Search results for "Python und NLP"
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7 Lessons in NLP
A SHORT COURSE IN NLP BASICS.7 Lessons in NLP is a quick course in leading people, giving you everything you need to know in just seven short chapters. From communicating more effectively to creating greater rapport with others, you'll discover the ability to change what isn't working in your life and increase what is.* Lesson 1: What is neuro-linguistic programming?* Lesson 2: Identify empowering and limiting beliefs* Lesson 3: Recognize how we represent information to ourselves* Lesson 4: Use precision questions to find out what people mean* Lesson 5: Identify different communication filters* Lesson 6: Use the six levels of change and reframing* Lesson 7: Increase your optionsExplore the main themes and ideas in NLP, get a basic knowledge and understanding of the key concepts, and test your progress with practical and thought-provoking exercises. 7 Lessons in NLP is your fastest route to success.ABOUT THE SERIES7 Lessons are for people who want to succeed at work. From negotiating and content marketing to finance and social media, the series covers the business topics that matter and that will help you make a difference today. Written in straightforward English, each book features a course of 7 short lessons so that with just a little work, you can quickly master the subject.
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Grundlagen der Python-Programmierung
Dieses Buch vermittelt einen systematischen Einstieg in die Python-Programmierung und führt Schritt für Schritt durch alle wichtigen Grundlagen bis hin zu fortgeschrittenen Konzepten. Es beginnt mit den zentralen Sprachelementen wie Datentypen, Variablen, Operatoren, Zeichenketten und numerischen Funktionen und zeigt anhand klarer Beispiele, wie Python effizient eingesetzt wird.Aufbauend darauf behandelt das Buch strukturierte Daten wie Listen, Tupel, Dictionaries und Sets sowie Kontrollstrukturen mit Verzweigungen und Schleifen. Im weiteren Verlauf werden Funktionen, Fehlerbehandlung und objektorientierte Programmierung ausführlich erklärt. Ein eigenes Kapitel widmet sich dem Arbeiten mit Dateien, inklusive Lesen, Schreiben, CSV-Verarbeitung und grundlegenden Funktionen des Betriebssystems.Abgerundet wird das Werk durch einen großen Übungsteil mit praxisnahen Aufgaben und vollständigen Lösungen, die das eigenständige Lernen unterstützen und den Stoff festigen. Damit eignet sich das Buch sowohl für Einsteigerinnen und Einsteiger in Informatik und Data Science als auch für Anwenderinnen und Anwender, die ein solides Fundament für weiterführende Python-Anwendungen aufbauen möchten.
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Behavioural Safety Demystified with an Introduction to NLP
Behavioural safety is a vitally important subject, but yet we let it languish in obscurity and terminology; we confound anyone wanting to learn more about it by throwing academic jargon in their face. That's why I've written this book to bring the subject to life. This book will demystify behavioural safety! I want to peel back the layers of academic-sounding rules and theories, shift through the masses of white papers and really get down to the crux of the matter in a jargon-less way. In a way that you'll find easy to appreciate and, hopefully, also enjoy! At the same time, I'll also give you an easy-to-understand introduction to Neuro Linguistic Programming which can really transform the way you work.
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Introduction to Python
Introduction to Python: with Applications in Optimization, Image and Video Processing, and Machine Learning is intended primarily for advanced undergraduate and graduate students in quantitative sciences such as mathematics, computer science, and engineering. In addition to this, the book is written in such a way that it can also serve as a self-contained handbook for professionals working in quantitative fields including finance, IT, and many other industries where programming is a useful or essential tool.The book is written to be accessible and useful to those with no prior experience of Python, but those who are somewhat more adept will also benefit from the more advanced material that comes later in the book.FeaturesCovers introductory and advanced material. Advanced material includes lists, dictionaries, tuples, arrays, plotting using Matplotlib, object-oriented programmingSuitable as a textbook for advanced undergraduates or postgraduates, or as a reference for researchers and professionalsSolutions manual, code, and additional examples are available for download
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Physik mit Python
Dieses Lehrbuch führt Sie anhand von physikalischen Fragestellungen aus der Mechanik in die Programmiersprache Python ein. Neben der reinen Simulation von physikalischen Systemen wird besonderes Augenmerk auf die Visualisierung von Ergebnissen und das Erstellen von Animationen gelegt. Mit zahlreichen Beispielen und Übungsaufgaben ermöglicht dieses Buch so den praktischen Einstieg in das wissenschaftliche Rechnen.Sie lernen Kurvenanpassungen durchzuführen sowie lineare und nicht-lineare Gleichungssysteme zu lösen, die bei der Behandlung von statischen Problemen auftreten. Auch die Lösung von Differentialgleichungen, die dynamische Systeme beschreiben, sowie Themen wie Fourier-Transformationen und Eigenwertprobleme kommen nicht zu kurz. Mithilfe der in diesem Buch vorgestellten Simulationsbeispiele vertiefen Sie darüber hinaus Ihr Verständnis der zugrundeliegenden Physik, indem Sie die physikalischen Gesetze algorithmisch umsetzen und physikalische Aufgaben simulieren, die weitaus komplexer sind als die üblichen mit Papier und Bleistift lösbaren Aufgaben.Alle im Buch vorgestellten Programme, die fertigen Animationen sowie die Lösungen zu den Übungsaufgaben werden online bereitgestellt.Die vorliegende zweite Auflage enthält zahlreiche Ergänzungen, Korrekturen und Anpassungen an die aktuellen Versionen von Python, NumPy, SciPy und Matplotlib. Darüber hinaus erwartet Sie ein zusätzliches Kapitel über objektorientierte Programmiermethoden. Ob Sie also Physik oder eine Ingenieurwissenschaft mit hohem physikalischem Anteil studieren, oder ob Sie unterrichten und Ihre Lehre durch Simulationen und Animationen anreichern möchten – dieses Buch ist dabei Ihr optimaler Begleiter!
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Advanced Data Science and Analytics with Python
The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today’s AI-driven world.
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50 Algorithms Every Programmer Should Know
Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaulPurchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesFamiliarize yourself with advanced deep learning architecturesExplore newer topics, such as handling hidden bias in data and algorithm explainabilityGet to grips with different programming algorithms and choose the right data structures for their optimal implementationBook DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnDesign algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisDelve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examplesCreate a recommendation engine that suggests relevant movies to subscribersGrasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMsWho this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.Python programming experience is a must, knowledge of data science will be helpful but not n
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Machine Learning and Python for Human Behavior, Emotion, and Health Status Analysis
This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences. In short, it is a gateway to the dynamic intersection of Python programming, smart home technology, and advanced machine learning applications, making it an invaluable resource for those eager to explore this rapidly growing field.Key Features:Throughout the book, practicality takes precedence, with hands-on coding examples accompanying each concept to facilitate an interactive learning journeyStriking a harmonious balance between theoretical foundations and practical coding, the book caters to a diverse audience, including smart home enthusiasts and researchersThe content prioritizes real-world applications, ensuring readers can immediately apply the knowledge gained to enhance smart home functionalitiesCovering Python basics, feature extraction, deep learning, and XAI, the book provides a comprehensive guide, offering an overall understanding of smart home applications
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Hands-On Q-Learning with Python
Leverage the power of reward-based training for your deep learning models with PythonKey FeaturesUnderstand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)Study practical deep reinforcement learning using Q-NetworksExplore state-based unsupervised learning for machine learning modelsBook DescriptionQ-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers.This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you’ll gain a sense of what’s in store for reinforcement learning.By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.What you will learnExplore the fundamentals of reinforcement learning and the state-action-reward processUnderstand Markov decision processesGet well versed with libraries such as Keras, and TensorFlowCreate and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI GymChoose and optimize a Q-Network’s learning parameters and fine-tune its performanceDiscover real-world applications and use cases of Q-learningWho this book is forIf you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.
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Ontologies with Python
Use ontologies in Python, with the Owlready2 module developed for ontology-oriented programming. You will start with an introduction and refresher on Python and OWL ontologies. Then, you will dive straight into how to access, create, and modify ontologies in Python. Next, you will move on to an overview of semantic constructs and class properties followed by how to perform automatic reasoning. You will also learn about annotations, multilingual texts, and how to add Python methods to OWL classes and ontologies. Using medical terminologies as well as direct access to RDF triples is also covered. Python is one of the most used programming languages, especially in the biomedical field, and formal ontologies are also widely used. However, there are limited resources for the use of ontologies in Python. Owlready2, downloaded more than 60,000 times, is a response to this problem, and this book is the first one on the topic of using ontologies with Python.What You Will LearnUse Owlready2 to access and modify OWL ontologies in PythonPublish ontologies on dynamic websitesPerform automatic reasoning in PythonUse well-known ontologies, including DBpedia and Gene Ontology, and terminological resources, such as UMLS (Unified Medical Language System)Integrate Python methods in OWL ontologies Who Is This Book ForBeginner to experienced readers from biomedical sciences and artificial intelligence fields would find the book useful.
