Emphasis on Process and Quality: Shaw underscores the importance of adhering to a structured process, nurturing creativity, and striving for quality in coding practices. These principles are woven throughout the exercises to cultivate disciplined and proficient programmers. Supplementary Video Content: The book is complemented by over 12 hours of online video tutorials, where Shaw demonstrates how to break, fix, and debug code. This visual aid reinforces the lessons from the exercises and provides deeper insights into effective coding practices. "Learn More Python 3 the Hard Way" is ideal for readers who have completed introductory Python courses and are seeking to advance their skills through practical application. By engaging with real-world projects and challenges, readers can transition from basic understanding to proficient coding, preparing them for more complex programming endeavors.

  Also filed under Python, Computer Science

• Learning Professional Python

  Usharani Bhimavarapu & Jude D. Hemanth · Book

  Learning Professional Python by Usharani Bhimavarapu and Jude D. Hemanth is a two-volume series designed to provide a comprehensive understanding of Python programming, catering to both beginners and experienced programmers. The series emphasizes object-oriented programming concepts and practical applications, aiming to equip readers with the skills necessary for professional Python development. Volume 1: The Basics Overview: This volume introduces readers to the fundamentals of Python programming, focusing on object-oriented principles and essential programming constructs. It's suitable for individuals new to programming or those transitioning from other languages. Key Highlights: Python Fundamentals: Covers the basics of Python syntax, data types, and control structures, providing a solid foundation for further learning. Object-Oriented Programming (OOP): Explores core OOP concepts such as classes, objects, inheritance, dynamic dispatch, interfaces, and packages, enabling readers to write modular and reusable code. Advanced Features: Delves into Python's generics and collections, exception handling, multithreaded applications, and designing graphical user interface (GUI) applications, preparing readers for complex programming tasks. Volume 2: Advanced Overview: Building upon the foundational knowledge from Volume 1, this volume delves into advanced topics, providing readers with the expertise needed for complex application development. Key Highlights: Advanced OOP Concepts: Further explores object-oriented principles, enhancing the reader's ability to design sophisticated systems. Exception Handling and Multithreading: Provides in-depth coverage of robust error handling and concurrent programming, essential for developing reliable and efficient applications. GUI Application Development: Guides readers through designing and implementing user-friendly GUI applications, expanding the scope of Python projects. Case Studies: Presents real-world projects such as WhatsApp Analyzer, Breast Cancer Prediction, Stock Price Prediction, and more, offering practical insights into applying Python in various domains.

  Also filed under Python, Computer Science

• Let Us Python Solutions

  Yashavant Kanetkar & Aditya Kanetkar · Book

  No part of this publication can be stored in a retrieval system or reproduced in any form or by any means without the prior written permission of the publishers. LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY The Author and Publisher of this book have tried their best to ensure that the programmes, procedures and functions described in the book are correct. However, the author and the publishers make no warranty of any kind, expressed or implied, with regard to these programmes or the documentation contained in the book. The author and publisher shall not be liable in any event of any damag...

  Also filed under Python, Data Visualization

• MATH-TWS: a package to connect Mathematica to Interactive Brokers Trader Workstation

  ALGORITHMIC EXECUTION LLC. · Guide

  "MATH-TWS" is a technical guide that introduces a package designed to integrate Mathematica with Interactive Brokers Trader Workstation (TWS), enabling traders and quantitative analysts to develop and execute trading strategies within Mathematica’s powerful computational environment. The book provides insights into the seamless connection between symbolic computation, real-time market data, and algorithmic trading execution. Key Topics Covered: Introduction to Mathematica and Interactive Brokers (IB) API: Explains the fundamentals of using Mathematica for financial modeling and how it can be integrated with IB’s TWS platform. Setting Up MATH-TWS: Guides users through the installation and configuration process to establish a real-time connection between Mathematica and IB’s trading system. Market Data Retrieval: Demonstrates how to fetch live market data, historical data, and financial indicators directly within Mathematica for analysis. Algorithmic Trading Execution: Covers order placement, trade execution, and automated strategy implementation using Mathematica’s computational tools. Risk Management & Portfolio Optimization: Introduces methods to manage trading risk, optimize portfolios, and analyze performance metrics using statistical and machine learning techniques. Customizing & Extending MATH-TWS: Discusses how users can modify and extend the package to fit their specific trading needs. This book is an essential resource for quantitative traders, algorithmic developers, and financial engineers who want to leverage Mathematica’s computational power alongside Interactive Brokers’ API for enhanced trading capabilities. It provides practical coding examples, real-world applications, and a step-by-step approach to building automated trading systems efficiently.

