#
Machine learning
Resource Information
The concept ** Machine learning** represents the subject, aboutness, idea or notion of resources found in **Sydney Jones Library, University of Liverpool**.

The Resource
Machine learning
Resource Information

The concept

**Machine learning**represents the subject, aboutness, idea or notion of resources found in**Sydney Jones Library, University of Liverpool**.- Label
- Machine learning

- Authority link
- http://id.loc.gov/authorities/subjects/sh85079324

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Context of Machine learning#### Subject of

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- 3D shape analysis : fundamentals, theory, and applications
- 5 questions on artificial intelligence
- 5 questions on artificial intelligence
- 6 Trends Framing the State of AI and ML
- AI and Machine Learning for Coders
- AI and machine learning for healthcare : an overview of tools and challenges for building a health-tech data pipeline
- AI and the index management problem
- AI as a Service
- AI for finance
- AUTOMATED MACHINE LEARNING : hyperparameter optimization, neural architecture search, and... algorithm selection with cloud platforms
- Achieving real business outcomes from artificial intelligence : enterprise considerations for AI initiatives
- Active lighting and its application for computer vision : 40 years of history of active lighting techniques
- Adaptive Blind Signal & Image Processing
- Advanced R statistical programming and data models : analysis, machine learning, and visualization
- Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
- Advanced deep learning with Python
- Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R
- Advanced machine learning
- Advanced machine learning with Python : solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
- Advanced machine learning with scikit-learn : tools and techniques for predictive analytics in Python
- Advanced statistics and data mining for data science
- Advances in cybernetics, cognition, and machine learning for communication technologies
- Advances in domain adaptation theory
- Advances in financial machine learning
- Advances in photometric 3D-reconstruction
- Adversarial and uncertain reasoning for adaptive cyber defense : control- and game-theoretic approaches to cyber security
- Agile machine learning : effective machine learning inspired by the agile manifesto
- Agile machine learning : effective machine learning inspired by the agile manifesto
- Agricultural informatics : automation using the IoT and machine learning
- Algorithmic recommendations at The New York Times
- Algorithms in machine learning paradigms
- Amazon SageMaker Best Practices : Proven Tips and Tricks to Build Successful Machine Learning Solutions on Amazon SageMaker
- Amazon machine learning
- An introduction to machine learning
- An introduction to machine learning interpretability : an applied perspective on fairness, accountability, transparency, and explainable AI
- An introduction to machine learning models in production : how to transition from one-off models to reproducible pipelines
- Analyzing and visualizing data with F#
- Apache Spark 2 data processing and real-time analytics : master complex big data processing, stream analytics, and machine learning with Apache
- Apache Spark 2.x machine learning cookbook : over 100 recipes to simplify machine learning model implementations with Spark
- Apache Spark deep learning cookbook : over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
- Apache Spark machine learning blueprints : develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide
- Apache Spark quick start guide : quickly learn the art of writing efficient big data applications with Apache Spark
- Applications of embeddings and deep learning at Groupon
- Applications of machine learning
- Applications of machine learning in wireless communications
- Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques
- Applied data science with Python and Jupyter
- Applied deep learning : a case-based approach to understanding neural networks
- Applied deep learning and computer vision for self-driving cars : build autonomous vehicles using deep neural networks and behavior-cloning techniques
- Applied machine learning
- Applied machine learning for spreading financial statements
- Applied text analysis with Python : enabling language-aware data products with machine learning
- Applied unsupervised learning with Python : discover hidden patterns and relationships in unstructured data with Python
- Applied unsupervised learning with R
- Artificial Intelligence Business : How you can profit from AI
- Artificial Intelligence By Example - Second Edition
- Artificial Intelligence and machine learning applications in civil, mechanical, and industrial engineering
- Artificial intelligence : the simplest way
- Artificial intelligence and deep learning in pathology
- Artificial intelligence and machine learning for digital pathology : state-of-the-art and future challenges
- Artificial intelligence and machine learning fundamentals
- Artificial intelligence and machine learning fundamentals
- Artificial intelligence and machine learning in industry : perspectives from leading practitioners
- Artificial intelligence and security : 6th International Conference, ICAIS 2020, Hohhot, China, July 17-20, 2020, Proceedings, Part I
- Artificial intelligence and security : 6th International Conference, ICAIS 2020, Hohhot, China, July 17-20, 2020, Proceedings, Part II
- Artificial intelligence and security : 6th International Conference, ICAIS 2020, Hohhot, China, July 17-20, 2020, Proceedings, Part II
- Artificial intelligence basics : a non-technical introduction
