Data Science a First Introduction with Python Pdf (3 results)

Taschenbuch. Condition: Neu. Neuware - Everything you need to know about Retrieval Augmented Generation in one human-friendly guide.Augmented Generationor RAGenhances an LLM's available data by adding context from an external knowledge base, so it can answer accurately about proprietary content, recent information, and even live conversations. RAG is powerful, and with A Simple Guide to Retrieval Augmented Generation, it's also easy to understand and implement! In A Simple Guide to Retrieval Augmented Generation you'll learn: The components of a RAG system How to create a RAG knowledge base The indexing and generation pipeline Evaluating a RAG system Advanced RAG strategies RAG tools, technologies, and frameworks A Simple Guide to Retrieval Augmented Generation gives an easy, yet comprehensive, introduction to RAG for AI beginners. You'll go from basic RAG that uses indexing and generation pipelines, to modular RAG and multimodal data from images, spreadsheets, and more. About the Technology If you want to use a large language model to answer questions about your specific business, you're out of luck. The LLM probably knows nothing about it and may even make up a response. Retrieval Augmented Generation is an approach that solves this class of problems. The model first retrieves the most relevant pieces of information from your knowledge stores (search index, vector database, or a set of documents) and then generates its answer using the user's prompt and the retrieved material as context. This avoids hallucination and lets you decide what it says. About the Book A Simple Guide to Retrieval Augmented Generation is a plain-English guide to RAG. The book is easy to follow and packed with realistic Python code examples. It takes you concept-by-concept from your first steps with RAG to advanced approaches, exploring how tools like LangChain and Python libraries make RAG easy. And to make sure you really understand how RAG works, you'll build a complete system yourselfeven if you're new to AI! What's Inside RAG components and applications Evaluating RAG systems Tools and frameworks for implementing RAG About the Readers For data scientists, engineers, and technology managersno prior LLM experience required. Examples use simple, well-annotated Python code. About the Author Abhinav Kimothi is a seasoned data and AI professional. He has spent over 15 years in consulting and leadership roles in data science, machine learning and AI, and currently works as a Director of Data Science at Sigmoid. Table of Contents Part 1 1 LLMs and the need for RAG 2 RAG systems and their design Part 2 3 Indexing pipeline: Creating a knowledge base for RAG 4 Generation pipeline: Generating contextual LLM responses 5 RAG evaluation: Accuracy, relevance, and faithfulness Part 3 6 Progression of RAG systems: Naïve, advanced, and modular RAG 7 Evolving RAGOps stack Part 4 8 Graph, multimodal, agentic, and other RAG variants 9 RAG development framework and further exploration Get a free Elektronisches Buch (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Taschenbuch. Condition: Neu. Neuware - A developer-centric look at quantum computing.The demand for developers who can implement solutions with quantum resources is growing larger every day. Building Quantum Software with Python gives you the foundation you need to build the software for the quantum age, and apply quantum computing to real-world business and research problems. In Building Quantum Software with Python you will learn about: Quantum states, gates, and circuits A practical introduction to quantum algorithms Running quantum software on classical simulators and quantum hardware Quantum search, phase estimation, and quantum counting Quantum solutions to optimization problems Building Quantum Software with Python lays out the math and programming techniques you'll need to apply quantum solutions to real challenges like sampling from classically intractable probability distributions and large-scale optimization problems. You will learn which quantum algorithms and patterns apply to different types of problems and how to build your first quantum applications. All the simulator code you write can be easily converted to run on real quantum hardware. Foreword by Heather Higgins. Purchase of the print book includes a free Elektronisches Buch in PDF and ePub formats from Manning Publications. About the technology Large-scale optimization problems, complex financial and scientific simulations, cryptographic calculations, and certain types of machine learning require unreasonably long times to run on classical computers. Quantum computers can perform some operations like these almost instantaneously! Don't wait to get started. This book will prime you on quantum applications, implementations, and hybrid quantum-classic designs so you'll be ready to join the quantum revolution. About the book Building Quantum Software with Python teaches you how to build working