  Also filed under Quant Finance, Data Visualization

• Mathematics for Inference and Machine Learning

  Marc Deisenroth & Stefanos Zafeiriou · Report

  5 1.2.1 Means and Covariances . 6 1.2.1.1 Sum of Random Variables . 7 1.2.1.2 Affine Transformation . 7 1.2.2 Statistical Independence .

  Also filed under Statistics, Machine Learning

• Mathematics For Machine Learning

  Marc Peter Deisenroth, A. Aldo Faisal & Cheng Soon Ong · Book

  "Mathematics for Machine Learning" is a comprehensive guide that provides the mathematical foundation necessary for understanding and developing machine learning models. The book is designed for students, engineers, and researchers who want to strengthen their mathematical skills to effectively engage with modern machine learning techniques. Key Topics Covered: Linear Algebra: Covers essential concepts such as vectors, matrices, eigenvalues, and singular value decomposition (SVD), which are crucial for algorithms like PCA and deep learning. Analytical Geometry: Explores coordinate transformations, basis changes, and geometric interpretations of machine learning problems. Matrix Decompositions: Discusses LU, QR, and Cholesky decompositions and their applications in optimization and numerical computations. Vector Calculus: Introduces gradients, Hessians, and Jacobians, which are fundamental for optimization and backpropagation in neural networks. Probability and Statistics: Provides an overview of probability distributions, Bayes’ theorem, and information theory concepts used in probabilistic machine learning models. Optimization Techniques: Explains gradient descent, convex optimization, and constrained optimization, which are essential for training machine learning models. The book balances theory with practical applications, making complex mathematical concepts more accessible through intuitive explanations and visualizations. By focusing on the mathematical principles behind machine learning, it equips readers with the tools needed to develop, analyze, and optimize machine learning algorithms effectively.

  Also filed under Machine Learning, Quant Finance

• ML Cheatsheet Documentation

  Author Unknown · Guide

  Warning: If you find errors, please raise an issue or contribute a better definition!

  Also filed under Data Visualization, SQL

• Monte Carlo methods Introduction, Course structure, Motivating Examples, Applications

  A. Taylan Cemgil · Slides

  Introduction, Course structure, Motivating Examples, Applications Department of Computer Engineering, Bo˘gazic¸i University, Istanbul, Turkey Instructor: A. Taylan Cemgil Cemgil CMPE 58N Monte Carlo Methods. , Bo˘gazic¸i University, Istanbul Main study materials ▶Handouts, Papers ▶Jun S. Liu, Monte Carlo Strategies in Scientific Computing, 2001, Springer.

  Also filed under Quant Finance, Statistics

• Object Oriented Programming

  Arun Arunisto · Notes

  "Object Oriented Programming" by Arun Arunisto provides a comprehensive overview of the principles and practices of object-oriented programming. It covers key concepts such as classes, objects, inheritance, polymorphism, and encapsulation, offering practical examples and exercises to help readers understand and apply these concepts in real-world programming scenarios. The book is designed for both beginners and experienced programmers looking to deepen their understanding of object-oriented design and implementation.

  Also filed under Computer Science, Software Engineering

• Papers on Topology

  Henri Poincaré · Book

  Introduction 1 Topology before Poincar´e . 9 Comments on terminology and notation . Second definition of manifold . Oppositely oriented manifolds .