- Artificial intelligence in 3 hours
- Artificial intelligence now : current perspectives from O'Reilly Media
- Artificial intelligence on human behavior : new insights into customer segmentation
- Automated machine learning : methods, systems, challenges
- Automated software engineering : a deep learning based approach
- Automating DevOps for machine learning
- Autonomous learning systems : from data streams to knowledge in real-time
- Avoiding the pitfalls of deep learning : solving model overfitting with regularization and dropout
- Azure masterclass : manage Azure cloud with ARM templates
- BUILDING AN EFFECTIVE DATA SCIENCE PRACTICE : a framework to bootstrap and manage a successful... data science practice
- Becoming a Data Head : How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning
- Beginning AI bot frameworks : getting started with bot development
- Beginning MATLAB and Simulink : from novice to professional
- Beginning data science with Python and Jupyter
- Beginning machine learning with AWS
- Best practices for bringing AI to the enterprise
- Big data analytics for intelligent healthcare management
- Big data analytics using Apache Spark
- Big data and machine learning in quantitative investment
- Bringing data to life : combining machine learning and art to tell a data story
- Broad learning through fusions : an application on social networks
- Build more inclusive TensorFlow pipelines with fairness indicators
- Building Recommender systems with machine learning and AI
- Building a big data analytics stack
- Building a recommendation system with R : learn the art of building robust and powerful recommendation engines using R
- Building advanced OpenCV 3 projects with Python
- Building enterprise data products
- Building intelligent cloud applications : develop scalable models using serverless architectures with Azure
- Building machine learning pipelines : automating model life cycles with TensorFlow
- Building machine learning powered applications : going from idea to product
- Business data science : combining machine learning and economics to optimize, automate, and accelerate business decisions
- Business forecasting : the emerging role of artificial intelligence and machine learning
- Can data science help us find what makes a hit television show
- Cause effect pairs in machine learning
- Centrality and diversity in search : roles in A.I., machine learning, social networks, and pattern recognition
- Challenges and applications for implementing machine learning in computer vision
- Challenges in machine learning from model building to deployment at scale
- Chinese Computational Linguistics : 19th China National Conference, CCL 2020, Hainan, China, October 30 - November 1, 2020, proceedings
- Clojure for data science : statistics, big data, and machine learning for Clojure programmers
- Clustering and unsupervised learning, Part 4, Introduction to real-world machine learning
- Cognitive computing with IBM Watson : build smart applications using artificial intelligence as a service
- Computational intelligence in business analytics : concepts, methods, and tools for big data applications
- Computational intelligence methods for bioinformatics and biostatistics : 16th International Meeting, CIBB 2019, Bergamo, Italy, September 4-6, 2019, revised selected papers
- Computational trust models and machine learning
- Computer Vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part V
- Computer Vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part VIII
- Computer vision - ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020 : proceedings, Part XIV
- Computer vision - ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020 : proceedings, Part XV
- Computer vision - ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020 : proceedings, Part XXIV
- Computer vision - ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020 : proceedings, Part XXVI
- Computer vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II
- Computer vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part III
- Computer vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IV
- Computer vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XVIII
- Computer vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXII
- Computer vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIII
- Computer vision -- ECCV 2020 : 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings. Part I
- Computer vision projects with OpenCV and Python 3 : six end-to-end projects build using machine learning with OpenCV, Python, and TensorFlow
- Considering TensorFlow for the enterprise : an overview of the deep learning ecosystem
- Customizing state-of-the-art deep learning models for new computer vision solutions
- Data Mining and Machine Learning in Cybersecurity
- Data Science Programming All-In-One for Dummies
- Data Science Solutions with Python : Fast and Scalable Models Using Keras, Pyspark MLlib, H2O, XGBoost, and Scikit-Learn
- Data analysis for direct numerical simulations of turbulent combustion : from equation-based analysis to machine learning
- Data analysis with Python : a modern approach
- Data analytics and machine learning fundamentals : LiveLessons
- Data analytics made easy : use machine learning and data storytelling in your work without writing... any code
- Data and social good : using data science to improve lives, fight injustice, and support democracy
- Data science : from research to application
- Data science algorithms in a week : top 7 algorithms for scientific computing, data analysis, and machine learning
- Data science and engineering at enterprise scale : notebook-driven results and analysis
- Data science and machine learning with Python--Hands on!
- Data science fundamentals, Part 1, Learning basic concepts, data wrangling, and databases with Python
- Data science fundamentals, Part 2, Machine learning and statistical analysis
- Data science in the cloud with Microsoft Azure machine learning and Python
- Data science in the cloud with Microsoft Azure machine learning and R : 2015 update
- Data science isn't just another job
- Data science projects with Python : a case study approach to gaining valuable insights from real data with machine learning
- Data science projects with Python : a case study approach to successful data science projects using Python, pandas, and scikcit-learn
- Data science with Microsoft Azure and R
- Data science with Python : combine Python with machine learning principles to discover hidden patterns in raw data
- Data statistics with full stack Python
- Data visualization recipes in Python
- Data, engineering and applications, Volume 2
- Database and expert systems applications : 31st International Conference, DEXA 2020, Bratislava, Czech Republic, September 14-17, 2020, Proceedings, Part II
- Database systems for advanced applications : 25th International Conference, DASFAA 2020, Jeju, South Korea, September 24-27, 2020, Proceedings, Part I
- Dealing with real-world data, Part 1, Introduction to real-world machine learning
- Deep Learning - Grundlagen und Implementierung : Neuronale Netze mit Python und PyTorch programmieren
- Deep Learning : Concepts and Architectures
- Deep Learning Patterns and Practices
- Deep Learning for Beginners
- Deep Learning for Computer Architects
- Deep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
- Deep Learning für die Biowissenschaften : Einsatz von Deep Learning in Genomik, Biophysik, Mikroskopie und medizinischer Analyse
- Deep Learning illustriert
- Deep Learning mit Python und Keras : Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek
- Deep Learning with JavaScript
- Deep Learning with PyTorch
- Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition
- Deep Reinforcement Learning in Action
- Deep biometrics
- Deep in-memory architectures for machine learning
- Deep learners and deep learner descriptors for medical applications
- Deep learning : a practitioner's approach
- Deep learning : a visual approach
- Deep learning : algorithms and applications
- Deep learning : das umfassende Handbuch : Grundlagen, aktuelle Verfahren und Algorithmen, neue Forschungsansätze
- Deep learning : moving toward artificial intelligence with neural networks and machine learning
- Deep learning : practical neural networks with Java : build and run intelligent applications by leveraging key Java machine learning libraries : a course in three modules
- Deep learning Kochbuch : Praxisrezepte für einen schnellen Einstieg
- Deep learning and parallel computing environment for bioengineering
- Deep learning and the game of Go
- Deep learning applications
- Deep learning classifiers with memristive networks : theory and applications
- Deep learning cookbook : practical recipes to get started quickly
- Deep learning crash course
- Deep learning for computer vision with SAS : an introduction
- Deep learning for data analytics : foundations, biomedical applications, and challenges
- Deep learning for dummies
- Deep learning for natural language processing : applications of deep neural networks to machine learning tasks
- Deep learning for natural language processing : creating neural networks with Python
- Deep learning for numerical applications with SAS
- Deep learning for recommender systems, or How to compare pears with apples
- Deep learning for search
- Deep learning for strategic decision makers : understanding deep learning and how it produces business value
- Deep learning for time series data
- Deep learning from scratch : building with Python from first principles
- Deep learning illustrated : a visual, interactive guide to artificial intelligence
- Deep learning in healthcare : paradigms and applications
- Deep learning mit R und Keras : Das Praxis-Handbuch : von Entwicklern von Keras und RStudio
