Uczenie maszynowe - ebooki
Ebooki z kategorii: Uczenie maszynowe dostępne w księgarni Helion
-
Before Machine Learning Volume 1 - Linear Algebra for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence
-
Databricks ML in Action. Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment
-
Accelerate Model Training with PyTorch 2.X. Build more accurate models by boosting the model training process
-
Active Machine Learning with Python. Refine and elevate data quality over quantity with active learning
-
Machine Learning: Make Your Own Recommender System. Build Your Recommender System with Machine Learning Insights
-
Machine Learning with Python. Unlocking AI Potential with Python and Machine Learning
-
Hands-On Entity Resolution
-
Data Labeling in Machine Learning with Python. Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models
-
Machine Learning Infrastructure and Best Practices for Software Engineers. Take your machine learning software from a prototype to a fully fledged software system
-
MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations
-
MATLAB for Machine Learning. Unlock the power of deep learning for swift and enhanced results - Second Edition
-
Deep Learning for Finance
-
Deep Learning with MXNet Cookbook. Discover an extensive collection of recipes for creating and implementing AI models on MXNet
-
Machine Learning Interviews
-
Training Data for Machine Learning
-
Machine Learning with Qlik Sense. Utilize different machine learning models in practical use cases by leveraging Qlik Sense
-
The Statistics and Machine Learning with R Workshop. Unlock the power of efficient data science modeling with this hands-on guide
-
Delta Lake: Up and Running
-
Machine Learning with LightGBM and Python. A practitioner's guide to developing production-ready machine learning systems
-
Debugging Machine Learning Models with Python. Develop high-performance, low-bias, and explainable machine learning and deep learning models
-
Machine Learning Engineering with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition
-
Graph-Powered Analytics and Machine Learning with TigerGraph
-
Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data
-
Machine Learning for High-Risk Applications
-
Computer Vision on AWS. Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker
-
Scaling Machine Learning with Spark
-
Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems
-
The Kaggle Workbook. Self-learning exercises and valuable insights for Kaggle data science competitions
-
Democratizing Application Development with Betty Blocks. Build powerful applications that impact business immediately with no-code app development
-
Practicing Trustworthy Machine Learning
-
Transforming Healthcare with DevOps. A practical DevOps4Care guide to embracing the complexity of digital transformation
-
Applied Machine Learning and AI for Engineers
-
Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage
-
Deep Learning with TensorFlow and Keras. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition
-
Hands-On Healthcare Data
-
Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems
-
Natural Language Processing with TensorFlow. The definitive NLP book to implement the most sought-after machine learning models and tasks - Second Edition
-
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
-
Tidy Modeling with R
-
Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes
-
Designing Autonomous AI
-
Practical Simulations for Machine Learning
-
Building Data Science Solutions with Anaconda. A comprehensive starter guide to building robust and complete models
-
Fundamentals of Deep Learning. 2nd Edition
-
Mastering Azure Machine Learning. Execute large-scale end-to-end machine learning with Azure - Second Edition
-
Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems
-
Democratizing Artificial Intelligence with UiPath. Expand automation in your organization to achieve operational efficiency and high performance
-
Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python
-
Natural Language Processing with Flair. A practical guide to understanding and solving NLP problems with Flair
-
Essential Mathematics for Quantum Computing. A beginner's guide to just the math you need without needless complexities
-
Automated Machine Learning on AWS. Fast-track the development of your production-ready machine learning applications the AWS way
-
TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter
-
Getting Started with Amazon SageMaker Studio. Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
-
Unity Artificial Intelligence Programming. Add powerful, believable, and fun AI entities in your game with the power of Unity - Fifth Edition
-
Reproducible Data Science with Pachyderm. Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0
-
Modern Mainframe Development
-
Time Series Analysis on AWS. Learn how to build forecasting models and detect anomalies in your time series data
-
Machine Learning in Biotechnology and Life Sciences. Build machine learning models using Python and deploy them on the cloud
-
Intelligent Workloads at the Edge. Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
-
Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value
-
The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets
-
Machine Learning for Financial Risk Management with Python
-
Azure Data Scientist Associate Certification Guide. A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam
-
Learn Amazon SageMaker. A guide to building, training, and deploying machine learning models for developers and data scientists - Second Edition
-
IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
-
Machine Learning Engineering with Python. Manage the production life cycle of machine learning models using MLOps with practical examples
-
Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
-
Machine Learning for Time-Series with Python. Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
-
Reliable Machine Learning
-
Conversational AI with Rasa. Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots
-
Practical Weak Supervision
-
Exploring GPT-3. An unofficial first look at the general-purpose language processing API from OpenAI
-
Getting Started with Streamlit for Data Science. Create and deploy Streamlit web applications from scratch in Python
-
AI and Machine Learning for On-Device Development
-
Practical Machine Learning for Computer Vision
-
Graph Machine Learning. Take graph data to the next level by applying machine learning techniques and algorithms
-
Machine Learning with BigQuery ML. Create, execute, and improve machine learning models in BigQuery using standard SQL queries
-
Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition
-
Automated Machine Learning with AutoKeras. Deep learning made accessible for everyone with just few lines of coding
-
Machine Learning Automation with TPOT. Build, validate, and deploy fully automated machine learning models with Python
-
Automated Machine Learning with Microsoft Azure. Build highly accurate and scalable end-to-end AI solutions with Azure AutoML
-
Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale
-
Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples
-
AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide. The definitive guide to passing the MLS-C01 exam on the very first attempt
-
Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
-
Odsłaniamy SQL Server 2019: Klastry Big Data i uczenie maszynowe
-
Kubeflow Operations Guide
-
Practical Fairness
-
Introducing MLOps
-
Codeless Deep Learning with KNIME. Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform
-
Python Machine Learning By Example. Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn - Third Edition
-
Microsoft Power BI Quick Start Guide. Bring your data to life through data modeling, visualization, digital storytelling, and more - Second Edition
-
Artificial Intelligence in Finance
-
Kubeflow for Machine Learning
-
Machine Learning and Data Science Blueprints for Finance
-
AI and Machine Learning for Coders
-
Deep Learning for Beginners. A beginner's guide to getting up and running with deep learning from scratch using Python
-
The Natural Language Processing Workshop. Confidently design and build your own NLP projects with this easy-to-understand practical guide
-
Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques
-
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits. A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
-
The Deep Learning with PyTorch Workshop. Build deep neural networks and artificial intelligence applications with PyTorch
-
Hands-On Simulation Modeling with Python. Develop simulation models to get accurate results and enhance decision-making processes
-
Building Machine Learning Pipelines
-
Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks
-
Hands-On Machine Learning with C++. Build, train, and deploy end-to-end machine learning and deep learning pipelines
-
Hands-On Python Deep Learning for the Web. Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow
-
Mastering Azure Machine Learning. Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning
-
Hands-On Deep Learning with R. A practical guide to designing, building, and improving neural network models using R
-
Hands-On One-shot Learning with Python. Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch
-
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
-
Przetwarzanie i analiza obrazów w systemach przemysłowych. Wybrane zastosowania
-
Jak myślą inteligentne maszyny
-
Automatyczna analiza składnikowa języka polskiego
-
Tłumaczenie wspomagane komputerowo
-
Deep Learning with R Cookbook. Over 45 unique recipes to delve into neural network techniques using R 3.5.x
-
Hands-On Music Generation with Magenta. Explore the role of deep learning in music generation and assisted music composition
-
Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition
-
Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models
-
Building Machine Learning Powered Applications. Going from Idea to Product
-
Deep Learning with TensorFlow 2 and Keras. Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API - Second Edition