Category: Computer Science

Price: $69.99 - $66.49
(as of May 05, 2024 11:42:35 UTC – Details)

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project’s success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.

Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to:

Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more

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Sharing the knowledge of experts

O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

Publisher ‏ : ‎ O’Reilly Media; 1st edition (May 21, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 257 pages
ISBN-10 ‏ : ‎ 1098136209
ISBN-13 ‏ : ‎ 978-1098136208
Item Weight ‏ : ‎ 14.7 ounces
Dimensions ‏ : ‎ 7 x 0.55 x 9.19 inches

Price: $44.99 - $30.72
(as of Apr 29, 2024 03:39:21 UTC – Details)

An easy-to-follow, illustrated guide to learning the geometry nodes editor and various other facets of geometry nodes through simple exercises that progress to more challenging projects

Purchase of the print or Kindle book includes a free PDF eBook

Key FeaturesDevelop a creative mathematical thinking of the modeling workflowUnderstand how Blender and geometry nodes store and manage the data that you are handlingLearn different scatter methods and how to use themBook Description

For anyone working in the computer graphics industry, understanding how to use Blender’s new geometry nodes tools to manipulate and generate 3D geometry in a node-based workflow is an essential skill. In this book, you’ll learn how to use the basic and intermediate features of geometry nodes that are a crucial part of your Blender roadmap.

You’ll start by understanding the different node inputs and outputs followed by the basic nodes you’ll need throughout your geometry nodes projects. The book will show you how the node system works and enable you to put your newfound knowledge to use through exercises that involve modifying curves, meshes, and more. You’ll work on a range of interesting projects such as creating a procedural plant, where you’ll use nodes to generate the intricate details and variations of a plant in a procedural manner, and a spiderweb generator to refine your skills of cleaning up a node tree. Finally, you’ll build a procedural LED panel using geometry nodes to generate the look of an LED panel.

By the end of this book, you’ll be able to overcome any geometry node issue confidently and make complicated geometry node trees exactly how you need them.

What you will learnDiscover the different node inputs and outputs that geometry nodes have to offerGet the hang of the flow of the geometry node systemUnderstand the common nodes you’ll be using along with their functions in the geometry node editorModify basic mesh primitives using the node system inside BlenderScatter and modify objects aligned onto a curveBecome familiar with the more advanced nodes in the geometry nodes systemLink geometry and material nodes editors using named attributesImplement your new-found knowledge of nodes in real-world projectsWho this book is for

If you are a CG Artist or follow modeling careers like that of an environment artist or even a CG generalist in the cinematography industry and you are looking to get into learning a node-based modeling workflow using Geometry Nodes in Blender, this is the perfect book for you. You will need a basic knowledge of the fundamentals of Blender, for example, knowing the specific workflow of material nodes and being able to apply this knowledge to your projects. To get the most out of this book, you should have a basic understanding of Blender’s shortcut system and some modeling experience.

Table of ContentsAn Introduction to Geometry NodesUnderstanding the Functionalities of Basic NodesMust-Have Add-Ons for Building Node TreesMaking Use of Node PrimitivesDistributing Instances onto a MeshWorking with the Spreadsheet in BlenderCreating and Modifying Text in the Geometry Node EditorEditing Curves with NodesManipulating a Mesh Using Geometry NodesCreating a Procedural Plant GeneratorCreating a Procedural Spiderweb GeneratorConstructing a Procedural LED PanelTips and Tricks for the Geometry Node EditorTroubleshooting the Most Common Problems in Geometry Nodes

Publisher ‏ : ‎ Packt Publishing (March 17, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 282 pages
ISBN-10 ‏ : ‎ 1804612553
ISBN-13 ‏ : ‎ 978-1804612552
Item Weight ‏ : ‎ 1.1 pounds
Dimensions ‏ : ‎ 9.25 x 7.52 x 0.59 inches

Price: $49.99 - $32.31
(as of Apr 28, 2024 14:20:21 UTC – Details)

Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems

Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain

Key FeaturesThis second edition delves deeper into key machine learning topics, CI/CD, and system designExplore core MLOps practices, such as model management and performance monitoringBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source toolsBook Description

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field.

The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You’ll explore the key steps of the ML development lifecycle and create your own standardized “model factory” for training and retraining of models. You’ll learn to employ concepts like CI/CD and how to detect different types of drift.

Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques.

With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.

