Computer Science
45 curated documents on computer science from the Gyre Research library, each with a summary. Free to read, no signup required.
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.
A guide to infrastructure hardening
Canonical Limited · Guide
summary Cloud technology and virtualisation enable developers and administrators to deploy infrastructure at a pace previously unheard of, which has brought them huge gains and enabled almost anyone to create internet services. However, this facility and flexibility does not mask the underlying need for deployments to be secure. The ever-present threats of ransomware attacks and data breaches make it imperative to lock down systems and prevent attackers from gaining an easy foothold. A fully secure system is made up of many layers, from the hardware to the operating system and the applicati...
Also filed under Software Engineering, SQL
Algorithms Python
Yang Hu · Notes
Algorithms Python YANG HU Simple is the beginning of wisdom. From the essence of practice, this book to briefly explain the concept, and vividly cultivate programming interest , you will learn it easy fast and well.
Also filed under Python, Machine Learning
Applied Data Science
Ian Langmore & Daniel Krasner · Book
26 5.2 Coefficient Estimation: Bayesian Formulation . 29 5.2.2 Ideal Gaussian World . 30 5.3 Coefficient Estimation: Optimization Formulation . 33 5.3.1 The least squares problem and the singular value de- composition .
Also filed under Python, Data Visualization
Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin (Google Brain) · Report
The 2017 NeurIPS paper that introduced the Transformer architecture — the foundation of every modern large language model. Dispensing with recurrence and convolutions entirely, the Transformer uses multi-head self-attention to parallelize sequence modeling. The single most-cited deep learning paper of the last decade.
Also filed under AI, Machine Learning
Automated Machine Learning: Methods, Systems, Challenges
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren (Editors) · Book
An edited volume in The Springer Series on Challenges in Machine Learning, covering methods, systems, and open challenges in automated machine learning (AutoML). Contributions span algorithmic foundations, hyperparameter optimization, neural architecture search, and real-world applications.
Also filed under Machine Learning, AI
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova (Google AI Language) · Report
The 2018 paper introducing BERT, which established the pre-train/fine-tune paradigm that dominated NLP before the GPT era. Masked language modeling + next-sentence prediction on unlabeled text produced representations transferable to a wide range of downstream tasks.
Also filed under AI, Machine Learning
Building Machine Learning Systems with Python
Book
A practical introduction to machine learning using Python, covering NumPy, SciPy, and Matplotlib for data handling; classification (kNN, naive Bayes), clustering, and feature engineering; hands-on examples on the Iris and Seeds datasets; techniques for evaluation and model selection.
Also filed under Python, Machine Learning
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, Math
Data Science from Scratch
Steven Cooper · Book
"Data Science from Scratch" by Steven Cooper is an excellent introduction to the world of data science for beginners and intermediate learners alike. The book focuses on the foundational concepts of data science and offers a practical approach to understanding its key principles, including algorithms, statistical analysis, and machine learning. The author breaks down complex topics into digestible pieces, providing a hands-on, Python-based approach for readers to learn and apply data science concepts from scratch. Key Highlights: Introduction to Data Science: The book begins with an overview of data science, its applications, and its importance in various industries. Cooper introduces the core concepts and workflows in data science, including data collection, cleaning, analysis, visualization, and modeling. He emphasizes the iterative process of working with data and the need for solid foundational knowledge to excel in the field. Python Basics for Data Science: Since the book assumes no prior experience in data science or programming, it starts with a primer on Python. The author introduces basic Python concepts, such as variables, loops, conditionals, functions, and data structures (lists, dictionaries, and tuples). The use of Python as a tool for data science is emphasized, with practical examples to illustrate key programming concepts. Data Collection and Preparation: A large portion of the book focuses on the critical step of data collection and preparation. Cooper walks readers through methods of gathering data from various sources, including APIs and databases. He also discusses techniques for cleaning data, handling missing values, and transforming raw data into a format suitable for analysis. The importance of data quality is stressed, and readers learn how to manage and preprocess data to get meaningful results. Exploratory Data Analysis (EDA): The book introduces exploratory data analysis (EDA) as a vital part of the data science process. Cooper explains how to summarize and visualize data using various Python libraries such as pandas, NumPy, and Matplotlib. Readers learn how to analyze distributions, detect patterns, and identify outliers in their datasets through descriptive statistics and visualizations. Probability and Statistics: The author covers the essential statistical concepts needed for data science, including probability theory, distributions, hypothesis testing, and confidence intervals. The book introduces concepts like mean, median, variance, correlation, and regression analysis to help readers understand how to quantify uncertainty and make inferences about data. Machine Learning: One of the key aspects of data science is machine learning, and the book provides a comprehensive introduction to this area. Cooper discusses supervised and unsupervised learning, explaining key algorithms like linear regression, k-nearest neighbors (KNN), decision trees, and clustering. The author also covers model evaluation techniques, such as cross-validation and confusion matrices, helping readers understand how to assess the performance of machine learning models.