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Python Reinforcement Learning Projects
Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful librariesKey FeaturesImplement Q-learning and Markov models with Python and OpenAIExplore the power of TensorFlow to build self-learning modelsEight AI projects to gain confidence in building self-trained applicationsBook DescriptionReinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years.In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks.By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.What you will learnTrain and evaluate neural networks built using TensorFlow for RLUse RL algorithms in Python and TensorFlow to solve CartPole balancingCreate deep reinforcement learning algorithms to play Atari gamesDeploy RL algorithms using OpenAI UniverseDevelop an agent to chat with humans Implement basic actor-critic algorithms for continuous controlApply advanced deep RL algorithms to games such as MinecraftAutogenerate an image classifier using RLWho this book is forPython Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. Individuals who want to work on self-learning model projects will also find this book useful.
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Python Feature Engineering Cookbook
Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn librariesKey FeaturesDiscover solutions for feature generation, feature extraction, and feature selectionUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasetsImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy librariesBook DescriptionFeature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code.Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains.By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.What you will learnSimplify your feature engineering pipelines with powerful Python packagesGet to grips with imputing missing valuesEncode categorical variables with a wide set of techniquesExtract insights from text quickly and effortlesslyDevelop features from transactional data and time series dataDerive new features by combining existing variablesUnderstand how to transform, discretize, and scale your variablesCreate informative variables from date and timeWho this book is forThis book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.
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Introduction to Psychophysics with Python
Introduction to Psychophysics with Python is a hands-on guide to exploring how we sense and perceive the world—powered by modern Python programming. Bridging classical theory with today’s computational tools, it equips students and researchers to design, run, and analyze psychophysical experiments with confidence.Starting from the foundations laid by Weber, Fechner, and Stevens, the book traces the evolution of psychophysics into contemporary practice. Readers gain a clear grounding in sensory physiology, experimental design, and statistical analysis, always tied to the central challenge: connecting physical stimuli to perceptual responses and making sense of the data.What sets this book apart is its practical, step-by-step approach. Through tutorials and annotated code using PsychoPy, readers learn Python essentials—loops, conditionals, data handling—while building real experiments. These skills extend seamlessly into advanced methods like curve fitting, bootstrapping, and statistical inference.Illustrated with intuitive explanations, diagrams, and thought-provoking exercises, the book blends conceptual clarity with real-world application. By the end, readers will not only understand the principles of psychophysics but also have the coding expertise to create and analyze their own experiments—opening the door to deeper insights into sensation and perception.
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Python: Real World Machine Learning
Learn to solve challenging data science problems by building powerful machine learning models using PythonAbout This Book* Understand which algorithms to use in a given context with the help of this exciting recipe-based guide* This practical tutorial tackles real-world computing problems through a rigorous and effective approach* Build state-of-the-art models and develop personalized recommendations to perform machine learning at scaleWho This Book Is ForThis Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.What You Will Learn* Use predictive modeling and apply it to real-world problems* Understand how to perform market segmentation using unsupervised learning* Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test* Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms* Increase predictive accuracy with deep learning and scalable data-handling techniques* Work with modern state-of-the-art large-scale machine learning techniques* Learn to use Python code to implement a range of machine learning algorithms and techniquesIn DetailMachine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.This Learning Path combines some
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NLP for Business Analysts
Method and process are important in business analysis, but knowing how to use them is even more so. Business analysts can increase effectiveness most by developing soft skills. You will find that these skills are transferable across project types and industry sectors. This book illustrates the application of NLP to develop competencies - better equipping you to communicate across cultures, reframe problems, manage stakeholder groups, resolve conflicts, motivate teams and become an even better leader.
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Hands-On Genetic Algorithms with Python
Explore the ever-growing world of genetic algorithms to build and enhance AI applications involving search, optimization, machine learning, deep learning, NLP, and XAI using Python librariesKey FeaturesLearn how to implement genetic algorithms using Python libraries DEAP, scikit-learn, and NumPyTake advantage of cloud computing technology to increase the performance of your solutionsDiscover bio-inspired algorithms such as particle swarm optimization (PSO) and NEATPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWritten by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms.After an introduction to genetic algorithms and their principles of operation, you’ll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you’ll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You’ll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications.By the end of this book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.What you will learnUse genetic algorithms to solve planning, scheduling, gaming, and analytics problemsCreate reinforcement learning, NLP, and explainable AI applicationsEnhance the performance of ML models and optimize deep learning architectureDeploy genetic algorithms using client-server architectures, enhancing scalability and computational efficiencyExplore how images can be reconstructed using a set of semi-transparent shapesDelve into topics like elitism, niching, and multiplicity in genetic solutions to enhance optimization strategies and solution diversityWho this book is forIf you’re a data scientist, software developer, AI enthusiast who wants to break into the world of genetic algorithms and apply them to real-world, intelligent applications as quickly as possible, this book is for you. Working knowledge of the Python programming language is required to get started with this book.