What you will learnPlan and manage end-to-end ML development projectsExplore deep learning, LLMs, and LLMOps to leverage generative AIUse Python to package your ML tools and scale up your solutionsGet to grips with Apache Spark, Kubernetes, and RayBuild and run ML pipelines with Apache Airflow, ZenML, and KubeflowDetect drift and build retraining mechanisms into your solutionsImprove error handling with control flows and vulnerability scanningHost and build ML microservices and batch processes running on AWSWho this book is for

This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you’re not a developer but want to manage or understand the product lifecycle of these systems, you’ll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.

Table of ContentsIntroduction to ML EngineeringThe Machine Learning Development ProcessFrom Model to Model Factory Packaging UpDeployment Patterns and ToolsScaling UpDeep Learning, Generative AI, and LLMOps Building an Example ML MicroserviceBuilding an Extract, Transform, Machine Learning Use Case

From the Publisher

Practical problem-solving

Apply your acquired skills and knowledge to solve real-world problems with examples and hands-on exercises. You can use your practical knowledge to solve production issues and scale them to any size based on your needs.

Deep dive into the fundamentals

You’ll learn what good ML engineering processes look like. You’ll discover how to choose, train, and even automate your models. You’ll gain an understanding of what aspects of software engineering you should bring to ML, and explore all the newest libraries and tools available.

The book focuses on teaching you the fundamentals and introducing you to all the key aspects needed for a successful ML engineering career. You’ll explore best practices, tips, and tricks for software development.

Explore best practices for machine learning engineering Automate training and deployment for your ML processes Build wrapper libraries for encapsulating your data science and ML logic and solutions

Learn > Apply > Master

Learn like a professional

Cover the basics, learn what ML engineering is, how to do it well, and how to design your own process.

Start small, but go big

Put your knowledge to the test by training, deploying, and scaling your solutions.

Test yourself

Work through real-world end-to-end scenarios to really cement your new skills.

Get to grips with the biggest libraries and packages

This latest edition of Machine Learning Engineering with Python is packed with new information and techniques. Building on the solid foundation of the first edition, more technical depth has been introduced with the example chapters revamped completely.

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Price

$32.31$32.31 $43.99$43.99

Tools:
AWS + Open-Source Software AWS

Models:
Traditional ML models + LLMs + DLMs Traditional ML models

Approach:
How+Why How to

Chapters:
9 8

Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (August 31, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 462 pages
ISBN-10 ‏ : ‎ 1837631964
ISBN-13 ‏ : ‎ 978-1837631964
Item Weight ‏ : ‎ 1.76 pounds
Dimensions ‏ : ‎ 9.25 x 7.52 x 0.93 inches

Price: $49.99 - $37.59
(as of Apr 28, 2024 10:56:21 UTC – Details)

Summary

Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively.

About the book

Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations.

What’s inside

Statistical analysis for business pros
Effective data presentation
The most useful R tools
Interpreting complicated predictive models

About the reader

You’ll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language.

About the author

Nina Zumel and John Mount founded a San Francisco–based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science.

Publisher ‏ : ‎ Manning; 2nd edition (December 7, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 483 pages
ISBN-10 ‏ : ‎ 1617295876
ISBN-13 ‏ : ‎ 978-1617295874
Item Weight ‏ : ‎ 2.29 pounds
Dimensions ‏ : ‎ 7.38 x 1.2 x 9.25 inches

Price: $41.49
(as of Apr 14, 2024 22:51:27 UTC – Details)

This minibook is a comprehensive guide for Python developers who want to learn how to build applications with large language models. Authors Olivier Caelen and Marie-Alice Blete cover the main features and benefits of GPT-4 and ChatGPT and explain how they work. You’ll also get a step-by-step guide for developing applications using the GPT-4 and ChatGPT Python library, including text generation, Q&A, and content summarization tools.

Written in clear and concise language, Developing Apps with GPT-4 and ChatGPT includes easy-to-follow examples to help you understand and apply the concepts to your projects. Python code examples are available in a GitHub repository, and the book includes a glossary of key terms. Ready to harness the power of large language models in your applications? This book is a must.

You’ll learn:

The fundamentals and benefits of ChatGPT and GPT-4 and how they workHow to integrate these models into Python-based applications for NLP tasksHow to develop applications using GPT-4 or ChatGPT APIs in Python for text generation, question answering, and content summarization, among other tasksAdvanced GPT topics including prompt engineering, fine-tuning models for specific tasks, plug-ins, LangChain, and more

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Sharing the knowledge of experts

O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

Publisher ‏ : ‎ Oreilly & Associates Inc; 1st edition (October 3, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 146 pages
ISBN-10 ‏ : ‎ 1098152484
ISBN-13 ‏ : ‎ 978-1098152482
Item Weight ‏ : ‎ 9.6 ounces
Dimensions ‏ : ‎ 7 x 0.5 x 9.25 inches