- Deep learning pipeline : building a deep learning model with TensorFlow
- Deep learning receptury
- Deep learning techniques for biomedical and health informatics
- Deep learning techniques for biomedical and health informatics
- Deep learning techniques for music generation
- Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow
- Deep learning with PyTorch
- Deep learning with PyTorch : a practical approach to building neural network models using PyTorch
- Deep learning with PyTorch quick start guide : learn to train and deploy neural network models in Python
- Deep learning with Python
- Deep learning with Python video edition
- Deep learning with R
- Deep learning with R
- Deep learning with R cookbook : over 45 unique recipes to delve into neural network techniques using R 3.5x
- Deep learning with R for beginners : design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
- Deep learning with R in motion
- Deep learning with TensorFlow
- Deep learning with TensorFlow 2 and Keras : regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API
- Deep learning with TensorFlow : take your machine learning knowledge to the next level with the power of TensorFlow
- Deep learning with fastai cookbook : leverage the easy-to-use fastai framework to unlock the power of deep learning
- Deep neural evolution : deep learning with evolutionary computation
- Deep reinforcement learning and GANS Livelessons
- Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
- Demystifying big data, machine learning, and deep learning for healthcare analytics
- Density Ratio Estimation in Machine Learning
- Deploying Spark ML pipelines in production on AWS : how to publish pipeline artifacts and run pipelines in production
- Deploying machine learning models as microservices using Docker : a REST-based architecture for serving ML model outputs at scale
- Developing an image classifier using TensorFlow : convolutional neural networks
- Development and analysis of deep learning architectures
- Dictionary Learning in Visual Computing
- Domain Adaptation Theory : Available Theoretical Results
- Dual Learning
- END-TO-END DATA SCIENCE WITH SAS : a hands-on programming guide;a hands-on programming guide
- Effective Amazon machine learning : machine learning in the Cloud
- Effective enterprise architecture
- Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
- Einführung in Machine Learning mit Python : Praxiswissen Data Science
- Einführung in TensorFlow : Deep-Learning-Systeme programmieren, trainieren, skalieren und deployen
- Embracing Industry 4.0 : Selected Articles from MUCET 2019
- Enhance recommendations in Uber Eats with graph convolutional networks
- Ensemble machine learning cookbook : over 35 practical recipes to explore ensemble machine learning techniques using Python
- Ethics and data science
- Evaluating machine learning models : a beginner's guide to key concepts and pitfalls
- Executive briefing : usable machine learning - lessons from Stanford and beyond
- Executive briefing : why machine-learned models crash and burn in production and what to do about it
- Feature engineering for machine learning : principles and techniques for data scientists
- Feature engineering made easy : identify unique features from your dataset in order to build powerful machine learning systems
- Feature learning and understanding : algorithms and applications
- Federated learning : privacy and incentive
- First-order and stochastic optimization methods for machine learning
- Foundations of Inductive Logic Programming
- Foundations of deep reinforcement learning : theory and practice in Python
- Fraud detection without feature engineering
- Fundamentals of deep learning : designing next-generation machine intelligence algorithms
- Fundamentals of pattern recognition and machine learning
- GANs mit PyTorch selbst programmieren
- Game Theory for Data Science: Eliciting Truthful Information
- Game engines and machine learning
- Game theory and machine learning for cyber security
