Download or read book Getting started with FastApi written by Andrés Cruz Yoris and published by Andres Cruz. This book was released on with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: FastAPI is a great web framework for creating web APIs with Python; It offers us multiple features with which it is possible to create modular, well-structured, scalable APIs with many options such as validations, formats, typing, among others. When you install FastAPI, two very important modules are installed: Pydantic that allows the creation of models for data validation. Starlette, which is a lightweight ASGI tooltip, used to create asynchronous (or synchronous) web services in Python. With these packages, we have the basics to create APIs, but we can easily extend a FastAPI project with other modules to provide the application with more features, such as the database, template engines, among others. FastAPI is a high-performance, easy-to-learn, start-up framework; It is ideal for creating all kinds of sites that not only consist of APIs, but we can install a template manager to return complete web pages. This book is mostly practical, we will learn the basics of FastAPI, knowing its main features based on a small application that we will extend chapter after chapter and whose content you can see below: Chapter 1: We present some essential commands to develop in FastApi , we will prepare the environment and we will give an introduction to the framework . Chapter 2: One of the main factors in FastApi is the creation of resources for the API through functions, in this section we will deal with the basics of this, introducing routing between multiple files as well as the different options for the arguments and parameters of these routes. Chapter 3: In this section, learn how to handle HTTP status codes from API methods and also handle errors/exceptions from API methods. Chapter 4: In this section we will see how to create sample data to use from the automatic documentation that FastAPI offers for each of the API methods. Chapter 5: In this chapter we will see how to implement the upload of files, knowing the different existing variants in FastAPI. Chapter 6: In this chapter we will see how to connect a FastAPI application to a relational database such as MySQL. Chapter 7: In this chapter we will see installing and using a template engine in Python, specifically Jinja, with which we can return responses in HTML format. Chapter 8: In this chapter we will see installing and using a template engine in Python, specifically Jinja, with which we can return responses in HTML format. Chapter 9: In this chapter we will learn how to use dependencies. Chapter 10: In this chapter we will see how to use middleware to intercept requests to API methods and execute some procedure before the request or after generating the response. Chapter 11: In this chapter we will see how to create a user module, to register users, login, generate access tokens and logout. Chapter 12: In this chapter we will learn about some particularities and functionalities of FastAPI such as the use of annotations and the Ellipsis operator. Chapter 13: In this chapter we will see how to implement unit tests. Chapter 14: In this chapter we will know some general aspects applied to FastAPI.

Download or read book Building Python Web APIs with FastAPI written by Abdulazeez Abdulazeez Adeshina and published by Packt Publishing Ltd. This book was released on 2022-07-29 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover FastAPI features and best practices for building and deploying high-quality web APIs from scratch Key Features • A practical guide to developing production-ready web APIs rapidly in Python • Learn how to put FastAPI into practice by implementing it in real-world scenarios • Explore FastAPI, its syntax, and configurations for deploying applications Book Description RESTful web services are commonly used to create APIs for web-based applications owing to their light weight and high scalability. This book will show you how FastAPI, a high-performance web framework for building RESTful APIs in Python, allows you to build robust web APIs that are simple and intuitive and makes it easy to build quickly with very little boilerplate code. This book will help you set up a FastAPI application in no time and show you how to use FastAPI to build a REST API that receives and responds to user requests. You'll go on to learn how to handle routing and authentication while working with databases in a FastAPI application. The book walks you through the four key areas: building and using routes for create, read, update, and delete (CRUD) operations; connecting the application to SQL and NoSQL databases; securing the application built; and deploying your application locally or to a cloud environment. By the end of this book, you'll have developed a solid understanding of the FastAPI framework and be able to build and deploy robust REST APIs. What you will learn • Set up a FastAPI application that is fully functional and secure • Understand how to handle errors from requests and send proper responses in FastAPI • Integrate and connect your application to a SQL and NoSQL (MongoDB) database • Perform CRUD operations using SQL and FastAPI • Manage concurrency in FastAPI applications • Implement authentication in a FastAPI application • Deploy a FastAPI application to any platform Who this book is for This book is for Python developers who want to learn FastAPI in a pragmatic way to create robust web APIs with ease. If you are a Django or Flask developer looking to try something new that's faster, more efficient, and produces fewer bugs, this FastAPI Python book is for you. The book assumes intermediate-level knowledge of Python programming.