Also filed under Python, Statistics
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 Data Visualization, Math
Data-Driven Innovation in the Creative Industries
Melissa Terras, Vikki Jones, Nicola Osborne & Chris Speed · Book
The exploration of innovation in this collection of essays champions the collaborative potential of creativity and technology and encourages the adoption of data-led creativity to shape the future of the creative economy. A must-read for those looking to drive meaningful change and innovation across the industry.” Lee Walters, CEO of Ffilm Cymru Wales. Formerly Programme Manager of Clwstwr, part of the Creative Industries Clusters Programme “This book brings to life real-world applications of data-led creativity and, importantly, addresses ethical considerations. But what do these mean in t...
Also filed under Data Visualization, Python
Deep Learning for Computer Vision with Python
Adrian Rosebrock · Book
Deep Learning for Computer Vision with Python Practitioner Bundle Dr. Books like this are made possible by the time invested by the authors. First printing, September 2017 To my father, Joe; my wife, Trisha; and the family beagles, Josie and Jemma. Without their constant love and support, this book would not be possible.
Also filed under Machine Learning, Python
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, Math
Deep Neural Networks and Data for Automated Driving
Tim Fingscheidt, Hanno Gottschalk & Sebastian Houben · 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 Machine Learning, AI
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun (Microsoft Research) · Report
The 2015 paper introducing residual connections and ResNet — the architecture that enabled training of networks 100+ layers deep. Residual connections solved the degradation problem in deep networks and became a near-universal building block of modern deep learning.
Also filed under AI, Machine Learning
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, Math
End-to-End Arguments in System Design
Saltzer, Reed, and Clark · Paper
Seminal paper by Saltzer, Reed, and Clark (1984). Access: open-access. Source: https://web.mit.edu/Saltzer/www/publications/endtoend/endtoend.pdf
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, Math
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 Math, Quant Finance
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, Math
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, Math
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton (University of Toronto) · Report
The 2012 AlexNet paper that catalyzed the deep learning revolution. A deep CNN trained on two GPUs reduced the ImageNet top-5 error rate from 26% to 15%, convincing the broader ML community that deep networks were production-ready.
Also filed under AI, Machine Learning
Introduction to Probability for Data Science
Stanley H.Chan · Book
"Introduction to Probability for Data Science" by Stanley H. Chan is an undergraduate-level textbook that emphasizes the integration of data computing and probability theory. The book aims to elucidate the motivations, intuitions, and implications of probabilistic tools used in science and engineering, highlighting their inseparability in modern data science. Key Concepts Covered: Mathematical Foundations: The book begins with essential mathematical concepts such as infinite series, approximations, integration, linear algebra, and basic combinatorics. These topics provide the necessary groundwork for understanding more advanced probabilistic theories. Probability Theory: Chan introduces fundamental probability concepts, including set theory, probability spaces, axioms of probability, and conditional probability. The text delves into independence, Bayes' theorem, and the law of total probability, offering a comprehensive understanding of these foundational principles. Random Variables and Distributions: The book explores discrete and continuous random variables, their probability mass functions (PMFs), probability density functions (PDFs), cumulative distribution functions (CDFs), expectations, moments, variances, and common distributions like Bernoulli, binomial, geometric, Poisson, uniform, exponential, and Gaussian. Joint Distributions and Multidimensional Analysis: Chan discusses joint PMFs and PDFs, marginal distributions, conditional distributions, covariance, correlation coefficients, and transformations of multivariate Gaussian distributions. The text also covers principal-component analysis and its applications. Sample Statistics and Estimation: The book addresses moment-generating and characteristic functions, probability inequalities, the law of large numbers, central limit theorem, regression principles, overfitting, bias-variance trade-off, regularization, and various estimation techniques such as maximum-likelihood estimation, maximum a posteriori estimation, and minimum mean-square estimation. Confidence, Hypothesis Testing, and Advanced Topics: Chan concludes with discussions on confidence intervals, bootstrapping, hypothesis testing, Neyman-Pearson tests, and other advanced topics, providing a holistic view of statistical inference methods used in data science.