- Generative adversarial networks cookbook : over 100 recipes to build generative models using Python, TensorFlow, and Keras
- Generative adversarial networks for image-to-image translation
- Generative adversarial networks projects : build next-generation generative models using TensorFlow and Keras
- Generative deep learning : teaching machines to paint, write, compose, and play
- Generatives Deep Learning : Maschinen das Malen, Schreiben und Komponieren beibringen
- Genetic algorithms and machine learning for programmers : create AI models and evolve solutions
- Getting involved in the TensorFlow community
- Getting started with SAS Enterprise Miner for machine learning : learning to perform segmentation and predictive modeling
- Getting started with TensorFlow
- Getting started with artificial intelligence : a practical guide to building enterprise applications
- Getting started with deep learning
- Getting started with machine learning in Python
- Getting started with machine learning in R
- Getting started with machine learning in the cloud : using cloud-based platforms to discover new business insights
- Go machine learning projects : eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go
- Google BigQuery : the definitive guide : data warehousing, analytics, and machine learning at scale
- Graph-Powered Machine Learning
- Grokking Machine Learning
- Grokking deep learning
- Grokking deep learning in motion
- Guide to Deep Learning Basics : Logical, Historical and Philosophical Perspectives
- HANDS-ON DEEP LEARNING ALGORITHMS WITH PYTHON : master deep learning algorithms with math by... implementing them from scratch
- Handbook of research on emerging trends and applications of machine learning
- Hands-On Artificial Intelligence for Banking
- Hands-On Neural Networks with TensorFlow 2.0
- Hands-on Java deep learning for computer vision : implement machine learning and neural network methodologies to perform computer vision-related tasks
- Hands-on Markov models with Python : implement probabilistic models for learning complex data sequences using Python ecosystem
- Hands-on Q-learning with Python : practical Q-learning with OpenAI Gym, Keras, and TensorFlow
- Hands-on Scikit-Learn for machine learning applications : data science fundamentals with Python
- Hands-on Scikit-Learn for machine learning applications : data science fundamentals with Python
- Hands-on TensorFlow Lite for intelligent mobile apps
- Hands-on artificial intelligence for IoT : expert machine learning and deep learning techniques for developing smarter IoT systems
- Hands-on artificial intelligence for beginners : an introduction to AI concepts, algorithms, and their implementation
- Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies
- Hands-on artificial intelligence for search : building intelligent applications and perform enterprise searches
- Hands-on artificial intelligence on Amazon Web Services : decrease the time to market for AI and ML applications with the power of AWS
- Hands-on automated machine learning : a beginner's guide to building automated machine learning systems using AutoML and Python
- Hands-on convolutional neural networks with TensorFlow : solve computer vision problems with modeling in TensorFlow and Python
- Hands-on data analysis with Pandas : efficiently perform data collection, wrangling, analysis, and visualization using Python
- Hands-on data science and Python machine learning : perform data mining and machine learning efficiently using Python and Spark
- Hands-on data science with Anaconda : utilize the right mix of tools to create high-performance data science applications
- Hands-on deep learning for images with TensorFlow : build intelligent computer vision applications using TensorFlow and Keras
- Hands-on deep learning with Apache Spark : build and deploy distributed deep learning applications on Apache Spark
- Hands-on deep learning with Caffe2
- Hands-on deep learning with TensorFlow : uncover what is underneath your data!