applications that run on a simulator or real quantum hardware. By relating QC to classical computing concepts you already know, this book's intuitive visualizations and code implementations make quantum computing easy to grasp even if you don't have a background in advanced math. As you go, you'll discover and implement quantum techniques for truly random sampling, optimization solutions, unstructured search, and moreall using easy-to-follow Python code. What's inside Hype-free discussions of when, where, and why QC makes sense Solving complex optimization problems Quantum search using Grover's Algorithm Fourier transform, phase estimation, and probability distribution sampling About the reader For developers who know Python. No advanced math knowledge required. About the author Constantin Gonciulea leads the Advanced Technology group at Wells Fargo and has worked in quantum computing since 2018. Charlee Stefanski is a senior software engineer at Wells Fargo, where she leads the development of the internal quantum computing platform. Table of Contents Part 1 1 Advantages and challenges of programming quantum computers 2 A first look at quantum computations: The knapsack problem 3 Single-qubit states and gates 4 Quantum state and circuits: Beyond one qubit Part 2 5 Selecting outcomes with quantum oracles 6 Quantum search and probability estimation 7 The quantum Fourier transform 8 Using the quantum Fourier transform 9 Quantum phase estimation Part 3 10 Encoding functions in quantum states 11 Search-based quantum optimization 12 Conclusions and outlook Appendixes A Math refresher B More about quantum states and gates C Outcome pairing strategies.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn computer architecture with Python and ARM, simulating assembly program execution and designing a computer simulatorPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features Build a computer simulator with Python: Learn computer architecture by designing and constructing a simulator Python for architecture: Use Python to simulate and execute assembly language instructions ARM programming on Raspberry Pi: Explore ARM assembly language and run programs on Raspberry PiBook DescriptionThis comprehensive guide offers a unique and immersive learning experience by combining Python programming with ARM architecture.Starting with an introduction to computer architecture and the flow of data within a computer system, you'll progress to building your own interpreter using Python. You'll see how this foundation enables the simulation of computer operations and learn ways to enhance a simulator by adding new instructions and displaying improved results.As you advance, you'll explore the TC1 Assembler and Simulator Program to gain insights into instruction analysis and explore practical examples of simulators. This will help you build essential skills in understanding complex computer instructions, strengthening your grasp of computer architecture. Moreover, you'll be introduced to the Raspberry Pi operating system, preparing you to delve into the detailed language of the ARM computer. This includes exploring the ARM instruction set architecture, data-processing instructions, subroutines, and the stack.With clear explanations, practical examples, and coding exercises, this resource will enable you to design and construct your own computer simulator, simulate assembly language programs, and leverage the Raspberry Pi for ARM programming.What you will learn Master the core principles of computer architecture Understand the role of registers, memory, and data flow in computers Discover how to design and implement a computer simulator using Python Simulate and execute assembly language programs on the simulator Enhance the simulator using new instructions for improved output Analyze complex computer instructions for deeper architectural understanding Explore the ARM instruction set and data processing on the Raspberry Pi Develop proficiency in writing, assembling, and running ARM code on the Raspberry PiWho this book is forThis book is for university students studying computer science, particularly those enrolled in a computer architecture module. With its practical approach and succinct explanations, it is also suitable for hobbyists, enthusiasts, and self-learners seeking a deeper understanding of computer systems. The book assumes foundational knowledge of number bases, binary arithmetic, and Boolean logic concepts. While it primarily caters to the computer science field, this book is less geared toward electrical or electronics engineering.Table of Contents Introduction to the computer Computer architecture: Data flow in a computer Crafting an interpreter: First steps A little more Python Analyzing the Instruction The Different Types of Computing Technologies Adding New Instructions Displaying results Examples of simulators 'Basics of the Raspberry Pi operating system' 'ARM instruction set architecture' ARM data-processing instructions Appendix I: Examples of ARM code.