David Stephenson - książki
Tytuły autora: dostępne w księgarni Ebookpoint
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Spark. Rozproszone uczenie maszynowe na dużą skalę. Jak korzystać z MLlib, TensorFlow i PyTorch
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Prompt Engineering - zostań Panem Sztucznej Inteligencji
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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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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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The Machine Learning Solutions Architect Handbook. Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI - Second Edition
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Aplikacje ChatGPT. Wejdź na wyższy poziom z inteligentnymi programami - generatory, boty i wiele innych!
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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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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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Effective Machine Learning Teams
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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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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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TinyML Cookbook. Combine machine learning with microcontrollers to solve real-world problems - Second Edition
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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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Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym
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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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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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Probabilistic Machine Learning for Finance and Investing
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Graph-Powered Analytics and Machine Learning with TigerGraph
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Potoki danych. Leksykon kieszonkowy. Przenoszenie i przetwarzanie danych na potrzeby ich analizy
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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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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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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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Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage
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Praktyczne uczenie maszynowe w języku R
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Matematyka w uczeniu maszynowym
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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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Generative Deep Learning. 2nd Edition
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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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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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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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The Kaggle Book. Data analysis and machine learning for competitive data science
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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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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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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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Machine Learning with PyTorch and Scikit-Learn. Develop machine learning and deep learning models with Python
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TinyML. Wykorzystanie TensorFlow Lite do uczenia maszynowego na Arduino i innych mikrokontrolerach
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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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The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets
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Machine Learning for Financial Risk Management with Python
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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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IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
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Uczenie głębokie i sztuczna inteligencja. Interaktywny przewodnik ilustrowany
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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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Reliable Machine Learning
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Machine Learning Engineering with MLflow. Manage the end-to-end machine learning life cycle with MLflow
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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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Practical Machine Learning for Computer Vision
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Sztuczna inteligencja. Błyskawiczne wprowadzenie do uczenia maszynowego, uczenia ze wzmocnieniem i uczenia głębokiego
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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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Deep learning dla programistów. Budowanie aplikacji AI za pomocą fastai i PyTorch
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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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Machine learning, Python i data science. Wprowadzenie
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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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Umiejętności analityczne w pracy z danymi i sztuczną inteligencją. Wykorzystywanie najnowszych technologii w rozwijaniu przedsiębiorstwa
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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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Deep learning i modelowanie generatywne. Jak nauczyć komputer malowania, pisania, komponowania i grania
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Przetwarzanie języka naturalnego w akcji
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Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
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Python. Machine learning i deep learning. Biblioteki scikit-learn i TensorFlow 2. Wydanie III
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Machine Learning Using TensorFlow Cookbook. Create powerful machine learning algorithms with TensorFlow
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Odsłaniamy SQL Server 2019: Klastry Big Data i uczenie maszynowe
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Python dla DevOps. Naucz się bezlitośnie skutecznej automatyzacji
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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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Python Machine Learning By Example. Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn - Third Edition
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Kubeflow for Machine Learning
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Machine Learning and Data Science Blueprints for Finance
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Uczenie maszynowe na Raspberry Pi
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Wprowadzenie do uczenia maszynowego według Esposito
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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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The Deep Learning Workshop. Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras
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The Deep Learning with Keras Workshop. Learn how to define and train neural network models with just a few lines of code
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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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Uczenie głębokie od zera. Podstawy implementacji w Pythonie
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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 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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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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Człowiek na rozdrożu. Sztuczna inteligencja 25 punktów widzenia
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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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Hands-On Music Generation with Magenta. Explore the role of deep learning in music generation and assisted music composition
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Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models
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Advanced Deep Learning with R. Become an expert at designing, building, and improving advanced neural network models using R
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TinyML. Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
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TensorFlow. 13 praktycznych projektów wykorzystujących uczenie maszynowe
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Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Third Edition
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Big data, nauka o danych i AI bez tajemnic. Podejmuj lepsze decyzje i rozwijaj swój biznes!
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Applied Deep Learning on Graphs. Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures
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Python Machine Learning By Example. Unlock machine learning best practices with real-world use cases - Fourth Edition
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Google Machine Learning and Generative AI for Solutions Architects. Build efficient and scalable AI/ML solutions on Google Cloud