Yuxi (Hayden) Liu, Pablo Maldonado - książki
Tytuły autora: dostępne w księgarni Ebookpoint
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De-Mystifying Math and Stats for Machine Learning. Mastering the Fundamentals of Mathematics and Statistics for Machine Learning
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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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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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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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Bayesian Analysis with Python. A practical guide to probabilistic modeling - Third Edition
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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 with MXNet Cookbook. Discover an extensive collection of recipes for creating and implementing AI models on MXNet
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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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Architecting Data and Machine Learning Platforms
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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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Zaufanie do systemów sztucznej inteligencji
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Sztuczna inteligencja od podstaw
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Machine Learning for High-Risk Applications
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Scaling Machine Learning with Spark
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Jak projektować systemy uczenia maszynowego. Iteracyjne tworzenie aplikacji gotowych do pracy
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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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Practicing Trustworthy Machine Learning
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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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Praktyczne uczenie maszynowe w języku R
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Matematyka w uczeniu maszynowym
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Głębokie uczenie. Wprowadzenie
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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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Głębokie uczenie przez wzmacnianie. Praca z chatbotami oraz robotyka, optymalizacja dyskretna i automatyzacja sieciowa w praktyce. Wydanie II
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Sztuczna inteligencja w finansach. Używaj języka Python do projektowania i wdrażania algorytmów AI
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Projektowanie głosowych interfejsów użytkownika. Zasady doświadczeń konwersacyjnych
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Practical Simulations for Machine Learning
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Designing Machine Learning Systems
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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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Democratizing Artificial Intelligence with UiPath. Expand automation in your organization to achieve operational efficiency and high performance
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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. Wykorzystanie TensorFlow Lite do uczenia maszynowego na Arduino i innych mikrokontrolerach
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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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IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
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Reliable Machine Learning
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Practical Weak Supervision
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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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Sztuczna inteligencja. Błyskawiczne wprowadzenie do uczenia maszynowego, uczenia ze wzmocnieniem i uczenia głębokiego
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PyTorch Pocket Reference
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Przetwarzanie języka naturalnego w akcji
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Kubeflow Operations Guide
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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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Artificial Intelligence in Finance
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Machine Learning and Data Science Blueprints for Finance
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Praktyczne uczenie nienadzorowane przy użyciu języka Python
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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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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 Unsupervised Learning Workshop. Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions
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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 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 Machine Learning with ML.NET. Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#
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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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Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition
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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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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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Big data, nauka o danych i AI bez tajemnic. Podejmuj lepsze decyzje i rozwijaj swój biznes!
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Google BigQuery: The Definitive Guide. Data Warehousing, Analytics, and Machine Learning at Scale
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Practical Automated Machine Learning on Azure. Using Azure Machine Learning to Quickly Build AI Solutions
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Programming PyTorch for Deep Learning. Creating and Deploying Deep Learning Applications
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Practical Data Science with SAP. Machine Learning Techniques for Enterprise Data
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Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications
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Algorytmy uczenia maszynowego. Zaawansowane techniki implementacji
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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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PyTorch Deep Learning Hands-On. Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
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Hands-On Neural Networks with Keras. Design and create neural networks using deep learning and artificial intelligence principles
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Python Machine Learning Cookbook. Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets - Second Edition
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Machine Learning with R Quick Start Guide. A beginner's guide to implementing machine learning techniques from scratch using R 3.5
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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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Hands-On Unsupervised Learning with Python. Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
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Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python
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Intelligent Projects Using Python. 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras
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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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Keras 2.x Projects. 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
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Python: Advanced Guide to Artificial Intelligence. Expert machine learning systems and intelligent agents using Python
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Hands-On Data Science with R. Techniques to perform data manipulation and mining to build smart analytical models using R
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Hands-On Image Processing with Python. Expert techniques for advanced image analysis and effective interpretation of image data
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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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TensorFlow Machine Learning Projects. Build 13 real-world projects with advanced numerical computations using the Python ecosystem
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Hands-On Artificial Intelligence for Beginners. An introduction to AI concepts, algorithms, and their implementation
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Hands-On Machine Learning with Azure. Build powerful models with cognitive machine learning and artificial intelligence
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Keras Deep Learning Cookbook. Over 30 recipes for implementing deep neural networks in Python
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Deep Learning. Uczenie głębokie z językiem Python. Sztuczna inteligencja i sieci neuronowe
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Keras Reinforcement Learning Projects. 9 projects exploring popular reinforcement learning techniques to build self-learning agents
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Python Reinforcement Learning Projects. Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
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Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem
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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 Artificial Intelligence for Search. Building intelligent applications and perform enterprise searches
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Machine Learning Algorithms. Popular algorithms for data science and machine learning - Second Edition
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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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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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Hands-On Intelligent Agents with OpenAI Gym. Your guide to developing AI agents using deep reinforcement learning
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Hands-On Ensemble Learning with R. A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
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Deep Learning. Praktyczne wprowadzenie
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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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Mastering Machine Learning Algorithms. Expert techniques to implement popular machine learning algorithms and fine-tune your models
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Artificial Intelligence for Big Data. Complete guide to automating Big Data solutions using Artificial Intelligence techniques
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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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TensorFlow Deep Learning Projects. 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning
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Practical Convolutional Neural Networks. Implement advanced deep learning models using Python
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R Deep Learning Projects. Master the techniques to design and develop neural network models in R
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Google Machine Learning and Generative AI for Solutions Architects. Build efficient and scalable AI/ML solutions on Google Cloud