Marcin Szeliga - książki
Tytuły autora: dostępne w księgarni Ebookpoint
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Deep Learning at Scale
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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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Aplikacje ChatGPT. Wejdź na wyższy poziom z inteligentnymi programami - generatory, boty i wiele innych!
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Active Machine Learning with Python. Refine and elevate data quality over quantity with active 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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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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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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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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Graph-Powered Analytics and Machine Learning with TigerGraph
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Zaufanie do systemów sztucznej inteligencji
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Jak sztuczna inteligencja zmieni twoje życie
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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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Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage
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Matematyka w uczeniu maszynowym
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Głębokie uczenie. Wprowadzenie
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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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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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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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The Kaggle Book. Data analysis and machine learning for competitive data science
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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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The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets
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IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
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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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Deep Learning with fastai Cookbook. Leverage the easy-to-use fastai framework to unlock the power of deep learning
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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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Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale
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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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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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Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
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Machine Learning Using TensorFlow Cookbook. Create powerful machine learning algorithms with TensorFlow
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Uczenie maszynowe w aplikacjach. Projektowanie, budowa i wdrażanie
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Introducing MLOps
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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 w Pythonie dla każdego
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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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Uczenie maszynowe w Pythonie. Leksykon kieszonkowy
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Uczenie głębokie od zera. Podstawy implementacji w Pythonie
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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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Automatyczna analiza składnikowa języka polskiego
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The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition
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Building Machine Learning Powered Applications. Going from Idea to Product
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TensorFlow. 13 praktycznych projektów wykorzystujących uczenie maszynowe
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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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Google BigQuery: The Definitive Guide. Data Warehousing, Analytics, and Machine Learning at Scale
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Practical Data Science with SAP. Machine Learning Techniques for Enterprise Data
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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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Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications
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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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Hands-On Deep Learning for IoT. Train neural network models to develop intelligent IoT applications
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Uczenie maszynowe w C#. Szybkie, sprytne i solidne aplikacje
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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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Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
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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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PyTorch Deep Learning Hands-On. Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
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Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras
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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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Machine Learning Quick Reference. Quick and essential machine learning hacks for training smart data models
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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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R Machine Learning Projects. Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
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Python w uczeniu maszynowym
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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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Computer Vision Projects with OpenCV and Python 3. Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow
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Python: Advanced Guide to Artificial Intelligence. Expert machine learning systems and intelligent agents using Python
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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 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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Python Deep Learning Projects. 9 projects demystifying neural network and deep learning models for building intelligent systems
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Deep Learning. Uczenie głębokie z językiem Python. Sztuczna inteligencja i sieci neuronowe
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Hands-On Neural Network Programming with C#. Add powerful neural network capabilities to your C# enterprise applications
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Python Reinforcement Learning Projects. Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
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Mastering Arduino. A project-based approach to electronics, circuits, and programming
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R Deep Learning Essentials. A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet - Second Edition
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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 Natural Language Processing with Python. A practical guide to applying deep learning architectures to your NLP applications
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Artificial Intelligence By Example. Develop machine intelligence from scratch using real artificial intelligence use cases
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Artificial Intelligence for Big Data. Complete guide to automating Big Data solutions using Artificial Intelligence techniques
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Hands-On Automated Machine Learning. A beginner's guide to building automated machine learning systems using AutoML and Python
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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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Building Smart Drones with ESP8266 and Arduino. Build exciting drones by leveraging the capabilities of Arduino and ESP8266
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Deep Learning with PyTorch. A practical approach to building neural network models using PyTorch
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R Deep Learning Projects. Master the techniques to design and develop neural network models in R
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Machine Learning and Security. Protecting Systems with Data and Algorithms
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Deep Learning for Computer Vision. Expert techniques to train advanced neural networks using TensorFlow and Keras
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Mastering TensorFlow 1.x. Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
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Computer Vision with OpenCV 3 and Qt5. Build visually appealing, multithreaded, cross-platform computer vision applications
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Cacti Beginner's Guide. Leverage Cacti to design a robust network operations center - Second Edition
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Neural Network Programming with Tensorflow. Unleash the power of TensorFlow to train efficient neural networks
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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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Data Science i uczenie maszynowe