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Osmani Addy: Building Web Apps with Bolt

Building Web Apps with Bolt

Unlock the Future of AI-First Development

"This book covers how to build software products with machine-learning components and provides a holistic view of ML systems built to achieve safety, security, usability, fairness in the real world"--

A practical and innovative textbook detailing how to build real-world software products with machine learning components, not just models. Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of building systems that are usable, reliable, scalable, and safe within the context of real-world conditions of uncertainty, incomplete information, and resource constraints. Based on the author’s popular class at Carnegie Mellon, this pioneering book integrates foundational knowledge in software engineering and machine learning to provide the holistic view needed to create not only prototype models but production-ready systems. •&nbsp;&nbsp;&nbsp;Integrates coverage of cutting-edge research, existing tools, and real-world applications •&nbsp;&nbsp;&nbsp;Provides students and professionals with an engineering view for production-ready machine learning systems •&nbsp;&nbsp;&nbsp;Proven in the classroom •&nbsp;&nbsp;&nbsp;Offers supplemental resources including slides, videos, exams, and further readings

From Models to Products

Machine Learning in Production

This book provides an introduction to mathematics and programming that is specifically designed for use by those engaged in the humanities. Linking mathematical concepts and computational skills, the chapters in this book explore humanistic questions from diverse fields, such as art, history and literature.

Mathematics and Programming in Context

Computational Literacy for the Humanities

A one-stop-shop for all the math you should have learned for your programming career. Every great programming challenge has mathematical principles at its heart. Whether you’re optimizing search algorithms, building physics engines for games, or training neural networks, success depends on your grasp of core mathematical concepts.&nbsp; In Math for Programming, you’ll master the essential mathematics that will take you from basic coding to serious software development. You’ll discover how vectors and matrices give you the power to handle complex data, how calculus drives optimization and machine learning, and how graph theory leads to advanced search algorithms. Through clear explanations and practical examples, you’ll learn to: <ul><li>Harness linear algebra to manipulate data with unprecedented efficiency</li><li>Apply calculus concepts to optimize algorithms and drive simulations</li><li>Use probability and statistics to model uncertainty and analyze data</li><li>Master the discrete mathematics that powers&nbsp;modern data structures</li><li>Solve dynamic problems through differential equations</li></ul> Whether you’re seeking to fill gaps in your mathematical foundation or looking to refresh your understanding of core concepts, Math for Programming will turn complex math into a practical tool you’ll use every day.

A one-stop-shop for all the math you should have learned for your programming career. Every great programming challenge has mathematical principles at its heart. Whether you’re optimizing search algorithms, building physics engines for games, or training neural networks, success depends on your grasp of core mathematical concepts.&nbsp; In Math for Programming, you’ll master the essential mathematics that will take you from basic coding to serious software development. You’ll discover how vectors and matrices give you the power to handle complex data, how calculus drives optimization and machine learning, and how graph theory leads to advanced search algorithms. Through clear explanations and practical examples, you’ll learn to: <ul><li>Harness linear algebra to manipulate data with unprecedented efficiency</li><li>Apply calculus concepts to optimize algorithms and drive simulations</li><li>Use probability and statistics to model uncertainty and analyze data</li><li>Master the discrete mathematics that powers&nbsp;modern data structures</li><li>Solve dynamic problems through differential equations</li></ul> Whether you’re seeking to fill gaps in your mathematical foundation or looking to refresh your understanding of core concepts, Math for Programming will turn complex math into a practical tool you’ll use every day.

Math for Programming

The purpose of this book was to avail students of the many different reasons and back-stories equated with the advent of interaction design and its effect on our world. The main goal of this book is to help you take your first steps in that new direction.

The purpose of this book was to avail students of the many different reasons and back-stories equated with the advent of interaction design and its effect on our world. The main goal of this book is to help you take your first steps in that new direction.

How our need to communicate created a new economy, society, and individual: what will be the effects of artificial intelligence and quantum computing?

rock • paper • pixels

Web browsers are the most common and widely-used platform there is, and this book is the essential description of how they work and how that impacts web developers and other software engineers whose work touches the web.

Web browsers are the most common and widely-used platform there is, and this book is the essential description of how they work and how that impacts web developers and other software engineers whose work touches the web.

Web Browser Engineering

"This is the second edition of the essential reference and guidebook for SuperCollider, a powerful, flexible, open-source, cross-platform audio programming language"--

A comprehensive update of the essential reference to SuperCollider, with new material on machine learning, musical notation and score making, SC Tweets, alternative editors, parasite languages, non-standard synthesis, and the cross-platform GUI library. SuperCollider is one of the most important domain-specific audio programming languages, with wide-ranging applications across installations, real-time interaction, electroacoustic pieces, generative music, and audiovisuals. Now in a comprehensively updated new edition, The SuperCollider Book remains the essential reference for beginners and advanced users alike, offering students and professionals a user-friendly guide to the language’s design, syntax, and use. Coverage encompasses the basics as well as explorations of advanced and cutting-edge topics including microsound, sonification, spatialization, non-standard synthesis, and machine learning. Second edition highlights: •&nbsp;&nbsp;&nbsp;New chapters on musical notation and score making, machine learning, SC Tweets, alternative editors, parasite languages, non-standard synthesis, SuperCollider on small computers, and the cross-platform GUI library •&nbsp;&nbsp;&nbsp;New tutorial on installing, setting up, and running the SuperCollider IDE •&nbsp;&nbsp;&nbsp;Technical documentation of implementation and information on writing your own unit generators •&nbsp;&nbsp;&nbsp;Diverse artist statements from international musicians •&nbsp;&nbsp;&nbsp;Accompanying code examples and extension libraries