Also filed under Statistics, Quant Finance
Introduction to Scientific Programming with Python
Joakim Sundnes · 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 Python, Statistics
Kubernetes Patterns: Reusable Elements for Designing Cloud Native Applications
Bilgin Ibryam & Roland Huß · Book
Kubernetes Patterns: Reusable Elements for Designing Cloud-Native Applications by Bilgin Ibryam and Roland Huß is a detailed guide that explores the design patterns essential for building scalable, reliable, and efficient cloud-native applications on Kubernetes. The book provides reusable solutions to common challenges faced during the design and implementation of Kubernetes-based systems, helping developers and architects optimize their workflows. Key Highlights: Introduction to Kubernetes and Cloud-Native Principles: The book starts with a solid foundation on Kubernetes and cloud-native application design. It introduces the core principles behind Kubernetes, including container orchestration, service management, and microservices architecture, which are fundamental to designing cloud-native applications. Understanding Design Patterns: The central theme of the book is design patterns—reusable solutions to recurring design problems. The authors demonstrate how design patterns are vital for building complex, cloud-native applications that need to scale, remain resilient, and be easily maintained over time. Observability and Monitoring Patterns: Kubernetes provides powerful tools for logging, monitoring, and tracing, and this book outlines patterns to implement observability in cloud-native applications. The authors explore how to monitor clusters, containers, and applications, ensuring that issues can be detected and resolved quickly. Scaling and Resilience Patterns: One of the key advantages of Kubernetes is its ability to scale applications efficiently. This section focuses on patterns to manage scaling horizontally and vertically, as well as ensuring that applications remain resilient to failures through automated self-healing processes, replica sets, and distributed systems. Security Patterns: Security is an essential part of designing cloud-native applications. The book outlines patterns for securing applications on Kubernetes, including role-based access control (RBAC), network policies, and managing sensitive data with Kubernetes secrets. Resource Management Patterns: Kubernetes allows for fine-grained control over resources, such as CPU and memory allocation. The authors explain patterns for managing resources effectively, ensuring that containers get the necessary resources while avoiding conflicts or bottlenecks. CI/CD and DevOps Integration: The book covers patterns for integrating Kubernetes with continuous integration and continuous deployment (CI/CD) pipelines. By automating testing and deployment workflows, developers can speed up release cycles and improve the quality and reliability of applications. Real-World Use Cases: Throughout the book, the authors provide practical case studies and real-world examples that demonstrate how the patterns discussed are implemented in live Kubernetes clusters. These case studies show how companies leverage Kubernetes to build resilient, scalable systems in production.
Also filed under Software Engineering, Python
Large Language Models in Cybersecurity
Andrei Kucharavy, Octave Plancherel, Valentin Mulder, Alain Mermoud & Vincent Lenders · 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 Machine Learning, Risk Management
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.
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.