- Hands-on ensemble learning with Python : build highly optimized ensemble machine learning models using scikit-learn and Keras
- Hands-on ensemble learning with R : a beginner's guide to combining the power of machine learning algorithms using ensemble techniques
- Hands-on generative adversarial networks with Keras : your guide to implementing next-generation generative adversarial networks
- Hands-on image processing with Python : expert techniques for advanced image analysis and effective interpretation of image data
- Hands-on intelligent agents with OpenAI Gym : a step-by-step guide to develop AI agents using deep reinforcement learning
- Hands-on machine learning for cybersecurity : safeguard your system by making your machines intelligent using the Python ecosystem
- Hands-on machine learning for data mining
- Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine
- Hands-on machine learning using JavaScript
- Hands-on machine learning with Azure : build powerful models with cognitive machine learning and artificial intelligence
- Hands-on machine learning with IBM Watson : leverage IBM Watson to implement machine learning techniques and algorithms using Python
- Hands-on machine learning with JavaScript : solve complex computational web problems using machine learning
- Hands-on machine learning with Microsoft Excel 2019 : build complete data analysis flows, from data collection to visualization
- Hands-on machine learning with Scala and Spark
- Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems
- Hands-on machine learning with TensorFlow.js : a guide to building ML applications integrated with web technology using the TensorFlow.js library
- Hands-on meta learning with Python : meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow
- Hands-on music generation with Magenta : explore the role of deep learning in music generation and assisted music composition
- Hands-on natural language processing with PyTorch
- Hands-on neural networks with Keras : design and create neural networks using deep learning and artificial intelligence principles
- Hands-on reinforcement learning for games : implementing self-learning agents in games using artificial intelligence techniques
- Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras
- Hands-on unsupervised learning using Python : how to discover hidden patterns in unlabeled data
- Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
- Hardcore data science : California 2015
- Hardcore data science : NYC 2014
- Healthcare analytics made simple : techniques in healthcare computing using machine learning and Python
- Hello, TensorFlow! : building and training your first TensorFlow graph from the ground up
- How Criteo optimized and sped up its TensorFlow models by 10x and served them under 5 ms
- How automated machine learning empowers businesses
- How to build privacy and security into deep learning models
- Human centric visual analysis with deep learning
- Human recognition in unconstrained environments : using computer vision, pattern recognition and machine learning methods for biometrics
- Hybrid machine intelligence for medical image analysis
- IBM PowerAI : deep learning unleashed on IBM Power Systems Servers
- IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance
- Image analysis and text classification using CNNs in PyTorch : learn to build powerful image and document classifiers in minutes
- Implementations and applications of machine learning
- Implementing serverless microservices architecture patterns
- Industrial machine learning : using artificial intelligence as a transformational disruptor
- Inpainting and denoising challenges
- Intelligent Computing Theories and Application : 16th International Conference, ICIC 2020, Bari, Italy, October 2-5, 2020, Proceedings, Part I
- Intelligent Workloads at the Edge : Deliver Cyber-Physical Outcomes with Data and Machine Learning Using AWS IoT Greengrass
- Intelligent computing for interactive system design : statistics, digital signal processing, and machine learning in practice
- Intelligent data-analytics for condition monitoring : smart grid applications
- Intelligent feature selection for machine learning using the dynamic wavelet fingerprint
- Intelligent mobile projects with TensorFlow : build 10+ artificial intelligence apps using TensorFlow Mobile and Lite for iOS, Android, and Raspberry Pi
- Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras
- Internet of Things, smart computing and technology : a roadmap ahead
- Interpretable and resilient AI for financial services
- Introducing Machine Learning
- Introducing data science : big data, machine learning, and more, using Python tools
- Introduction : mining the tar sands of big data
- Introduction to Amazon machine learning : learn how to build data driven predictive applications with Amazon Web Services (AWS)
- Introduction to Apache Spark 2.0 : a primer on Spark 2.0 fundamentals and architecture
- Introduction to GPUs for data analytics : advances and applications for accelerated computing
- Introduction to Pandas for developers : understand the basic workflows and gotchas of crawling, munging and plotting data
- Introduction to TensorFlow-Slim : complex TensorFlow model building and training made easy
