Search results for "Andre Python"
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Python Without Fear
Praise for this book, Python Without Fear “This is really a great book. I wish I’d had it when I was learning Python.” –John M. Wargo, author of Apache Cordova 4 Programming Praise for the previous book in the series, C++ Without Fear “I’m in love with your C++ Without Fear book. It keeps me awake for hours during the night. Thanks to you, I got most of the idea in just a few hours.” –Laura Viral, graduate physics student at CERN and Istanbul, Turkey “It’s hard to tell where I began and ended with your book. I felt like I woke up and literally knew how to write C++ code. I can’t overstate the confidence you gave me.” – Danny Grady, senior programmer/analyst at a Fortune 500 Company Whether you’re new to programming or moving from another language, Python Without Fear will quickly make you productive! Brian Overland’s unique approach to Python includes: Taking you by the hand while teaching topics from the very basics to intermediate and advanced features of PythonTeaching by examples that are explained line by lineHeavy emphasis on examples that are fun and useful, including games, graphics, database applications, file storage, puzzles, and more!How to think “Pythonically” and avoid common “gotchas” Register your product at informit.com/register for convenient access to downloads, updates, and/or corrections as they become available.
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Generative AI Apps with LangChain and Python
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape.What You Will Learn Understand different types of LLMs and how to select the right ones for responsible AI.Structure effective prompts.Master LangChain concepts, such as chains, models, memory, and agents.Apply embeddings effectively for search, content comparison, and understanding similarity.Setup and integrate Pinecone vector database for indexing, structuring data, and search.Build Q & A applications for multiple doc formats.Develop multi-step AI workflow apps using LangChain agents.Who This Book Is ForPython programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.
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Python for Asset Management
The asset management industry is undergoing a paradigm shift toward automation, transparency, and data-driven decision-making. Traditional tools (Excel, Bloomberg) are being replaced by programmable, scalable solutions. Yet, most finance professionals lack accessible, practical training in applying Python to real portfolio problems.Python For Asset Management fills that gap. The book empowers non-programmers—portfolio managers, risk analysts, and students—to implement advanced models themselves. It responds to the growing demand for quantitative literacy in finance, especially in sustainable investing and smart beta strategies, areas of active research for both of the authors.Features31 hands-on Python exercises with real data and executable code.Complete GitHub repository (MIT License) with all scripts, data pipelines, and results.Step-by-step implementation of VaR (historical, parametric, Monte Carlo), bond immunization, and factor models.Real-world decision tools — e.g., build a bullet/barbell/ladder bond portfolio, run Brinson-Fachler attribution, or backtest smart beta vs. index.Immediate applicability — every exercise produces a deliverable (e.g., optimal weights, risk report, attribution table) ready for client meetings.Focus on practical asset management workflows, not just theory.
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Causal Inference and Discovery in Python
Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental dataPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExamine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and moreDiscover modern causal inference techniques for average and heterogenous treatment effect estimationExplore and leverage traditional and modern causal discovery methodsBook DescriptionCausal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code.Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms.The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.What you will learnMaster the fundamental concepts of causal inferenceDecipher the mysteries of structural causal modelsUnleash the power of the 4-step causal inference process in PythonExplore advanced uplift modeling techniquesUnlock the secrets of modern causal discovery using PythonUse causal inference for social impact and community benefitWho this book is forThis book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.
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Generative AI with Python and PyTorch
Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs)Key FeaturesImplement real-world applications of LLMs and generative AIFine-tune models with PEFT and LoRA to speed up trainingExpand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndexPurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionBecome an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable.From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence.You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models.Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI.What you will learnGrasp the core concepts and capabilities of LLMsCraft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputsUnderstand how attention and transformers have changed NLPOptimize your diffusion models by combining them with VAEsBuild text generation pipelines based on LSTMs and LLMsLeverage the power of open-source LLMs, such as Llama and Mistral, for diverse applicationsWho this book is forThis book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required.
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Supercharged Python
If you’re ready to write better Python code and use more advanced features, Advanced Python Programming was written for you. Brian Overland and John Bennett distill advanced topics down to their essentials, illustrating them with simple examples and practical exercises. Building on Overland’s widely-praised approach in Python Without Fear, the authors start with short, simple examples designed for easy entry, and quickly ramp you up to creating useful utilities and games, and using Python to solve interesting puzzles. Everything you’ll need to know is patiently explained and clearly illustrated, and the authors illuminate the design decisions and tricks behind each language feature they cover. You’ll gain the in-depth understanding to successfully apply all these advanced features and techniques: Coding for runtime efficiencyLambda functions (and when to use them)Managing versioning Localization and UnicodeRegular expressionsBinary operators Radix conversions Data formatting, in depthCreating, building, and importing packagesUsing the Django web-development packageGraphics programmingMagic methods and advanced object orientationReading and writing binary files, and more
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Advanced Applications of Python Data Structures and Algorithms
Data structures are essential principles applicable to any programming language in computer science. Data structures may be studied more easily with Python than with any other programming language because of their interpretability, interactivity, and object-oriented nature. Computers may store and process data at an extraordinary rate and with outstanding accuracy. Therefore, it is of the utmost importance that the data is efficiently stored and is able to be accessed promptly. In addition, data processing should take as little time as feasible while maintaining the highest possible level of precision.Advanced Applications of Python Data Structures and Algorithms assists in understanding and applying the fundamentals of data structures and their many implementations and discusses the advantages and disadvantages of various data structures. Covering key topics such as Python, linked lists, datatypes, and operators, this reference work is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.
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Python for Programmers
This book is intended primarily for developers who have little or no experience with Python or Pandas. It contains a fast-paced introduction to Python and Python-based solutions to various tasks. Chapter 1 provides a quick tour of basic Python 3, followed by a chapter that shows how to work with loops and conditional logic in Python. Chapter 3 discusses data structures in Python, followed by a chapter that features code samples for tasks with strings and arrays in Python. Chapter 5 contains concepts in object-oriented programming, along with code samples that illustrate how they are implemented in Python. Chapter 6 introduces recursion and some fundamental topics in combinatorics. Finally, the appendix provides an introduction to Pandas. Companion files with code and figures are available for downloading from the publisher.Features:Provides the reader with basic Python 3 and Pandas programming concepts.Companion files with code and figures
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