Download or read book Microservice APIs written by Jose Haro Peralta and published by Simon and Schuster. This book was released on 2023-03-07 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Strategies, best practices, and patterns that will help you design resilient microservices architecture and streamline your API integrations. In Microservice APIs, you’ll discover: Service decomposition strategies for microservices Documentation-driven development for APIs Best practices for designing REST and GraphQL APIs Documenting REST APIs with the OpenAPI specification (formerly Swagger) Documenting GraphQL APIs using the Schema Definition Language Building microservices APIs with Flask, FastAPI, Ariadne, and other frameworks Service implementation patterns for loosely coupled services Property-based testing to validate your APIs, and using automated API testing frameworks like schemathesis and Dredd Adding authentication and authorization to your microservice APIs using OAuth and OpenID Connect (OIDC) Deploying and operating microservices in AWS with Docker and Kubernetes Microservice APIs teaches you practical techniques for designing robust microservices with APIs that are easy to understand, consume, and maintain. You’ll benefit from author José Haro Peralta’s years of experience experimenting with microservices architecture, dodging pitfalls and learning from mistakes he’s made. Inside you’ll find strategies for delivering successful API integrations, implementing services with clear boundaries, managing cloud deployments, and handling microservices security. Written in a framework-agnostic manner, its universal principles can easily be applied to your favorite stack and toolset. About the technology Clean, clear APIs are essential to the success of microservice applications. Well-designed APIs enable reliable integrations between services and help simplify maintenance, scaling, and redesigns. Th is book teaches you the patterns, protocols, and strategies you need to design, build, and deploy effective REST and GraphQL microservices APIs. About the book Microservice APIs gathers proven techniques for creating and building easy-to-consume APIs for microservices applications. Rich with proven advice and Python-based examples, this practical book focuses on implementation over philosophy. You’ll learn how to build robust microservice APIs, test and protect them, and deploy them to the cloud following principles and patterns that work in any language. What's inside Service decomposition strategies for microservices Best practices for designing and building REST and GraphQL APIs Service implementation patterns for loosely coupled components API authorization with OAuth and OIDC Deployments with AWS and Kubernetes About the reader For developers familiar with the basics of web development. Examples are in Python. About the author José Haro Peralta is a consultant, author, and instructor. He’s also the founder of microapis.io. Table of Contents PART 1 INTRODUCING MICROSERVICE APIS 1 What are microservice APIs? 2 A basic API implementation 3 Designing microservices PART 2 DESIGNING AND BUILDING REST APIS 4 Principles of REST API design 5 Documenting REST APIs with OpenAPI 6 Building REST APIs with Python 7 Service implementation patterns for microservices PART 3 DESIGNING AND BUILDING GRAPHQL APIS 8 Designing GraphQL APIs 9 Consuming GraphQL APIs 10 Building GraphQL APIs with Python PART 4 SECURING, TESTING, AND DEPLOYING MICROSERVICE APIS 11 API authorization and authentication 12 Testing and validating APIs 13 Dockerizing microservice APIs 14 Deploying microservice APIs with Kubernetes

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Download or read book Learn Python by Building Data Science Applications written by Philipp Kats and published by Packt Publishing Ltd. This book was released on 2019-08-30 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the constructs of the Python programming language and use them to build data science projects Key FeaturesLearn the basics of developing applications with Python and deploy your first data applicationTake your first steps in Python programming by understanding and using data structures, variables, and loopsDelve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in PythonBook Description Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards. What you will learnCode in Python using Jupyter and VS CodeExplore the basics of coding – loops, variables, functions, and classesDeploy continuous integration with Git, Bash, and DVCGet to grips with Pandas, NumPy, and scikit-learnPerform data visualization with Matplotlib, Altair, and DatashaderCreate a package out of your code using poetry and test it with PyTestMake your machine learning model accessible to anyone with the web APIWho this book is for If you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.

