Machine learning

Building a simple but powerful recommendation system is much easier than you think.

Building a simple but powerful recommendation system is much easier than you think.

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far.

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far.

How we think and react has a direct impact on experience design, but often designers don't understand the "e;whys"e; behind their best practices, leaving them at risk for misusing or underutilizing those designs.

This book provides a comprehensive explanation of precision (i.

Vertiefendes Wissen von Deep Learning über Computer Vision bis Natural Language Processing- Schließt die Lücke zwischen Grundlagen und Profiwissen- Einfache, prägnante Erklärungen zu wichtigen und aktuellen Themen- Mit Übungsaufgaben sowie Codebeispielen auf GitHub Sie verfügen bereits über Grundkenntnisse zu maschinellem Lernen und künstlicher Intelligenz, haben aber viele Fragen und wollen tiefer in wesentliche und aktuelle Konzepte eintauchen?

Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python.

Get started with artificial intelligence for medical sciences and psychology.

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist.

Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit.

This book will teach you how to build recommender systems with machine learning algorithms using Python.

Get a hands-on introduction to Transformer architecture using the Hugging Face library.

Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python.

Design and develop end-to-end, production-grade computer vision projects for real-world industry problems.

Understand the concepts of image processing with Python 3 and create applications using Raspberry Pi 4.

Welcome to your hands-on guide to artificial intelligence for IT operations (AIOps).

User experience (UX) design practices have seen a fundamental shift as more and more software products incorporate machine learning (ML) components and artificial intelligence (AI) algorithms at their core.

Use artificial intelligence (AI) techniques to build tools for auditing your organization.

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python.

Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning.

Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras.

Delivering AI projects and building an AI organization are two big challenges for enterprises.

Learn to develop and deploy dashboards as web apps using the Python programming language, and how to integrate algorithms into web apps.

Master the new features in PySpark 3.

Apply supervised and unsupervised learning to solve practical and real-world big data problems.

Learn how to harness modern deep-learning methods in many contexts.

Leverage machine learning and deep learning techniques to build fully-fledged natural language processing (NLP) projects.

Take a journey toward discovering, learning, and using Apache Spark 3.

Use TensorFlow 2.

Use this hands-on guide book to learn and explore cognitive APIs developed by Microsoft and provided with the Azure platform.

So far, little effort has been devoted to developing practical approaches on how to develop and deploy AI systems that meet certain standards and principles.

Work with natural language tools and techniques to solve real-world problems.

Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment.

Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning.

Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework.

Build a chatbot using the Microsoft Conversational AI platform.

What is an artificial intelligence (AI)-enabled drone and what can it do?

Learn about Robotic Processing Automation (RPA) and how to build bots using UiPath.

Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.

Use TensorFlow 2.

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite.

Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work.

Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure.

Build and deploy machine learning and deep learning models in production with end-to-end examples.

Get introduced to ML.

This updated second edition offers a guided tour of machine learning algorithms and architecture design.

Discover the capabilities of PySpark and its application in the realm of data science.

Understand how neural networks work and learn how to implement them using TensorFlow 2.

Learn to use adaptive algorithms to solve real-world streaming data problems.