Ronald T. Kneusel - książki
Tytuły autora: dostępne w księgarni Ebookpoint
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Deep Learning at Scale
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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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Accelerate Model Training with PyTorch 2.X. Build more accurate models by boosting the model training process
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Uczenie maszynowe w Pythonie. Receptury. Od przygotowania danych do deep learningu. Wydanie II
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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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Bayesian Analysis with Python. A practical guide to probabilistic modeling - Third Edition
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MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations
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Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads
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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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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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Architecting Data and Machine Learning Platforms
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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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Przetwarzanie języka naturalnego w praktyce. Przewodnik po budowie rzeczywistych systemów NLP
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Podręcznik architekta rozwiązań. Poznaj reguły oraz strategie projektu architektury i rozpocznij niezwykłą karierę. Wydanie II
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Graph-Powered Analytics and Machine Learning with TigerGraph
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Machine Learning for High-Risk Applications
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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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Uczenie maszynowe. Elementy matematyki w analizie danych
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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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Matematyka w uczeniu maszynowym
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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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Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes
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Natural Language Processing with Transformers, Revised 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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Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python
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Machine Learning with PyTorch and Scikit-Learn. Develop machine learning and deep learning models with Python
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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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The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets
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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 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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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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Algorytmy sztucznej inteligencji. Ilustrowany przewodnik
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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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Python dla DevOps. Naucz się bezlitośnie skutecznej automatyzacji
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Machine Learning Design Patterns
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Uczenie maszynowe na Raspberry Pi
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The Deep Learning with PyTorch Workshop. Build deep neural networks and artificial intelligence applications with PyTorch
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The Machine Learning Workshop. Get ready to develop your own high-performance machine learning algorithms with scikit-learn - Second Edition
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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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Deep Learning with TensorFlow 2 and Keras. Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API - Second Edition
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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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Google BigQuery: The Definitive Guide. Data Warehousing, Analytics, and Machine Learning at Scale
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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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Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
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Machine Learning for Data Mining. Improve your data mining capabilities with advanced predictive modeling
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TensorFlow Reinforcement Learning Quick Start Guide. Get up and running with training and deploying intelligent, self-learning agents using Python
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Mastering OpenCV 4 with Python. A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7
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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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Python. Uczenie maszynowe. Wydanie II
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Deep Learning. Praca z językiem R i biblioteką Keras
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Uczenie maszynowe w Pythonie. Receptury
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The Enterprise Big Data Lake. Delivering the Promise of Big Data and Data Science
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Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python
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Machine Learning with the Elastic Stack. Expert techniques to integrate machine learning with distributed search and analytics
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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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Python Deep Learning. Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow - Second Edition
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Generative Adversarial Networks Cookbook. Over 100 recipes to build generative models using Python, TensorFlow, and Keras
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Hands-On Machine Learning for Cybersecurity. Safeguard your system by making your machines intelligent using the Python ecosystem
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Python: Advanced Guide to Artificial Intelligence. Expert machine learning systems and intelligent agents using Python
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Machine Learning in Java. Helpful techniques to design, build, and deploy powerful machine learning applications in Java - Second Edition
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Deep Learning. Uczenie głębokie z językiem Python. Sztuczna inteligencja i sieci neuronowe
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Python Reinforcement Learning Projects. Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
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Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras
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Hands-On Deep Learning for Images with TensorFlow. Build intelligent computer vision applications using TensorFlow and Keras
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Hands-On Intelligent Agents with OpenAI Gym. Your guide to developing AI agents using deep reinforcement learning
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Hands-On Natural Language Processing with Python. A practical guide to applying deep learning architectures to your NLP applications
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Machine Learning Solutions. Expert techniques to tackle complex machine learning problems using Python
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TensorFlow Deep Learning Projects. 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning
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Machine Learning with Swift. Artificial Intelligence for iOS
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R Deep Learning Projects. Master the techniques to design and develop neural network models in R
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Deep Learning for Computer Vision. Expert techniques to train advanced neural networks using TensorFlow and Keras
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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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Neural Networks with R. Build smart systems by implementing popular deep learning models in R
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Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark
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Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - 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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Inteligentna sieć. Algorytmy przyszłości. Wydanie II
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Deep Learning with Keras. Implementing deep learning models and neural networks with the power of Python
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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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Large Scale Machine Learning with Python. Click here to enter text
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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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Building a Recommendation System with R. Learn the art of building robust and powerful recommendation engines using R
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Sztuczna inteligencja na froncie. Kurs video. Uczenie maszynowe w JavaScript
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Applied Deep Learning on Graphs. Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures
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Matematyka w deep learningu. Co musisz wiedzieć, aby zrozumieć sieci neuronowe