  Also filed under Computer Science, Data Visualization

• Pro Git

  Scott Chacon · Book

  "Pro Git" is the definitive guide to Git, the powerful and widely used version control system. Written by Scott Chacon, one of the early contributors to Git, the book provides a comprehensive, hands-on approach to learning Git, from basic concepts to advanced workflows. It is an essential resource for developers, software engineers, and teams looking to improve their version control practices. Key Topics Covered: 1. Introduction to Git: The history of Git and why it was created. Understanding distributed version control and how Git differs from other systems. 2. Git Basics: Installing and setting up Git on different platforms. Creating repositories, cloning, committing, and working with branches. Basic Git commands like git add, git commit, git status, and git log. 3. Branching and Merging: The power of branching in Git and best practices for feature branches. Merging strategies: fast-forward merges, three-way merges, and conflict resolution. 4. Working with Remote Repositories: Setting up and using GitHub, GitLab, and Bitbucket for collaboration. Pushing, pulling, and synchronizing changes with git push and git pull. 5. Git Advanced Features: Rebasing vs. merging: when to use each approach. Interactive rebasing (git rebase -i) for cleaning up commit history. Stashing changes with git stash for temporary work storage. Using git bisect for debugging with binary search. 6. Git Internals: Understanding how Git stores data using objects, trees, and commits. Exploring the .git directory structure. How Git compresses and optimizes repository storage. 7. Collaborative Workflows: Best practices for working in a team using Git. Forking, pull requests, and code reviews. Using Git in continuous integration (CI) and DevOps pipelines. 8. Customizing Git & Automation: Writing custom Git hooks for automating workflows. Configuring .gitconfig to personalize Git behavior. Integrating Git with other tools and platforms. With clear explanations, real-world examples, and hands-on exercises, "Pro Git" is the ultimate guide to mastering Git for version control and software development.

  Also filed under Software Engineering, Computer Science

• Python 200 Things Every Beginner Should Know

  Author Unknown · Book

  Purpose Chapter 2 for beginners 1. Python uses indentation for code blocks 2. Variables are dynamically typed 3. Using snake_case Naming Conventions 4.

  Also filed under Python, Computer Science

• Python Essentials 1

  The OpenEDG Python Institute · Book

  About this eBook While ePUB is an open standard widely employed for the publication of electronic books, support and features may vary from one device to another. Every effort has been made to ensure that this book will display faithfully on all devices, but it may be necessary to adjust the settings of your particular device for optimum readability. There are many examples of code employed throughout this book. Due to the flowing nature of text in the ePUB format, this code, which is written line by line, may not always display correctly.

  Also filed under Python, Data Visualization

• Python for Science and Engineering

  Hans-Petter Halvorsen · Book

  Preface Python is a popular programming language, and it is one of the most used pro- gramming languages today. Python works on all the main platforms and operating systems used today, such Windows, macOS, and Linux. Python is a multi-purpose programming language, which can be use for simu- lation, creating web pages, communicate with database systems, etc. Here you can download the software, download code examples, etc.

  Also filed under Python, Computer Science

• Python makes programming fun!

  Mike McGrath · Book

  "Python Makes Programming Fun!" by Mike McGrath is an engaging introduction to Python, designed to make learning to program an enjoyable and accessible experience for beginners. The book provides a step-by-step approach to Python programming, ensuring that readers build a solid foundation in coding while having fun with practical examples and projects. Key Highlights: Beginner-Friendly Approach: The book is designed for those with little or no prior programming experience, making it an excellent resource for students, hobbyists, and aspiring developers who want to explore Python in a simple and engaging way. Core Python Concepts Explained Clearly: Readers are introduced to fundamental programming concepts such as variables, data types, loops, conditional statements, functions, and error handling. Each topic is explained in an easy-to-follow manner with practical examples. Hands-On Coding with Fun Projects: The book encourages hands-on learning by guiding readers through small, enjoyable coding projects. These include creating simple games, automating tasks, and building interactive programs that make learning Python an exciting experience. Graphical and Interactive Programming: In addition to traditional programming exercises, the book covers how to use Python libraries such as Tkinter for GUI development and turtle graphics to create visual projects, making programming more engaging for beginners. Logical Thinking and Problem-Solving Skills: By working through various challenges and exercises, readers develop logical thinking and problem-solving skills that are essential for programming in any language. Python’s Versatility and Real-World Applications: The book highlights Python's broad applications, from web development to data science, showing how learning Python can open doors to various career opportunities. Simple, Practical, and Fun Learning Experience: The author emphasizes a fun and enjoyable learning journey, making programming less intimidating and more rewarding. The use of humor, interactive examples, and step-by-step instructions ensures that readers stay engaged.

  Also filed under Python, Data Visualization

• Python Mastery

  Chloe Annable · Book

  "Python Mastery" by Chloe Annable is a comprehensive guide designed to elevate readers from beginner to expert in Python programming. It covers fundamental concepts, advanced techniques, and practical applications, providing hands-on exercises and real-world examples to ensure mastery of the language.

  Also filed under Python, Data Visualization

• Python Programming

  Dylan Penny · Book

  "Python Programming" by Dylan Penny is a comprehensive guide that introduces readers to the fundamentals of Python, covering essential concepts such as data types, control structures, functions, and modules. The book progresses to more advanced topics like object-oriented programming, file handling, and libraries, providing practical examples and exercises to reinforce learning. It is designed for both beginners and those looking to enhance their Python skills for real-world applications.

  Also filed under Python, Computer Science

• Python Tutorial (Codes)

  Mustafa Germec · Book

  "Python Tutorial (Codes)" by Mustafa Germec, PhD, is a comprehensive guide aimed at both beginners and intermediate learners seeking to enhance their Python programming skills. The tutorial is structured into 21 chapters, each focusing on a specific aspect of Python, providing detailed explanations and practical code examples. Key Features: Foundational Concepts: The initial chapters introduce the basics of Python, including data types, strings, lists, tuples, sets, and dictionaries. These sections lay the groundwork for understanding how data is stored and manipulated in Python. Control Structures: Subsequent chapters delve into conditions and loops, explaining how to control the flow of a Python program using conditional statements and iterative processes. Functions and Exception Handling: The tutorial covers the creation and utilization of functions for modular programming and discusses exception handling to manage errors gracefully. Object-Oriented Programming (OOP): An in-depth exploration of classes and objects is provided, illustrating the principles of OOP and how they are implemented in Python. File Operations: Chapters on reading and writing files equip readers with the skills to handle file input and output operations, essential for data processing tasks. Advanced Topics: The latter sections introduce advanced concepts such as decorators, generators, lambda functions, list comprehensions, and the use of the math module. These topics are crucial for writing efficient and Pythonic code. Each chapter is designed to build upon the previous ones, ensuring a cohesive learning experience. The inclusion of practical code snippets throughout the tutorial allows readers to apply concepts immediately, reinforcing their understanding through hands-on practice. This tutorial is particularly beneficial for individuals aiming to solidify their Python programming abilities, offering a structured approach to both fundamental and advanced topics. By following this guide, readers can develop a robust foundation in Python, preparing them for more complex programming challenges.

  Also filed under Python, Computer Science

• RoadMap Of Mathematics

  Hayder Zaeem · Report

  4 ○Modules ○Algebras ○Galois Theory: Study of symmetries in algebraic equations. ●Boolean Algebra: Algebraic structure used in logic and computer science.

  Also filed under Data Visualization, Computer Science

• The Analytics Setup Guidebook

  Huy Nguyen, Ha Pham, Cedric Chin · Book

  "The Analytics Setup Guidebook," authored by Huy Nguyen, Ha Pham, and Cedric Chin, serves as a practical manual for constructing modern, scalable analytics and business intelligence (BI) systems. Targeted primarily at technical team members embarking on setting up analytics infrastructures, the book offers a structured approach to understanding and implementing contemporary data analytics architectures. Key Concepts Covered: High-Level Overview of Analytics Systems: The guidebook introduces the components of modern analytics stacks, emphasizing their interconnections and roles in data processing pipelines. Data Consolidation: It discusses strategies for centralizing data from diverse sources, highlighting best practices in Extract, Load, Transform (ELT) processes and the rationale behind the industry's shift from traditional Extract, Transform, Load (ETL) methods. Data Transformation and Modeling: The book delves into transforming and modeling data to align with business objectives, comparing modern approaches to traditional methodologies and offering insights into efficient data structuring. Data Utilization: It explores the application of data through various means such as ad-hoc reporting, data exploration, and visualization, providing guidance on selecting appropriate BI tools and understanding their roles within the analytics ecosystem.

