Emmanuel Ameisen - książki
Tytuły autora: dostępne w księgarni Ebookpoint
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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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Uczenie maszynowe w języku R. Tworzenie i doskonalenie modeli - od przygotowania danych po dostrajanie, ewaluację i pracę z big data. Wydanie IV
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Python Machine Learning By Example. Unlock machine learning best practices with real-world use cases - Fourth Edition
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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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Before Machine Learning Volume 1 - Linear Algebra for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence
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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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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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Uczenie maszynowe w Pythonie. Receptury. Od przygotowania danych do deep learningu. Wydanie II
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Deep Learning for Time Series Cookbook. Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
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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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Uczenie maszynowe: Scikit-Learn, Keras i TensorFlow. Szczegółowy poradnik
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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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Effective Machine Learning Teams
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AI bez tajemnic. Sztuczna Inteligencja od podstaw po zaawansowane techniki
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Hands-On Entity Resolution
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Bayesian Analysis with Python. A practical guide to probabilistic modeling - Third Edition
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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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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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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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Delta Lake: Up and Running
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Architecting Data and Machine Learning Platforms
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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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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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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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Uczenie maszynowe z użyciem Scikit-Learn, Keras i TensorFlow. Wydanie III
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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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Computer Vision on AWS. Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker
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Jak sztuczna inteligencja zmieni twoje życie
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Scaling Machine Learning with Spark
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The Kaggle Workbook. Self-learning exercises and valuable insights for Kaggle data science competitions
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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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Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 1
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Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 2
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Transforming Healthcare with DevOps. A practical DevOps4Care guide to embracing the complexity of digital transformation
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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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Deep Learning. Praktyczne wprowadzenie z zastosowaniem środowiska Pythona
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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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Inżynieria danych na platformie AWS. Jak tworzyć kompletne potoki uczenia maszynowego
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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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Tidy Modeling with R
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Sztuczna inteligencja w finansach. Używaj języka Python do projektowania i wdrażania algorytmów AI
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Deep learning z TensorFlow 2 i Keras dla zaawansowanych. Sieci GAN i VAE, deep RL, uczenie nienadzorowane, wykrywanie i segmentacja obiektów i nie tylko. Wydanie II
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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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Projektowanie głosowych interfejsów użytkownika. Zasady doświadczeń konwersacyjnych
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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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Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python
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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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Essential Mathematics for Quantum Computing. A beginner's guide to just the math you need without needless complexities
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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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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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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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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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Machine Learning with PyTorch and Scikit-Learn. Develop machine learning and deep learning models with Python
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TinyML. Wykorzystanie TensorFlow Lite do uczenia maszynowego na Arduino i innych mikrokontrolerach
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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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Intelligent Workloads at the Edge. Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
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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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The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets
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Machine Learning for Financial Risk Management with Python
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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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Uczenie głębokie i sztuczna inteligencja. Interaktywny przewodnik ilustrowany
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Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
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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 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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Machine Learning Engineering with MLflow. Manage the end-to-end machine learning life cycle with MLflow
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Getting Started with Streamlit for Data Science. Create and deploy Streamlit web applications from scratch in Python
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AI and Machine Learning for On-Device Development
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Sztuczna inteligencja i uczenie maszynowe dla programistów. Praktyczny przewodnik po sztucznej inteligencji
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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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Graph Machine Learning. Take graph data to the next level by applying machine learning techniques and algorithms
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Deep learning dla programistów. Budowanie aplikacji AI za pomocą fastai i PyTorch
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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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Machine learning, Python i data science. Wprowadzenie
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Automated Machine Learning with AutoKeras. Deep learning made accessible for everyone with just few lines of coding
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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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Algorytmy sztucznej inteligencji. Ilustrowany przewodnik
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Umiejętności analityczne w pracy z danymi i sztuczną inteligencją. Wykorzystywanie najnowszych technologii w rozwijaniu przedsiębiorstwa
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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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Przetwarzanie języka naturalnego w akcji
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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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Uczenie maszynowe w aplikacjach. Projektowanie, budowa i wdrażanie
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Applied Deep Learning on Graphs. Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures