M. - ebooki
Tytuły autora: M. dostępne w księgarni Ebookpoint
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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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Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym
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Sztuczna inteligencja i uczenie maszynowe dla programistów. Praktyczny przewodnik po sztucznej inteligencji
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Matematyka w uczeniu maszynowym
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Deep learning dla programistów. Budowanie aplikacji AI za pomocą fastai i PyTorch
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Power Automate. Kurs video. Automatyzacja procesów biznesowych
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Przetwarzanie języka naturalnego w praktyce. Przewodnik po budowie rzeczywistych systemów NLP
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Potoki danych. Leksykon kieszonkowy. Przenoszenie i przetwarzanie danych na potrzeby ich analizy
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Uczenie głębokie od zera. Podstawy implementacji w Pythonie
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Python. Machine learning i deep learning. Biblioteki scikit-learn i TensorFlow 2. Wydanie III
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Jak sztuczna inteligencja zmieni twoje życie
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Python dla DevOps. Naucz się bezlitośnie skutecznej automatyzacji
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Uczenie maszynowe w Pythonie dla każdego
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Uczenie maszynowe w Pythonie. Leksykon kieszonkowy
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Machine learning, Python i data science. Wprowadzenie
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Zostań Milionerem z ChatGPT. Prosty przewodnik jak osiągnąć sukces w każdej branży za pomocą sztucznej inteligencji
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Głębokie uczenie. Wprowadzenie
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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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Człowiek na rozdrożu. Sztuczna inteligencja 25 punktów widzenia
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Deep Learning. Uczenie głębokie z językiem Python. Sztuczna inteligencja i sieci neuronowe
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TensorFlow. 13 praktycznych projektów wykorzystujących uczenie maszynowe
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Uczenie maszynowe w C#. Szybkie, sprytne i solidne aplikacje
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Uczenie maszynowe w aplikacjach. Projektowanie, budowa i wdrażanie
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Sztuczna inteligencja na froncie. Kurs video. Uczenie maszynowe w JavaScript
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Aplikacje ChatGPT. Wejdź na wyższy poziom z inteligentnymi programami - generatory, boty i wiele innych!
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Wzorce projektowe uczenia maszynowego. Rozwiązania typowych problemów dotyczących przygotowania danych, konstruowania modeli i MLOps
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Naczelny Algorytm. Jak jego odkrycie zmieni nasz świat
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Głębokie uczenie z TensorFlow. Od regresji liniowej po uczenie przez wzmacnianie
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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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Machine Learning with PyTorch and Scikit-Learn. Develop machine learning and deep learning models with Python
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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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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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Democratizing Artificial Intelligence with UiPath. Expand automation in your organization to achieve operational efficiency and high performance
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AI and Machine Learning for On-Device Development
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Machine Learning and Data Science Blueprints for Finance
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Python. Uczenie maszynowe. Wydanie II
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Inteligentna sieć. Algorytmy przyszłości. Wydanie II
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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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Practical Machine Learning for Computer Vision
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Deep Learning. Praktyczne wprowadzenie
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Architecting Data and Machine Learning Platforms
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Praktyczne uczenie maszynowe w języku R
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Uczenie maszynowe dla programistów
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TinyML Cookbook. Combine machine learning with microcontrollers to solve real-world problems - Second Edition
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The Kaggle Book. Data analysis and machine learning for competitive data science
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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 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 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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Python Deep Learning. Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow - Second Edition
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Artificial Intelligence By Example. Develop machine intelligence from scratch using real artificial intelligence use cases
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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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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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Praktyczne uczenie maszynowe
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Python w uczeniu maszynowym
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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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Machine Learning Using TensorFlow Cookbook. Create powerful machine learning algorithms with TensorFlow
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Machine Learning Design Patterns
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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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Natural Language Processing with PyTorch. Build Intelligent Language Applications Using Deep Learning
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Data Science i uczenie maszynowe
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Wnioskowanie przyczynowe w Pythonie. Praktyczne wykorzystanie w branży technologicznej
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Deep Learning at Scale
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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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Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
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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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Active Machine Learning with Python. Refine and elevate data quality over quantity with active learning
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Hands-On Entity Resolution
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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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Machine Learning Infrastructure and Best Practices for Software Engineers. Take your machine learning software from a prototype to a fully fledged software system
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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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Graph-Powered Analytics and Machine Learning with TigerGraph
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Zaufanie do systemów sztucznej inteligencji
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Machine Learning for High-Risk Applications
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Computer Vision on AWS. Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker
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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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Transforming Healthcare with DevOps. A practical DevOps4Care guide to embracing the complexity of digital transformation
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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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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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TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter
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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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Modern Mainframe Development
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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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Intelligent Workloads at the Edge. Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
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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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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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Learn Amazon SageMaker. A guide to building, training, and deploying machine learning models for developers and data scientists - Second Edition
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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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Reliable Machine Learning
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Practical Weak Supervision
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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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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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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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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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Kubeflow Operations Guide
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Introducing MLOps
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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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AI and Machine Learning for Coders
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Deep Learning for Beginners. A beginner's guide to getting up and running with deep learning from scratch using Python