- Introduction to algorithms for data mining and machine learning
- Introduction to cognitive computing with IBM Watson Services : break free from the myths surrounding IBM Watson to learn what it really can and can't do
- Introduction to computer vision with TensorFlow : using convolutional neural networks and TensorFlow to solve computer vision tasks
- Introduction to deep learning : concepts and fundamentals
- Introduction to deep learning and neural networks with Python
- Introduction to deep learning business applications for developers : from conversational bots in customer service to medical image processing
- Introduction to deep learning models with TensorFlow : learn how to work with TensorFlow to create and run a TensorFlow graph, and build a deep learning model
- Introduction to deep learning using PyTorch : create simple neural networks in Python using PyTorch
- Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R
- Introduction to machine learning with Python : a guide for data scientists
- IoT machine learning applications in telecom, energy, and agriculture : with Raspberry Pi and Arduino using Python
- Java : data science made easy : data collection, processing, analysis, and more : a course in two modules
- Java data science cookbook : explore the power of MLlib, DL4j, Weka and more
- Java deep learning cookbook : train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j
- Java deep learning projects : implement 10 real-world deep learning applications using Deeplearning4j and open source APIs
- Java for data science : examine the techniques and Java tools supporting the growing field of data science
- Keras 2.x projects : 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
- Keras deep learning cookbook : over 30 recipes for implementing deep neural networks in Python
- Keras in motion
- Keras reinforcement learning projects : 9 projects exploring popular reinforcement learning techniques to build self-learning agents
- Keras to Kubernetes : the journey of a machine learning model to production
- Kernel methods for pattern analysis
- Knowledge acquisition and machine learning: theory, methods and applications
- Knowledge discovery from data streams
- Language Models in Plain English
- Large scale machine learning with python : learn to build powerful machine learning models quickly and deploy large-scale predictive applications
- Learn Amazon SageMaker : a guide to building, training, and deploying machine learning models for developers and data scientists
- Learn R for Applied Statistics : With Data Visualizations, Regressions, and Statistics
- Learn R programming
- Learn TensorFlow 2.0 : implement machine learning and deep learning models with Python
- Learn Unity ML-Agents : fundamentals of Unity machine learning : incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games
- Learn data mining through Excel : a step-by-step approach for understanding machine learning methods
- Learn from the experts : artificial intelligence
- Learning Apache Mahout : acquire practical skills in Big Data Analytics and explore data science with Apache Mahout
- Learning Apache Spark 2 : process big data with the speed of light!
- Learning Automata and Stochastic Optimization
- Learning Bayesian models with R : become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems
- Learning Classifier Systems : From Foundations to Applications
- Learning Python data analysis
- Learning Python for data science
- Learning Salesforce Einstein : artificial intelligence and deep learning for your Salesforce CRM
- Learning Spark : lightening fast data analysis
- Learning TensorFlow : a guide to building deep learning systems
- Learning TensorFlow. js : machine learning in JavaScript
- Learning from multiagent emergent behaviors in a simulated environment
- Learning quantitative finance with R : implement machine learning, time-series analysis, algorithmic trading and more
- Leveraging data science in asset management
- Leveraging entity-resolution to identify customers in 3rd party data
- Leveraging multi-CDN at Riot Games
- Lifelong Machine Learning
- Linear algebra for data science in Python
- Linear regression, Part 2, Introduction to real-world machine learning
- Live-coding a machine learning model from scratch
- MACHINE LEARNING SOLUTIONS ARCHITECT HANDBOOK : create machine learning platforms to run... solutions in an enterprise setting
- MACHINE LEARNING USING TENSORFLOW COOKBOOK : over 60 recipes on machine learning using deep ... learning solutions from kaggle masters and google
- MATLAB for machine learning : functions, algorithms, and use cases
- MATLAB machine learning
- ML at Twitter : a deep dive into Twitter's timeline
- MLOps - Kernkonzepte im Überblick : Machine-Learning-Prozesse im Unternehmen nachhaltig automatisieren und skalieren
- Machine Learning : an algorithmic perspective
- Machine Learning Approach for Cloud Data Analytics in IoT
- Machine Learning Engineering with Python : Manage the Production Life Cycle of Machine Learning Models Using MLOps with Practical Examples
- Machine Learning Forensics for Law Enforcement, Security, and Intelligence
- Machine Learning Kochbuch : Praktische Lösungen mit Python: von der Vorverarbeitung der Daten bis zum Deep Learning