The SuperCollider Book, second edition

An essential companion to The Rust Programming Language, 2nd Edition, offering targeted exercises to enhance beginners' skills in reading and writing Rust code. The Rust Programming Language Workbook provides short coding exercises inspired by the rustlings project and organized by topic. The workbook starts with concepts common to many programming languages today like variables, data types, and functions then moves on to important Rust concepts like move semantics, structs, and enums. Short introductions of the concepts are followed by exercises that are intentionally broken. Readers need to change the code or add code to fix each exercise. The workbook also provides hints and example solutions to help beginners get unstuck.

The Rust Programming Language Workbook

Messy systems slow progress and increase costs--but tidying up isn't just an individual effort, it's a team responsibility. In this essential guide, author Kent Beck offers a practical framework for improving system design collaboratively. Building on the ideas from Tidy First?, this book shows teams how to balance changes to a system's behavior with improvements to its structure, ensuring smoother development, faster progress, and more effective collaboration. With a focus on teamwork, this book emphasizes not just fixing code but strengthening the human relationships that sustain system evolution. Through thoughtful insights, real-world examples, and actionable practices, Beck teaches teams to alternate between tidying and building, minimizing risk while maintaining momentum. Learn how to make large changes safely, refine your team dynamics, and clear the path to sustainable system improvements with approaches that prioritize both people and code. Whether you're an individual contributor or leading a design team, you'll be equipped to design better systems--together. <ul> <li>Understand the difference between changes to system behavior and structure</li> <li>Learn the power of alternating investments between tidying and building</li> <li>Apply small, safe steps to execute large system changes</li> <li>Strengthen team relationships to sustain healthy design practices</li> <li>Prepare for design challenges shaped by diverging incentives and human collaboration</li></ul>

A Team Exercise in Empirical Software Design

Tidy Together

Probability for Deep Learning Quantum provides readers with the first book to address probabilistic methods in the deep learning environment and the quantum technological area simultaneously, by using a common platform: the Many-Sorted Algebra (MSA) view. While machine learning is created with a foundation of probability, probability is at the heart of quantum physics as well. It is the cornerstone in quantum applications. These applications include quantum measuring, quantum information theory, quantum communication theory, quantum sensing, quantum signal processing, quantum computing, quantum cryptography, and quantum machine learning. Although some of the probabilistic methods differ in machine learning disciplines from those in the quantum technologies, many techniques are very similar. Probability is introduced in the text rigorously, in Komogorov’s vision. It is however, slightly modified by developing the theory in a Many-Sorted Algebra setting. This algebraic construct is also used in showing the shared structures underlying much of both machine learning and quantum theory. Both deep learning and quantum technologies have several probabilistic and stochastic methods in common. These methods are described and illustrated using numerous examples within the text. Concepts in entropy are provided from a Shannon as well as a von-Neumann view. Singular value decomposition is applied in machine learning as a basic tool and presented in the Schmidt decomposition. Besides the in-common methods, Born’s rule as well as positive operator valued measures are described and illustrated, along with quasi-probabilities. Author Charles R. Giardina provides clear and concise explanations, accompanied by insightful and thought-provoking visualizations, to deepen your understanding and enable you to apply the concepts to real-world scenarios.

A Many-Sorted Algebra View

Probability for Deep Learning Quantum

This book illustrates statistics with the R programming language, an emerging tool in data science. Filled with techniques for data science and analytics programming, the book is written for students and professionals. It is focused on learning outcomes and problem solving.

This book illustrates statistics with the R programming language, an emerging tool in data science. Filled with techniques for data science and analytics programming, the book is written for students and professionals. It is focused on learning outcomes and problem solving.

Demystifying Probability and Statistics for Data Scientists with R

Looking to accelerate development and build cloud native applications with AWS Cloud Development Kit? Through hands-on projects, you'll learn the basics of AWS CDK, the tool of choice for many of the world's largest technology companies. Informed by real case studies and years of work with enterprise-scale cloud architectures, this book will benefit both novice and advanced cloud developers. It's complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions to help you build a shareable portfolio of completed projects, demonstrating your ability to build cloud infrastructure at scale. You'll explore: <ul> <li>Basic cloud computing concepts, including the AWS Well-Architected Framework </li><li>End-to-end cloud native software and infrastructure as code </li><li>The benefits of a reusable code library to accelerate your projects </li><li>Functional modules of code that can stand alone or build toward an integrated cloud application </li></ul>

Building Cloud Native Applications with Infrastructure-As-Code

Hands-On AWS Cdk

Building Web Apps with Bolt

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