Search results for "Mastering Large Language Models with PyTorch"
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Language Modeling for Automatic Speech Recognition of Inflective Languages
This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages.
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Beyond LLMs: The Next Frontier of AI
Build AI systems that move beyond LLM demos by combining memory, planning, control, and evaluation to create reliable, production-ready intelligenceKey FeaturesDesign AI systems with memory and control instead of relying on prompts aloneMeasure reliability, safety, cost, and drift with evaluation and monitoring patternsBuild and evolve a production-style AI project across the book in PythonBook DescriptionArtificial Intelligence (AI) has moved very quickly in recent years, and large language models (LLMs) have opened the door for teams to build impressive prototypes in a short space of time. However, many of these systems struggle when they need to operate reliably in real-world settings. Prompting alone is often not enough when the goal is consistency, control, observability, and long-term performance. Teams now need to think beyond single model calls and start designing AI as a system made up of multiple working parts.Beyond LLMs provides a practical way to understand and build the next generation of AI systems using Python. This book introduces you to the core ideas that sit beyond basic LLM usage, including memory, reasoning loops, planning, agents, control mechanisms, evaluation, monitoring, and hybrid constraints.Using a recurring SupportOps AI project across the book, you will see how these capabilities fit together in a structured system rather than as isolated techniques. You will learn how to design modular components, measure reliability and drift, handle failure modes, and make safer update decisions as the system evolves.By the end of the book, you will be able to move from prompt-driven demos to more reliable, production-ready AI systems that are easier to test, monitor, and improve.What you will learnUnderstand AI system design beyond single model callsBuild memory and retrieval for continuity and response qualityCreate reasoning and planning loops for reliable task executionLearn how agents use bounded actions and control policiesMeasure reliability, safety, cost, and drift with evaluationHandle failures and update AI systems with safer rolloutsApply hybrid rules and constraints to improve consistencyWho this book is forThis book is for AI engineers, ML engineers, data scientists, software engineers, technical leads, and advanced students who want to move beyond prompt-based LLM prototypes and build reliable, production-ready AI systems. Readers should be comfortable with Python and understand basic machine learning concepts, including training, inference, evaluation, and LLM behavior. Prior experience with agentic AI, world models, neuro-symbolic AI, or production AI architecture is not required.
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Mastering Machine Learning with R
Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applicationsKey FeaturesBuild independent machine learning (ML) systems leveraging the best features of R 3.5Understand and apply different machine learning techniques using real-world examplesUse methods such as multi-class classification, regression, and clusteringBook DescriptionGiven the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work.What you will learnPrepare data for machine learning methods with easeUnderstand how to write production-ready code and package it for useProduce simple and effective data visualizations for improved insightsMaster advanced methods, such as Boosted Trees and deep neural networksUse natural language processing to extract insights in relation to textImplement tree-based classifiers, including Random Forest and Boosted TreeWho this book is forThis book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.
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Mastering Scala Machine Learning
Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and HadoopAbout This Book• This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data• Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning• Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainabilityWho This Book Is ForMastering Scala Machine Learning is intended for enthusiasts who want to plunge into the new pool of emerging techniques for machine learning. Some familiarity with standard statistical techniques is required.What You Will Learn• Sharpen your functional programming skills in Scala using REPL• Apply standard and advanced machine learning techniques using Scala• Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data• Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data• Understand the principles of supervised and unsupervised learning in machine learning• Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet• Construct reliable and robust data pipelines and manage data in a data-driven enterprise• Implement scalable model monitoring and alerts with ScalaIn DetailSince the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing.This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees.Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.Style and approachThis hands-on guide dives straight into implementing Scala for machine learning without delving much into mathematical proofs or validations. There are ample code examples and tricks that will help you sail through using the standard techniques
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Mastering Predictive Analytics with R -
Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential conceptsAbout This Book• Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding• Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types• Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easilyWho This Book Is ForAlthough budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure.What You Will Learn• Master the steps involved in the predictive modeling process• Grow your expertise in using R and its diverse range of packages• Learn how to classify predictive models and distinguish which models are suitable for a particular problem• Understand steps for tidying data and improving the performing metrics• Recognize the assumptions, strengths, and weaknesses of a predictive model• Understand how and why each predictive model works in R• Select appropriate metrics to assess the performance of different types of predictive model• Explore word embedding and recurrent neural networks in R• Train models in R that can work on very large datasetsIn DetailR offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks.By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R.Style and approachThis book takes a step-by-step approach in expl
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Mastering Python
Use advanced features of Python to write high-quality, readable code and packagesKey FeaturesExtensively updated for Python 3.10 with new chapters on design patterns, scientific programming, machine learning, and interactive PythonShape your scripts using key concepts like concurrency, performance optimization, asyncio, and multiprocessingLearn how advanced Python features fit together to produce maintainable codeBook DescriptionEven if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python's capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code's performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challengesWhat you will learnWrite beautiful Pythonic code and avoid common Python coding mistakesApply the power of decorators, generators, coroutines, and metaclassesUse different testing systems like pytest, unittest, and doctestTrack and optimize application performance for both memory and CPU usageDebug your applications with PDB, Werkzeug, and faulthandlerImprove your performance through asyncio, multiprocessing, and distributed computingExplore popular libraries like Dask, NumPy, SciPy, pandas, TensorFlow, and scikit-learnExtend Python's capabilities with C/C++ libraries and system callsWho this book is forThis book will benefit more experienced Python programmers who wish to upskill, serving as a reference for best practices and some of the more intricate Python techniques. Even if you have been using Python for years, chances are that you haven't yet encountered every topic discussed in this book. A good understanding of Python programming is necessary
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Language at Large
The volume brings together important essays on syntax and semantics by Aikhenvald and Dixon, highlighting their expertise in various fields of linguistics. The first part focusses on linguistic typology, covering case markers used on verbs, argument-determined constructions, unusual meanings of causatives, the semantic basis for a typology, word-class-changing derivations, speech reports and semi-direct speech. The second part concentrates on documentation and analysis of previously undescribed languages, from South America and Indigenous Australia. The third part addresses a variety of issues in grammar and lexicography of English. This includes pronouns with transferred reference, comparative constructions, features of the noun phrase, and the discussion of 'twice'. The treatment of Australian Aboriginal words in dictionaries is discussed in the final chapter.
R 12,683.00
Practical Prompt Guide for GPT-6 Astra
Your GPT-5-era prompting habits now backfire on GPT-6 Astra. Learn why a more capable model needs different instructions and master a practical framework to get reliable, high-quality results in coding, research, and writing.Key FeaturesUnderstand why GPT-6 Astra's greater capability makes legacy prompt habits backfire.Build prompts from a practical six-part framework for reliable, high-quality output.Apply the techniques across coding, research, writing, and long-context workflows and calibrate control against autonomy.Book DescriptionGPT-6 Astra is the most capable model OpenAI has deployed, and it does not prompt like the models you learned on. Where earlier models would guess, Astra asks. Where they would charge ahead, Astra stops for approval. Where they ignored bloated instruction files, Astra follows every line. The habits you built to compensate for weaker models are now quietly working against you.The Practical Prompt Guide for GPT-6 Astra explains why, grounded in OpenAI's official documentation, system card, and independent benchmark data. It shows how Astra's stronger alignment, instruction sensitivity, and new API behaviors shift the prompting relationship from compensation to coordination. You will learn a practical framework for designing prompts from scratch: defining objectives, scope and completion criteria; providing context and constraints that actually help; and specifying the outputs you want. You will master Astra's core behaviors, manage long-context and multi-step workflows, and tune reasoning effort to balance cost, latency, and quality.Across coding, research, and writing, the book gives you anti-patterns to avoid, ways to diagnose failures, and decision rules for when to build a custom prompt and when not to. By the end, you will prompt the most capable model OpenAI has released with reliability, confidence, and control.What you will learnUnderstand how GPT-6 Astra differs from earlier models and why legacy prompt habits backfireMaster a six-part framework for designing reliable prompts from scratchDefine objectives, scope, and completion criteria to prevent premature stops and scope creepProvide context and constraints that help Astra instead of constraining itManage long-context and multi-step workflows and calibrate reasoning effortApply the techniques to coding, research, writing, and computer-use tasksAvoid common anti-patterns and apply decision rulesWho this book is forThis book is for anyone who already uses large language models and wants reliable, high-quality results from GPT-6 Astra: developers, technical writers, researchers, data analysts, and technical leads. You do not need to be an AI specialist, only to have used a modern LLM before. Whether you are automating coding work, running research pipelines, or drafting content, the book gives you a practical framework to prompt Astra effectively. If you are building or evaluating agentic AI workflows, you will also find concrete guidance on autonomy, delegat
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