Sean Saito, Yang Wenzhuo, Rajalingappaa Shanmugamani - książki
Tytuły autora: dostępne w księgarni Ebookpoint
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Introduction to Algorithms. A Comprehensive Guide for Beginners: Unlocking Computational Thinking
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Data Analysis Foundations with Python. Master Data Analysis with Python: From Basics to Advanced Techniques
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Before Machine Learning Volume 1 - Linear Algebra for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence
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Uczenie maszynowe w Pythonie. Receptury. Od przygotowania danych do deep learningu. Wydanie II
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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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Active Machine Learning with Python. Refine and elevate data quality over quantity with active learning
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Effective Machine Learning Teams
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Eksploracja danych za pomocą Excela. Metody uczenia maszynowego krok po kroku
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MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations
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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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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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Implementing MLOps in the Enterprise
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Machine Learning Interviews
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TinyML Cookbook. Combine machine learning with microcontrollers to solve real-world problems - Second Edition
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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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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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Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym
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TensorFlow Developer Certificate Guide. Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam
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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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Uczenie maszynowe z użyciem Scikit-Learn, Keras i TensorFlow. Wydanie III
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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 with TensorFlow and Keras. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 3rd Edition
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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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Inżynieria danych na platformie AWS. Jak tworzyć kompletne potoki uczenia maszynowego
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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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Tidy Modeling with R
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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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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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Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems
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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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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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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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Reproducible Data Science with Pachyderm. Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0
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Machine Learning with PyTorch and Scikit-Learn. Develop machine learning and deep learning models with Python
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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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Conversational AI with Rasa. Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots
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Practical Weak Supervision
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Exploring GPT-3. An unofficial first look at the general-purpose language processing API from OpenAI
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Practical Machine Learning for Computer Vision
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Deep learning dla programistów. Budowanie aplikacji AI za pomocą fastai i PyTorch
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Machine learning, Python i data science. Wprowadzenie
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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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Umiejętności analityczne w pracy z danymi i sztuczną inteligencją. Wykorzystywanie najnowszych technologii w rozwijaniu przedsiębiorstwa
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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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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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Kubeflow for Machine Learning
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Machine Learning and Data Science Blueprints for Finance
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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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Uczenie maszynowe z użyciem Scikit-Learn i TensorFlow. Wydanie II
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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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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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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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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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Jak myślą inteligentne maszyny
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Tłumaczenie wspomagane komputerowo
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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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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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TensorFlow. 13 praktycznych projektów wykorzystujących uczenie maszynowe
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Głębokie uczenie z TensorFlow. Od regresji liniowej po uczenie przez wzmacnianie
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Praktyczne uczenie maszynowe
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Google BigQuery: The Definitive Guide. Data Warehousing, Analytics, and Machine Learning at Scale
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Algorytmy Data Science. Siedmiodniowy przewodnik. Wydanie II
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Practical Automated Machine Learning on Azure. Using Azure Machine Learning to Quickly Build AI Solutions
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Practical Data Science with SAP. Machine Learning Techniques for Enterprise Data
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Deep Learning
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Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications
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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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Hands-On Ensemble Learning with Python. Build highly optimized ensemble machine learning models using scikit-learn and Keras
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Algorytmy uczenia maszynowego. Zaawansowane techniki implementacji
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Deep Learning. Receptury
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Deep Learning for Natural Language Processing. Solve your natural language processing problems with smart deep neural networks
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Advanced Machine Learning with R. Tackle data analytics and machine learning challenges and build complex applications with R 3.5
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Mastering Machine Learning on AWS. Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow
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Hands-On Machine Learning with Microsoft Excel 2019. Build complete data analysis flows, from data collection to visualization
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Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras
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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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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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The Enterprise Big Data Lake. Delivering the Promise of Big Data and Data Science
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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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R Machine Learning Projects. Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
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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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Artificial Intelligence and Machine Learning Fundamentals. Develop real-world applications powered by the latest AI advances
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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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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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Machine Learning in Java. Helpful techniques to design, build, and deploy powerful machine learning applications in Java - Second Edition
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Data Science Algorithms in a Week. Top 7 algorithms for scientific computing, data analysis, and machine learning - Second Edition
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Keras Deep Learning Cookbook. Over 30 recipes for implementing deep neural networks in Python
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Machine Learning Projects for Mobile Applications. Build Android and iOS applications using TensorFlow Lite and Core ML
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Python Deep Learning Projects. 9 projects demystifying neural network and deep learning models for building intelligent systems
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Machine Learning for Healthcare Analytics Projects. Build smart AI applications using neural network methodologies across the healthcare vertical market
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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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Google Machine Learning and Generative AI for Solutions Architects. Build efficient and scalable AI/ML solutions on Google Cloud
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Budowanie systemów inteligentnych
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems. 2nd Edition