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Python Data Cleaning and Preparation Best Practices
Take your data preparation skills to the next level by converting any type of data asset into a structured, formatted, and readily usable dataset Key FeaturesMaximize the value of your data through effective data cleaning methodsEnhance your data skills using strategies for handling structured and unstructured dataElevate the quality of your data products by testing and validating your data pipelinesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionProfessionals face several challenges in effectively leveraging data in today's data-driven world. One of the main challenges is the low quality of data products, often caused by inaccurate, incomplete, or inconsistent data. Another significant challenge is the lack of skills among data professionals to analyze unstructured data, leading to valuable insights being missed that are difficult or impossible to obtain from structured data alone.To help you tackle these challenges, this book will take you on a journey through the upstream data pipeline, which includes the ingestion of data from various sources, the validation and profiling of data for high-quality end tables, and writing data to different sinks. You’ll focus on structured data by performing essential tasks, such as cleaning and encoding datasets and handling missing values and outliers, before learning how to manipulate unstructured data with simple techniques. You’ll also be introduced to a variety of natural language processing techniques, from tokenization to vector models, as well as techniques to structure images, videos, and audio.By the end of this book, you’ll be proficient in data cleaning and preparation techniques for both structured and unstructured data.What you will learnIngest data from different sources and write it to the required sinksProfile and validate data pipelines for better quality controlGet up to speed with grouping, merging, and joining structured dataHandle missing values and outliers in structured datasetsImplement techniques to manipulate and transform time series dataApply structure to text, image, voice, and other unstructured dataWho this book is forWhether you're a data analyst, data engineer, data scientist, or a data professional responsible for data preparation and cleaning, this book is for you. Working knowledge of Python programming is needed to get the most out of this book.
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Mastering LangChain
This book provides a comprehensive exploration of LangChain, empowering you to effectively harness large language models (LLMs) for Gen AI applications. It focuses on practical implementation and techniques, making it a valuable resource for learning LangChain. The book starts with foundational topics such as environment setup and building basic chains, then delves into key components such as prompt templates, tool integration, and memory management. You will also explore practical topics such as output parsing, embedding models, and developing chatbots and retrieval-augmented generation (RAG) systems. Additional chapters focus on integrating LangChain with other AI tools and deploying applications while emphasizing best practices for AI ethics and performance. By the time you finish this book, you'll have the know-how to confidently build Generative AI solutions using LangChain. Whether you're exploring practical applications or curious about the latest trends, this guide gives you the tools and insights to solve real-world AI problems. You'll be ready to design smart, data-driven applications--and rethink how you approach Generative AI. What You Will Learn Understand the core ideas, architecture, and essential features of the LangChain framework Create advanced LLM-driven workflows and applications that address real-world challengesDevelop robust Retrieval-Augmented Generation (RAG) systems using LangChain, vector databases, and proven best practices for retrieving and generating high-quality responses Who This Book Is For Data scientists and AI enthusiasts with basic Python skills who want to use LangChain for advanced development, and Python developers interested in building data-responsive applications with large language models (LLMs)
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Python in a Nutshell
Python was recently ranked as today's most popular programming language on the TIOBE index, thanks to its broad applicability to design and prototyping to testing, deployment, and maintenance. With this updated fourth edition, you'll learn how to get the most out of Python, whether you're a professional programmer or someone who needs this language to solve problems in a particular field.Carefully curated by recognized experts in Python, this new edition focuses on version 3.10, bringing this seminal work on the Python language fully up to date on five version releases, including preview coverage of upcoming 3.11 features.This handy guide will help you:Learn how Python represents data and program as objectsUnderstand the value and uses of type annotationsExamine which language features appeared in which recent versionsDiscover how to use modern Python idiomaticallyLearn ways to structure Python projects appropriatelyUnderstand how to debug Python code
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Python: Advanced Guide to Artificial Intelligence
Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problemsKey FeaturesMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and moreBuild, deploy, and scale end-to-end deep neural network models in a production environmentBook DescriptionThis Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problemsThis Learning Path includes content from the following Packt products:Mastering Machine Learning Algorithms by Giuseppe BonaccorsoMastering TensorFlow 1.x by Armando FandangoDeep Learning for Computer Vision by Rajalingappaa ShanmugamaniWhat you will learnExplore how an ML model can be trained, optimized, and evaluatedWork with Autoencoders and Generative Adversarial NetworksExplore the most important Reinforcement Learning techniquesBuild end-to-end deep learning (CNN, RNN, and Autoencoders) modelsWho this book is forThis Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.
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