  Also filed under SQL, Data Visualization

• The Hundred-Page Machine Learning Book

  Andriy Burkov · Book

  "The Hundred-Page Machine Learning Book" by Andriy Burkov provides a concise overview of machine learning concepts, techniques, and algorithms. It covers fundamental topics such as supervised and unsupervised learning, model evaluation, and neural networks, offering practical insights and examples to help readers understand and apply machine learning in real-world scenarios.

  Also filed under Machine Learning, Computer Science

• The legal implications of Generative AI

  Donna Bartlett, Willem-Jan Cosemans, Matt Saunders, Till Contzen, Klaus Gresbrand, Maria-Alexandra Papoutsi, Pietro Boccaccini, Peggy Anstett, Bruce Braude, Richard Reeve-Young · Report

  The book explores the legal challenges and considerations surrounding the use of generative AI technologies. It examines issues such as intellectual property rights, data privacy, liability, and regulatory frameworks. The authors provide insights into how these technologies impact various industries and offer guidance on navigating the evolving legal landscape to ensure compliance and mitigate risks.

  Also filed under AI, Machine Learning

• The Python Handbook

  Flavio Copes · Book

  "The Python Handbook" by Flavio Copes is a comprehensive guide for beginners and experienced programmers alike, covering Python programming fundamentals, data structures, functions, modules, and libraries. It provides practical examples and exercises to help readers understand and apply Python concepts effectively.

  Also filed under Python, Computer Science

• Think Python

  Allen B. Downey · Book

  Downey Think Python by Allen B. Downey Copyright © 2024 Allen Downey. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.

  Also filed under Python, Data Visualization

• Topologies of Reasoning: Demystifying Chains, Trees, and Graphs of Thoughts

  Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwasniewski, Jurgen Muller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Onur Mutlu,Torsten Hoefler · Report

  "Topologies of Reasoning: Demystifying Chains, Trees, and Graphs of Thoughts" is a comprehensive study by Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Onur Mutlu, and Torsten Hoefler, published in January 2024. The paper explores structured prompting techniques in large language models (LLMs), focusing on how different reasoning topologies—chains, trees, and graphs—can enhance model performance across various tasks. Key Highlights: Structured Prompting Techniques: The study introduces paradigms like Chain-of-Thought, Tree of Thoughts, and Graph of Thoughts, which guide LLM reasoning through explicit structures, leading to improved task-solving capabilities. Taxonomy of Reasoning Topologies: The authors present a taxonomy categorizing structure-enhanced LLM reasoning schemes, analyzing aspects such as topology class, scope, representation, and execution algorithms. Comparative Analysis: By dissecting existing prompting schemes, the paper evaluates how design choices influence performance metrics like accuracy, latency, and cost, providing insights into effective prompting strategies. Theoretical Insights and Future Challenges: The paper discusses the interplay between prompting techniques and other LLM components, highlighting research challenges and suggesting directions for future advancements in prompt engineering. This work serves as a valuable resource for understanding and advancing structured prompting methods in LLMs, offering a foundation for developing more efficient and effective language models.

  Also filed under AI, Machine Learning

• Unlocking Artificial Intelligence

  Christopher Mutschler, Christian Münzenmayer, Norman Uhlmann & Alexander Martin · Book

  This book is an open access publication. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc.

  Also filed under AI, Machine Learning

• Vector Calculus

  Michael Corral · Book

  About the author: Michael Corral is an Adjunct Faculty member of the Department of Mathematics at Schoolcraft College. in Mathematics from the University of California at Berkeley, and received an M.A. in Industrial & Operations Engineering from the University of Michigan. This text was typeset in LATEX 2ε with the KOMA-Script bundle, using the GNU Emacs text editor on a Fedora Linux system.

  Also filed under Quant Finance, Data Visualization

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