Download or read book Learn Python From an Expert The Complete Guide With Artificial Intelligence written by Edson L P Camacho and published by . This book was released on 2023-06-08 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Ultimate Guide to Advanced Python and Artificial Intelligence: Unleash the Power of Code! Are you ready to take your Python programming skills to the next level and dive into the exciting world of artificial intelligence? Look no further! We proudly present the comprehensive book written by renowned author Edson L P Camacho: "Advanced Python: Mastering AI." In today's rapidly evolving technological landscape, the demand for AI professionals is soaring. Python, with its simplicity and versatility, has become the go-to language for AI development. Whether you are a seasoned Pythonista or a beginner eager to learn, this book is your gateway to mastering AI concepts and enhancing your programming expertise. What sets "Advanced Python: Mastering AI" apart from other books is its unparalleled combination of in-depth theory and hands-on practicality. Edson L P Camacho, a leading expert in the field, guides you through every step, from laying the foundation of Python fundamentals to implementing cutting-edge AI algorithms. Here's a glimpse of what you'll find within the pages of this comprehensive guide: 1. Python Fundamentals: Review and reinforce your knowledge of Python basics, including data types, control flow, functions, and object-oriented programming. Build a solid foundation to tackle complex AI concepts. 2. Data Manipulation and Visualization: Learn powerful libraries such as NumPy, Pandas, and Matplotlib to handle and analyze data. Understand how to preprocess and visualize data effectively for AI applications. 3. Machine Learning Essentials: Dive into the world of machine learning and explore popular algorithms like linear regression, decision trees, support vector machines, and neural networks. Discover how to train, evaluate, and optimize models for various tasks. 4. Deep Learning and Neural Networks: Delve deeper into neural networks, the backbone of modern AI. Gain insights into deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Implement advanced techniques like transfer learning and generative models. 5. Natural Language Processing (NLP): Explore the fascinating field of NLP and learn how to process and analyze textual data using Python. Discover techniques like sentiment analysis, named entity recognition, and text generation. 6. Computer Vision: Unleash the power of Python for image and video analysis. Build computer vision applications using popular libraries like OpenCV and TensorFlow. Understand concepts like object detection, image segmentation, and image captioning. 7. Reinforcement Learning: Embark on the exciting journey of reinforcement learning. Master the fundamentals of Q-learning, policy gradients, and deep Q-networks. Create intelligent agents that can learn and make decisions in dynamic environments. "Advanced Python: Mastering AI" not only equips you with the theoretical knowledge but also provides numerous real-world examples and projects to reinforce your understanding. Each chapter is accompanied by practical exercises and coding challenges to sharpen your skills and boost your confidence. Don't miss the opportunity to stay ahead in this AI-driven era. Order your copy of "Advanced Python: Mastering AI" today and unlock the full potential of Python programming with artificial intelligence. Take your career to new heights and become a proficient AI developer. Get ready to write the code that shapes the future!

Download or read book Introducing Python written by Bill Lubanovic and published by "O'Reilly Media, Inc.". This book was released on 2019-11-06 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: Easy to understand and fun to read, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you’ve learned. You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages.

Download or read book Interpretable Machine Learning with Python written by Serg Masís and published by Packt Publishing Ltd. This book was released on 2021-03-26 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.

Download or read book Building Data Science Applications with FastAPI written by Francois Voron and published by Packt Publishing Ltd. This book was released on 2021-10-08 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications Key FeaturesCover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injectionDevelop efficient RESTful APIs for data science with modern PythonBuild, test, and deploy high performing data science and machine learning systems with FastAPIBook Description FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you'll cover best practices relating to testing and deployment to run a high-quality and robust application. You'll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you'll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you'll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you'll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI. What you will learnExplore the basics of modern Python and async I/O programmingGet to grips with basic and advanced concepts of the FastAPI frameworkImplement a FastAPI dependency to efficiently run a machine learning modelIntegrate a simple face detection algorithm in a FastAPI backendIntegrate common Python data science libraries in a web backendDeploy a performant and reliable web backend for a data science applicationWho this book is for This Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.

Download or read book FastAPI written by Bill Lubanovic and published by "O'Reilly Media, Inc.". This book was released on 2023-11-06 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: FastAPI is a young yet solid framework that takes advantage of newer Python features in a clean design. As its name implies, FastAPI is indeed fast, rivaling similar frameworks in languages such as Golang. With this practical book, developers familiar with Python will learn how FastAPI lets you accomplish more in less time with less code. Author Bill Lubanovic covers the nuts and bolts of FastAPI development with how-to guides on various topics such as forms, database access, graphics, maps, and more that will take you beyond the basics. This book also includes how-to guides that will get you up to speed on RESTful APIs, data validation, authorization, and performance. With its similarities to frameworks like Flask and Django, you'll find it easy to get started with FastAPI. Through the course of this book, you will: Learn how to build web applications with FastAPI Understand the differences between FastAPI, Starlette, and pydantic Learn two features that set FastAPI apart: asynchronous functions and data type checking and validation Examine new features of Python 3.8+, especially type annotations Understand the differences between sync and async Python Learn how to connect with external APIs and services

Download or read book Grokking Deep Learning written by Andrew W. Trask and published by Simon and Schuster. This book was released on 2019-01-23 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide

Download or read book Enterprise DevOps for Architects written by Jeroen Mulder and published by Packt Publishing Ltd. This book was released on 2021-11-11 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: An architect's guide to designing, implementing, and integrating DevOps in the enterprise Key FeaturesDesign a DevOps architecture that is aligned with the overall enterprise architectureDesign systems that are ready for AIOps and make the move toward NoOpsArchitect and implement DevSecOps pipelines, securing the DevOps enterpriseBook Description Digital transformation is the new paradigm in enterprises, but the big question remains: is the enterprise ready for transformation using native technology embedded in Agile/DevOps? With this book, you'll see how to design, implement, and integrate DevOps in the enterprise architecture while keeping the Ops team on board and remaining resilient. The focus of the book is not to introduce the hundreds of different tools that are available for implementing DevOps, but instead to show you how to create a successful DevOps architecture. This book provides an architectural overview of DevOps, AIOps, and DevSecOps – the three domains that drive and accelerate digital transformation. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this DevOps book will help you to successfully integrate DevOps into enterprise architecture. You'll learn what AIOps is and what value it can bring to an enterprise. Lastly, you will learn how to integrate security principles such as zero-trust and industry security frameworks into DevOps with DevSecOps. By the end of this DevOps book, you'll be able to develop robust DevOps architectures, know which toolsets you can use for your DevOps implementation, and have a deeper understanding of next-level DevOps by implementing Site Reliability Engineering (SRE). What you will learnCreate DevOps architecture and integrate it with the enterprise architectureDiscover how DevOps can add value to the quality of IT deliveryExplore strategies to scale DevOps for an enterpriseArchitect SRE for an enterprise as next-level DevOpsUnderstand AIOps and what value it can bring to an enterpriseCreate your AIOps architecture and integrate it into DevOpsCreate your DevSecOps architecture and integrate it with the existing DevOps setupApply zero-trust principles and industry security frameworks to DevOpsWho this book is for This book is for enterprise architects and consultants who want to design DevOps systems for the enterprise. It provides an architectural overview of DevOps, AIOps, and DevSecOps. If you're looking to learn about the implementation of various tools within the DevOps toolchain in detail, this book is not for you.

Download or read book A Handbook of Python 4 0 and Beyond Syntax written by N.B. Singh and published by N.B. Singh. This book was released on with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: "A Handbook of Python 4.0 and Beyond Syntax" is a comprehensive guide designed for absolute beginners eager to learn the fundamentals of Python programming. This book introduces readers to the syntax and structure of Python 4.0, providing clear explanations and practical examples to help readers grasp key concepts. From basic language features to advanced topics such as type hinting, concurrency, and machine learning integration, this handbook covers a wide range of topics essential for building a strong foundation in Python programming. With its accessible language and hands-on approach, this book empowers beginners to dive into the world of Python programming with confidence and enthusiasm, setting them on the path to becoming proficient Python developers.

Download or read book A Hands On Introduction to Essential Python Libraries and Frameworks With Code Samples written by Murat Durmus and published by Murat Durmus. This book was released on 2023-03-02 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essential Python libraries and frameworks that every aspiring data scientist, ML engineer, and Python developer should know. "Python is not just a language, it's a community where developers can learn, collaborate and create wonders." ~ Guido van Rossum (Creator of Python)

Download or read book Quick Start Guide to Large Language Models written by Sinan Ozdemir and published by Addison-Wesley Professional. This book was released on 2023-09-20 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generation Master advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data Construct and fine-tune multimodal Transformer architectures using opensource LLMs Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind "By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application." --Giada Pistilli, Principal Ethicist at HuggingFace "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Download or read book Pragmatic AI written by Noah Gift and published by Addison-Wesley Professional. This book was released on 2018-07-12 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Download or read book Getting Started with Streamlit for Data Science written by Tyler Richards and published by Packt Publishing Ltd. This book was released on 2021-08-20 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create, deploy, and test your Python applications, analyses, and models with ease using Streamlit Key Features Learn how to showcase machine learning models in a Streamlit application effectively and efficiently Become an expert Streamlit creator by getting hands-on with complex application creation Discover how Streamlit enables you to create and deploy apps effortlessly Book DescriptionStreamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What you will learn Set up your first development environment and create a basic Streamlit app from scratch Explore methods for uploading, downloading, and manipulating data in Streamlit apps Create dynamic visualizations in Streamlit using built-in and imported Python libraries Discover strategies for creating and deploying machine learning models in Streamlit Use Streamlit sharing for one-click deployment Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar Implement best practices for prototyping your data science work with Streamlit Who this book is for This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you’re a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered.