Ronald T. Kneusel - książki
Tytuły autora: dostępne w księgarni Ebookpoint
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Dylemat sztucznej inteligencji. 7 zasad odpowiedzialnego tworzenia technologii
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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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Hands-On Entity Resolution
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Delta Lake: Up and Running
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Podręcznik architekta rozwiązań. Poznaj reguły oraz strategie projektu architektury i rozpocznij niezwykłą karierę. Wydanie II
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Uczenie maszynowe z użyciem Scikit-Learn, Keras i TensorFlow. Wydanie III
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Zaufanie do systemów sztucznej inteligencji
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Sztuczna inteligencja od podstaw
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Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 1
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Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 2
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Deep Learning. Praktyczne wprowadzenie z zastosowaniem środowiska Pythona
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 3rd Edition
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Deep learning z TensorFlow 2 i Keras dla zaawansowanych. Sieci GAN i VAE, deep RL, uczenie nienadzorowane, wykrywanie i segmentacja obiektów i nie tylko. Wydanie II
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Practical Simulations for Machine Learning
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Mastering Azure Machine Learning. Execute large-scale end-to-end machine learning with Azure - Second Edition
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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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Time Series Analysis on AWS. Learn how to build forecasting models and detect anomalies in your time series data
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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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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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Uczenie głębokie i sztuczna inteligencja. Interaktywny przewodnik ilustrowany
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Exploring GPT-3. An unofficial first look at the general-purpose language processing API from OpenAI
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Wzorce projektowe uczenia maszynowego. Rozwiązania typowych problemów dotyczących przygotowania danych, konstruowania modeli i MLOps
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Sztuczna inteligencja. Błyskawiczne wprowadzenie do uczenia maszynowego, uczenia ze wzmocnieniem i uczenia głębokiego
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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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Umiejętności analityczne w pracy z danymi i sztuczną inteligencją. Wykorzystywanie najnowszych technologii w rozwijaniu przedsiębiorstwa
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Uczenie maszynowe w aplikacjach. Projektowanie, budowa i wdrażanie
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Kubeflow Operations Guide
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Practical Fairness
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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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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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Machine Learning Design Patterns
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Praktyczne uczenie nienadzorowane przy użyciu języka Python
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Szeregi czasowe. Praktyczna analiza i predykcja z wykorzystaniem statystyki i uczenia maszynowego
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Uczenie maszynowe z użyciem Scikit-Learn i TensorFlow. Wydanie II
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Building Machine 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 Machine Learning with ML.NET. Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#
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Building Machine Learning Powered Applications. Going from Idea to Product
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TinyML. Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
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Machine Learning for Cybersecurity Cookbook. Over 80 recipes on how to implement machine learning algorithms for building security systems using Python
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Praktyczne uczenie maszynowe
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Practical Time Series Analysis. Prediction with Statistics and Machine Learning
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Deep Learning
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Machine Learning for OpenCV 4. Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn - Second Edition
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Hands-On Deep Learning with Go. A practical guide to building and implementing neural network models using Go
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Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
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The Enterprise Big Data Lake. Delivering the Promise of Big Data and Data Science
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Python Machine Learning Blueprints. Put your machine learning concepts to the test by developing real-world smart projects - Second Edition
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Natural Language Processing with PyTorch. Build Intelligent Language Applications Using Deep Learning
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Python Deep Learning. Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow - Second Edition
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Practical Site Reliability Engineering. Automate the process of designing, developing, and delivering highly reliable apps and services with SRE
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Advanced Deep Learning with Keras. Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
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Deep Learning. Uczenie głębokie z językiem Python. Sztuczna inteligencja i sieci neuronowe
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R Programming Fundamentals. Deal with data using various modeling techniques
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Hands-On Artificial Intelligence for Search. Building intelligent applications and perform enterprise searches
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Hands-On Convolutional Neural Networks with TensorFlow. Solve computer vision problems with modeling in TensorFlow and Python
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Uczenie maszynowe z użyciem Scikit-Learn i TensorFlow
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Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition
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Splunk 7 Essentials. Demystify machine data by leveraging datasets, building reports, and sharing powerful insights - Third Edition
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OpenCV 3.x with Python By Example. Make the most of OpenCV and Python to build applications for object recognition and augmented reality - Second Edition
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Machine Learning With Go. Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language
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Learning TensorFlow. A Guide to Building Deep Learning Systems
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Hands-On Deep Learning with TensorFlow. Uncover what is underneath your data!
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Machine Learning for OpenCV. Intelligent image processing with Python
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Data Science i uczenie maszynowe
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Mastering OpenCV 3. Get hands-on with practical Computer Vision using OpenCV 3 - Second Edition
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Python Deep Learning. Next generation techniques to revolutionize computer vision, AI, speech and data analysis
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Windows Server 2016 Hyper-V Cookbook. Save time and resources by getting to know the best practices and intelligence from industry experts - Second Edition
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Test-Driven Machine Learning. Control your machine learning algorithms using test-driven development to achieve quantifiable milestones
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Learning Bayesian Models with R. Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems
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Building a Recommendation System with R. Learn the art of building robust and powerful recommendation engines using R
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Zwinna analiza danych. Apache Hadoop dla każdego
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R Machine Learning Essentials. Gain quick access to the machine learning concepts and practical applications using the R development environment
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Practical Machine Learning: Innovations in Recommendation
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Practical Machine Learning: A New Look at Anomaly Detection
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Designing Data Visualizations. Representing Informational Relationships
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Applied Deep Learning on Graphs. Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures
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Building Machine Learning Systems with Python
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Data science, trudne elementy. Jak stać się ekspertem w danologii
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Matematyka w deep learningu. Co musisz wiedzieć, aby zrozumieć sieci neuronowe