Valliappa Lakshmanan, Sara Robinson, Michael Munn - książki
Tytuły autora: dostępne w księgarni Ebookpoint
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Deep Learning at Scale
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Data Modeling with Microsoft Power BI
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Augmented Analytics
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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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Aplikacje ChatGPT. Wejdź na wyższy poziom z inteligentnymi programami - generatory, boty i wiele innych!
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Active Machine Learning with Python. Refine and elevate data quality over quantity with active learning
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Fundamentals of Analytics Engineering. An introduction to building end-to-end analytics solutions
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The Definitive Guide to Data Integration. Unlock the power of data integration to efficiently manage, transform, and analyze data
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Artificial Intelligence with Microsoft Power BI
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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide. The ultimate guide to passing the MLS-C01 exam on your first attempt - Second Edition
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Data-Centric Machine Learning with Python. The ultimate guide to engineering and deploying high-quality models based on good data
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Transformers for Natural Language Processing and Computer Vision. Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 - Third Edition
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Bayesian Analysis with Python. A practical guide to probabilistic modeling - Third Edition
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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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MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations
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Data Observability for Data Engineering. Proactive strategies for ensuring data accuracy and addressing broken data pipelines
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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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Practical Guide to Applied Conformal Prediction in Python. Learn and apply the best uncertainty frameworks to your industry applications
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Machine Learning for Imbalanced Data. Tackle imbalanced datasets using machine learning and deep learning techniques
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TinyML Cookbook. Combine machine learning with microcontrollers to solve real-world problems - Second Edition
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Power BI i sztuczna inteligencja. Jak w pełni wykorzystać funkcje AI dostępne w Power BI
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Zostań Milionerem z ChatGPT. Prosty przewodnik jak osiągnąć sukces w każdej branży za pomocą sztucznej inteligencji
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Interpretable Machine Learning with Python. Build explainable, fair, and robust high-performance models with hands-on, real-world examples - Second Edition
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Synthetic Data for Machine Learning. Revolutionize your approach to machine learning with this comprehensive conceptual guide
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Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym
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Architecting Data and Machine Learning Platforms
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Google Analytics od podstaw. Analiza wpływu biznesowego i wyznaczanie trendów
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Machine Learning for Emotion Analysis in Python. Build AI-powered tools for analyzing emotion using natural language processing and machine learning
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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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Przetwarzanie języka naturalnego w praktyce. Przewodnik po budowie rzeczywistych systemów NLP
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AI & Data Literacy. Empowering Citizens of Data Science
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Microsoft Power BI dla bystrzaków
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Graph-Powered Analytics and Machine Learning with TigerGraph
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Enhancing Deep Learning with Bayesian Inference. Create more powerful, robust deep learning systems with Bayesian deep learning in Python
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Geospatial Data Analytics on AWS. Discover how to manage and analyze geospatial data in the cloud
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Graph Data Modeling in Python. A practical guide to curating, analyzing, and modeling data with graphs
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Inżynieria danych w praktyce. Kluczowe koncepcje i najlepsze technologie
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Zaufanie do systemów sztucznej inteligencji
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Siatka danych. Nowoczesna koncepcja samoobsługowej infrastruktury danych
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Building an Event-Driven Data Mesh
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Analityka biznesowa wspomagana sztuczną inteligencją. Ulepszanie prognoz i podejmowania decyzji za pomocą uczenia maszynowego
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Machine Learning in Microservices. Productionizing microservices architecture for machine learning solutions
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Jak sztuczna inteligencja zmieni twoje życie
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The Kaggle Workbook. Self-learning exercises and valuable insights for Kaggle data science competitions
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DAX i Power BI w analizie danych. Tworzenie zaawansowanych i efektywnych analiz dla biznesu
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Tomographic imaging in environmental, industrial and medical applications
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Practicing Trustworthy Machine Learning
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Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning
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Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage
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Neural Search - From Prototype to Production with Jina. Build deep learning–powered search systems that you can deploy and manage with ease
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Matematyka w uczeniu maszynowym
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Serverless ETL and Analytics with AWS Glue. Your comprehensive reference guide to learning about AWS Glue and its features
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SQL for Data Analytics. Harness the power of SQL to extract insights from data - Third Edition
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Cyfrowe Państwo. Uwarunkowania i perspektywy
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Głębokie uczenie. Wprowadzenie
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Quantum Computing Experimentation with Amazon Braket. Explore Amazon Braket quantum computing to solve combinatorial optimization problems
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Simplifying Data Engineering and Analytics with Delta. Create analytics-ready data that fuels artificial intelligence and business intelligence
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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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Głębokie uczenie przez wzmacnianie. Praca z chatbotami oraz robotyka, optymalizacja dyskretna i automatyzacja sieciowa w praktyce. Wydanie II
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Tidy Modeling with R
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Practical Deep Learning at Scale with MLflow. Bridge the gap between offline experimentation and online production
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Feature Store for Machine Learning. Curate, discover, share and serve ML features at scale
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In-Memory Analytics with Apache Arrow. Perform fast and efficient data analytics on both flat and hierarchical structured data
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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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Fundamentals of Data Engineering
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The Pandas Workshop. A comprehensive guide to using Python for data analysis with real-world case studies
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AI-Powered Business Intelligence
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Practical Simulations for Machine Learning
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Natural Language Processing with Transformers, Revised Edition
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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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Artificial Intelligence with Power BI. Take your data analytics skills to the next level by leveraging the AI capabilities in Power BI
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Natural Language Processing with Flair. A practical guide to understanding and solving NLP problems with Flair
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The Kaggle Book. Data analysis and machine learning for competitive data science
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Google Analytics w biznesie. Poradnik dla zaawansowanych. Wydanie II
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Data Algorithms with Spark
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TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter
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Transformers for Natural Language Processing. Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4 - Second Edition
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Data Mesh
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Extreme DAX. Take your Power BI and Microsoft data analytics skills to the next level
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Digital Transformation and Modernization with IBM API Connect. A practical guide to developing, deploying, and managing high-performance and secure hybrid-cloud APIs
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Google Analytics dla marketingowców. Wydanie III
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The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets
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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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Digital Transformation with Dataverse for Teams. Become a citizen developer and lead the digital transformation wave with Microsoft Teams and Power Platform
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Data Engineering with Apache Spark, Delta Lake, and Lakehouse. Create scalable pipelines that ingest, curate, and aggregate complex data in a timely and secure way
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Deep Learning with fastai Cookbook. Leverage the easy-to-use fastai framework to unlock the power of deep learning
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Data Science for Marketing Analytics. A practical guide to forming a killer marketing strategy through data analysis with Python - Second Edition
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Data Analytics Made Easy. Analyze and present data to make informed decisions without writing any code
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Practical Machine Learning for Computer Vision
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Wzorce projektowe uczenia maszynowego. Rozwiązania typowych problemów dotyczących przygotowania danych, konstruowania modeli i MLOps
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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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Limitless Analytics with Azure Synapse. An end-to-end analytics service for data processing, management, and ingestion for BI and ML
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97 Things Every Data Engineer Should Know
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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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Dane grafowe w praktyce. Jak technologie grafowe ułatwiają rozwiązywanie złożonych problemów
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Mastering Tableau 2021. Implement advanced business intelligence techniques and analytics with Tableau - Third Edition
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Quantum Computing with Silq Programming. Get up and running with quantum computing with the simplicity of this new high-level programming language
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Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale
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Scalable Data Streaming with Amazon Kinesis. Design and secure highly available, cost-effective data streaming applications with Amazon Kinesis
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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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Machine Learning Using TensorFlow Cookbook. Create powerful machine learning algorithms with TensorFlow
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Python Data Analysis. Perform data collection, data processing, wrangling, visualization, and model building using Python - Third Edition
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Uczenie maszynowe w aplikacjach. Projektowanie, budowa i wdrażanie
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Introducing MLOps
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The Economics of Data, Analytics, and Digital Transformation. The theorems, laws, and empowerments to guide your organization’s digital transformation
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Python dla DevOps. Naucz się bezlitośnie skutecznej automatyzacji
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Microsoft Excel 2013 Budowanie modeli danych przy użyciu PowerPivot
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Microsoft SQL Server 2012 Analysis Services: Model tabelaryczny BISM
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Machine Learning Design Patterns
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Pandas Cookbook. Practical recipes for scientific computing, time series and exploratory data analysis using Python - Third Edition
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Becoming a Data Analyst. A beginner's guide to kickstarting your data analysis journey
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Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition
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Responsible AI Made Easy with TensorFlow. The Ultimate Roadmap to Ethical AI: A Practical Guide to AI Fairness, Accountability, and Transparency
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Analiza statystyczna z IBM SPSS Statistics