Lecture Notes On Artificial Intelligence
Prashanta Kumar Patra · Book
"Lecture Notes on Artificial Intelligence" is a structured and concise guide designed to introduce the fundamental concepts of Artificial Intelligence (AI). The book is tailored for students, researchers, and professionals looking to understand the theoretical foundations and practical applications of AI. It provides a systematic overview of AI methodologies, techniques, and algorithms, making it an essential resource for academic learning and real-world problem-solving. Key Topics Covered: Introduction to Artificial Intelligence: Definition, history, and evolution of AI, along with its impact on various industries. Problem Solving & Search Algorithms: Covers uninformed search (BFS, DFS), heuristic search (A), and optimization techniques*. Knowledge Representation & Reasoning: Explores logical reasoning, semantic networks, ontologies, and rule-based systems. Machine Learning Fundamentals: Introduces supervised, unsupervised, and reinforcement learning, along with key algorithms like decision trees and neural networks. Natural Language Processing (NLP): Discusses text processing, sentiment analysis, and AI-driven language models. Expert Systems & Fuzzy Logic: Explains the design and implementation of expert systems and how fuzzy logic enhances decision-making. Neural Networks & Deep Learning: Covers perceptrons, backpropagation, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). AI Ethics & Future Trends: Discusses ethical considerations, AI governance, and the future impact of AI on society. With clear explanations, mathematical foundations, and practical examples, this book serves as a valuable resource for understanding AI principles and preparing for advanced studies or careers in artificial intelligence.
Also filed under AI, Machine Learning
Machine Learning with Python
Tutorials Point · Book
A comprehensive tutorial from Tutorials Point covering the fundamentals of machine learning using Python. It introduces key ML concepts, the Python ecosystem for data science, methods and tasks suited for machine learning, data loading and preprocessing, and practical implementation of algorithms using NumPy, Scikit-learn, SciPy, and Matplotlib.
Also filed under Machine Learning, Python
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 Math, Software Engineering
On Computable Numbers
Alan M. Turing · Paper
Seminal paper by Alan M. Turing (1936). Access: open-access. Source: https://www.cs.virginia.edu/~robins/Turing_Paper_1936.pdf
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 Math, Data Visualization
Pattern Recognition and Machine Learning
Christopher M. Bishop · Book
Pattern Recognition and Machine Learning by Christopher M. Bishop is a fundamental textbook that provides a comprehensive introduction to statistical pattern recognition and machine learning. The book is well-regarded for its probabilistic approach, making it an essential resource for students, researchers, and practitioners in data science, artificial intelligence, and related fields. Key Highlights: Foundations of Pattern Recognition Introduces the Bayesian approach to pattern recognition, emphasizing probability distributions and decision theory. Covers fundamental concepts such as linear regression, classification, and generative models. Probability and Machine Learning Discusses core probability distributions, including Gaussian, Multivariate Gaussian, and Exponential families. Explains Bayesian inference, its role in machine learning, and its advantages over frequentist methods. Supervised Learning Techniques Covers linear and logistic regression, support vector machines (SVMs), and neural networks. Introduces regularization techniques to prevent overfitting. Unsupervised Learning and Clustering Explores principal component analysis (PCA), mixture models, and expectation-maximization (EM) algorithms. Discusses clustering techniques such as k-means and Gaussian mixture models (GMMs). Graphical Models and Probabilistic Inference Introduces Bayesian networks and Markov random fields for modeling dependencies between variables. Covers approximate inference techniques, including variational inference and Markov chain Monte Carlo (MCMC). Kernel Methods and Advanced Topics Discusses kernel-based learning, including Gaussian processes and support vector machines (SVMs). Covers hidden Markov models (HMMs) and their applications in sequential data analysis.
Also filed under Machine Learning, Statistics
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, Math
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.
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.
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.
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.
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 Math, Data Visualization
The Byzantine Generals Problem
Leslie Lamport, Robert Shostak, and Marshall Pease · Paper
Seminal paper by Leslie Lamport, Robert Shostak, and Marshall Pease (1982). Access: open-access. Source: https://lamport.azurewebsites.net/pubs/byz.pdf
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, Math
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.
Time, Clocks, and the Ordering of Events in a Distributed System
Leslie Lamport · Paper
Seminal paper by Leslie Lamport (1978). Access: open-access. Source: https://lamport.azurewebsites.net/pubs/time-clocks.pdf
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