Uczenie maszynowe stanowi jeden z najbardziej fascynujących i dynamicznie rozwijających się obszarów technologii informatycznej. W księgarni internetowej helion.pl oferujemy szeroki zakres książek oraz kursów video, które pomogą Ci zgłębić tajniki tej dziedziny.
Uczenie maszynowe
Książki, ebooki, kursy video z kategorii: Uczenie maszynowe dostępne w księgarni Helion
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Hands-On Machine Learning with ML.NET. Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#
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Hands-On Q-Learning with Python. Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
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Building Computer Vision Projects with OpenCV 4 and C++. Implement complex computer vision algorithms and explore deep learning and face detection
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Inteligentna sieć. Algorytmy przyszłości. Wydanie II
Czasowo niedostępna
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Databricks ML in Action. Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment
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Accelerate Model Training with PyTorch 2.X. Build more accurate models by boosting the model training process
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Active Machine Learning with Python. Refine and elevate data quality over quantity with active learning
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Deep Learning for Time Series Cookbook. Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
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Machine Learning: Make Your Own Recommender System. Build Your Recommender System with Machine Learning Insights
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Machine Learning with Python. Unlocking AI Potential with Python and Machine Learning
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Hands-On Entity Resolution
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Data Labeling in Machine Learning with Python. Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models
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Machine Learning Infrastructure and Best Practices for Software Engineers. Take your machine learning software from a prototype to a fully fledged software system
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MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations
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MATLAB for Machine Learning. Unlock the power of deep learning for swift and enhanced results - Second Edition
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Deep Learning for Finance
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Deep Learning with MXNet Cookbook. Discover an extensive collection of recipes for creating and implementing AI models on MXNet
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Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads
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Machine Learning Interviews
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Training Data for Machine Learning
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Machine Learning with Qlik Sense. Utilize different machine learning models in practical use cases by leveraging Qlik Sense
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The Statistics and Machine Learning with R Workshop. Unlock the power of efficient data science modeling with this hands-on guide
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Delta Lake: Up and Running
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Machine Learning with LightGBM and Python. A practitioner's guide to developing production-ready machine learning systems
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Debugging Machine Learning Models with Python. Develop high-performance, low-bias, and explainable machine learning and deep learning models
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Machine Learning Engineering with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition
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Graph-Powered Analytics and Machine Learning with TigerGraph
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Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data
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Machine Learning for High-Risk Applications
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Computer Vision on AWS. Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker
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Scaling Machine Learning with Spark
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Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems
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The Kaggle Workbook. Self-learning exercises and valuable insights for Kaggle data science competitions
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Democratizing Application Development with Betty Blocks. Build powerful applications that impact business immediately with no-code app development
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Practicing Trustworthy Machine Learning
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Transforming Healthcare with DevOps. A practical DevOps4Care guide to embracing the complexity of digital transformation
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Applied Machine Learning and AI for Engineers
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Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage
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Deep Learning with TensorFlow and Keras. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition
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Hands-On Healthcare Data
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Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems
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Natural Language Processing with TensorFlow. The definitive NLP book to implement the most sought-after machine learning models and tasks - Second Edition
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Simplifying Android Development with Coroutines and Flows. Learn how to use Kotlin coroutines and the flow API to handle data streams asynchronously in your Android app
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Tidy Modeling with R
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Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes
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Designing Autonomous AI
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Practical Simulations for Machine Learning
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Building Data Science Solutions with Anaconda. A comprehensive starter guide to building robust and complete models
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Fundamentals of Deep Learning. 2nd Edition
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Mastering Azure Machine Learning. Execute large-scale end-to-end machine learning with Azure - Second Edition
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Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems
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Democratizing Artificial Intelligence with UiPath. Expand automation in your organization to achieve operational efficiency and high performance
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Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python
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Natural Language Processing with Flair. A practical guide to understanding and solving NLP problems with Flair
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Essential Mathematics for Quantum Computing. A beginner's guide to just the math you need without needless complexities
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Automated Machine Learning on AWS. Fast-track the development of your production-ready machine learning applications the AWS way
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TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter
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Getting Started with Amazon SageMaker Studio. Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
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Unity Artificial Intelligence Programming. Add powerful, believable, and fun AI entities in your game with the power of Unity - Fifth Edition
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Reproducible Data Science with Pachyderm. Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0
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Modern Mainframe Development
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Time Series Analysis on AWS. Learn how to build forecasting models and detect anomalies in your time series data
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Machine Learning in Biotechnology and Life Sciences. Build machine learning models using Python and deploy them on the cloud
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Intelligent Workloads at the Edge. Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
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Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value
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Azure Data Scientist Associate Certification Guide. A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam
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Learn Amazon SageMaker. A guide to building, training, and deploying machine learning models for developers and data scientists - Second Edition
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IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
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Machine Learning Engineering with Python. Manage the production life cycle of machine learning models using MLOps with practical examples
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Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
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Machine Learning for Time-Series with Python. Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
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Reliable Machine Learning
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Conversational AI with Rasa. Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots
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Practical Weak Supervision
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Exploring GPT-3. An unofficial first look at the general-purpose language processing API from OpenAI
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Getting Started with Streamlit for Data Science. Create and deploy Streamlit web applications from scratch in Python
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AI and Machine Learning for On-Device Development
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Practical Machine Learning for Computer Vision
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Graph Machine Learning. Take graph data to the next level by applying machine learning techniques and algorithms
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Machine Learning with BigQuery ML. Create, execute, and improve machine learning models in BigQuery using standard SQL queries
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Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition
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Automated Machine Learning with AutoKeras. Deep learning made accessible for everyone with just few lines of coding
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Machine Learning Automation with TPOT. Build, validate, and deploy fully automated machine learning models with Python
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Automated Machine Learning with Microsoft Azure. Build highly accurate and scalable end-to-end AI solutions with Azure AutoML
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Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale
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Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples
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AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide. The definitive guide to passing the MLS-C01 exam on the very first attempt
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Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
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Odsłaniamy SQL Server 2019: Klastry Big Data i uczenie maszynowe
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Kubeflow Operations Guide
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Practical Fairness
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Introducing MLOps
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Codeless Deep Learning with KNIME. Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform
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Python Machine Learning By Example. Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn - Third Edition
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Microsoft Power BI Quick Start Guide. Bring your data to life through data modeling, visualization, digital storytelling, and more - Second Edition
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Artificial Intelligence in Finance
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Kubeflow for Machine Learning
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Machine Learning and Data Science Blueprints for Finance
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AI and Machine Learning for Coders
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Deep Learning for Beginners. A beginner's guide to getting up and running with deep learning from scratch using Python
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The Natural Language Processing Workshop. Confidently design and build your own NLP projects with this easy-to-understand practical guide
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Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques
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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits. A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
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The Deep Learning with PyTorch Workshop. Build deep neural networks and artificial intelligence applications with PyTorch
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Hands-On Simulation Modeling with Python. Develop simulation models to get accurate results and enhance decision-making processes
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Building Machine Learning Pipelines
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Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks
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Hands-On Machine Learning with C++. Build, train, and deploy end-to-end machine learning and deep learning pipelines
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Hands-On Python Deep Learning for the Web. Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow
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Mastering Azure Machine Learning. Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning
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Hands-On Deep Learning with R. A practical guide to designing, building, and improving neural network models using R
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Hands-On One-shot Learning with Python. Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch
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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter. Build scalable real-world projects to implement end-to-end neural networks on Android and iOS
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Przetwarzanie i analiza obrazów w systemach przemysłowych. Wybrane zastosowania
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Jak myślą inteligentne maszyny
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Automatyczna analiza składnikowa języka polskiego
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Tłumaczenie wspomagane komputerowo
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Deep Learning with R Cookbook. Over 45 unique recipes to delve into neural network techniques using R 3.5.x
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Wnioskowanie i związki przyczynowe w Pythonie. Nowoczesne uczenie maszynowe z wykorzystaniem bibliotek DoWhy, EconML, PyTorch i nie tylko
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Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
Jakie książki pozwolą na praktyczną naukę uczenia maszynowego?
Zastanawiasz się czym jest Uczenie Maszynowe (Machine Learning)? To technika programowania komputerów, które uczą się wykonywać określone zadania na podstawie ogromnych ilości zebranych danych. W wielu przypadkach to rozwiązanie sprawdza się znacznie lepiej niż tradycyjne metody programowania, szczególnie w obszarach, gdzie trudno jest sformułować jasne reguły decyzyjne. Jeśli szukasz praktycznych przykładów uczenia maszynowego, to książki z tej kategorii oferują wiele case studies i analiz. Nasza oferta obejmuje różnorodne książki, które skupiają się na wszystkich najważniejszych aspektach uczenia maszynowego.
Uczenie maszynowe jak zacząć? – książki dla początkujących
Jeśli dopiero zaczynasz swoją przygodę z tą dziedziną, polecamy książkę "Jak projektować systemy uczenia maszynowego”, która omawia temat uczenia maszynowego od podstaw. Znajdziesz tu także praktyczne poradniki, takie jak "Uczenie maszynowe dla programistów" czy „Uczenie głębokie od zera”, które krok po kroku pokażą Ci, jak zacząć przygodę z uczeniem maszynowym czy uczeniem głębokim. Książki te sprawnie wprowadzą Cię także w zagadnienia związane z metodami uczenia nadzorowanego i nienadzorowanego, omówią kluczowe algorytmy i dostarczą liczne przykłady uczenia maszynowego.
Uczenie maszynowe i Python: książki dla programistów
Jeśli Twoim językiem programowania jest Python, to mamy dla Ciebie wiele propozycji książek. Język ten idealnie nadaje się do programowania mechanizmów uczenia maszynowego. Znajdziesz tu takie książki jak „Uczenie maszynowe w Pythonie. Receptury” oraz „Machine learning, Python i data science. Wprowadzenie” czy „Uczenie maszynowe z użyciem Scikit-Learn, Keras i TensorFlow”, dzięki którym dowiesz się jak korzystać z bibliotek takich jak scikit-learn czy TensorFlow, by efektywnie budować i trenować inteligentne modele.
Uczenie maszynowe a sztuczna inteligencja: książki dla zaawansowanych
Różnica między uczeniem maszynowym a sztuczną inteligencją jest często niejasna. W skrócie, sztuczna inteligencja to szeroki obszar informatyki skupiający się na tworzeniu inteligentnych systemów i maszyn, podczas gdy uczenie maszynowe to jedna z technik stosowanych w AI. Wiele osób interesuje się również sieciami neuronowymi, które stanowią podstawę dla głębokiego uczenia maszynowego - jednego z najgorętszych tematów w dziedzinie AI.
W naszej ofercie znajdziesz również książki opisujące różne algorytmy uczenia maszynowego, w tym drzewa decyzyjne, oraz metody uczenia maszynowego, takie jak uczenie nienadzorowane,
Nie ważne, czy jesteś początkującym entuzjastą czy doświadczonym programistą, nasza oferta obejmuje książki dla każdego. A jeśli preferujesz materiały w formie elektronicznej, nie zapomnij sprawdzić naszych książek w formacie PDF, EPUB czy MOBI.
Zachęcamy do odkrywania świata uczenia maszynowego poprzez nasze książki, które rozwijają umiejętności i otwierają drzwi do nowoczesnej analizy danych i sztucznej inteligencji.