- Machine Learning Pocket Reference
- Machine Learning Workshop - Second Edition
- Machine Learning and AI for Healthcare : Big Data for Improved Health Outcomes
- Machine Learning and Its Applications : Advanced Lectures
- Machine Learning for Algorithmic Trading - Second Edition
- Machine Learning for Authorship Attribution and Cyber Forensics
- Machine Learning for Business
- Machine Learning for Cyber Security : third international conference, ML4CS 2020, Guangzhou, China, October 8-10, 2020 : proceedings, Part III
- Machine Learning for OpenCV 4 : Intelligent Algorithms for Building Image Processing Apps Using OpenCV 4, Python, and Scikit-Learn, 2nd Edition
- Machine Learning for Time-Series with Python : Forecast, Predict, and Detect Anomalies with State-Of-the-art Machine Learning Methods
- Machine Learning für Softwareentwickler
- Machine Learning in the AWS Cloud : Add Intelligence to Applications with AWS SageMaker and AWS Rekognition
- Machine Learning kompakt : Alles, was Sie wissen müssen
- Machine Learning mit Python : das Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning
- Machine Learning mit Python und Scikit-learn und TensorFlow : das umfassende Praxis-Handbuch für Data Science, Deep Learning und Predictive Analytics
- Machine Learning with Microsoft technologies : selecting the right architecture and tools for your project
- Machine Learning with R, the tidyverse, and mlr
- Machine Learning with Spark and Python, 2nd Edition
- Machine Learning with the Raspberry Pi : Experiments with Data and Computer Vision
- Machine Learning--die Referenz : mit strukturierten Daten in Python arbeiten
- Machine learning
- Machine learning 101 with Scikit-Learn and StatsModels
- Machine learning : a Bayesian and optimization perspective
- Machine learning : algorithms and applications
- Machine learning : kurz & gut
- Machine learning : les fondamentaux
- Machine learning : methods and applications to brain disorders
- Machine learning : theory and applications
- Machine learning algorithms : reference guide for popular algorithms for data science and machine learning
- Machine learning algorithms in 7 days
- Machine learning and AI for healthcare : big data for improved health outcomes
- Machine learning and artificial intelligence
- Machine learning and big data : concepts, algorithms, tools and applications
- Machine learning and big data with kdb+/q
- Machine learning and cognitive computing for mobile communications and wireless networks
- Machine learning and data mining in aerospace technology
- Machine learning and data monetization
- Machine learning and data science in the oil and gas industry : best practices, tools, and case studies
- Machine learning and data science in the power generation industry
- Machine learning and data science with Python : a complete beginners guide
- Machine learning and internet of medical things in healthcare
- Machine learning and security : protecting systems with data and algorithms
- Machine learning applications using Python : cases studies from healthcare, retail, and finance
- Machine learning approaches for convergence of IoT and Blockchain
- Machine learning approaches in cyber security analytics
- Machine learning approaches to non-intrusive load monitoring
- Machine learning at enterprise scale : how real practitioners handle six common challenges
- Machine learning avec R
- Machine learning engineering with MLflow : manage the end-to-end machine learning lifecycle with MLflow
- Machine learning for absolute beginners
- Machine learning for contextual targeting
- Machine learning for designers
- Machine learning for designers : an introduction to the core technologies of machine learning and the emerging opportunities for ML-enhanced design
- Machine learning for email
- Machine learning for finance : principles and practice for financial insiders
- Machine learning for financial risk management with Python : algorithms for modeling risk
- Machine learning for future wireless communications
- Machine learning for healthcare analytics projects : build smart AI applications using neural network methodologies across the healthcare vertical market
- Machine learning for intelligent decision science
- Machine learning for ios developers
- Machine learning for mobile : practical guide to building intelligent mobile applications powered by machine learning
- Machine learning for the web : explore the web and make smarter predictions using Python
- Machine learning for time series forecasting with Python
- Machine learning fundamentals
- Machine learning fundamentals
- Machine learning fundamentals with Amazon SageMaker on AWS : LiveLessons
- Machine learning guide for oil and gas using Python : a step-by-step breakdown with data, algorithms, codes, and applications
- Machine learning in R : automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks
- Machine learning in action
- Machine learning in aquaculture : hunger classification of Lates Calcarifer
- Machine learning in finance : from theory to practice
- Machine learning in medicine -- a complete overview
- Machine learning in production : developing and optimizing data science workflows and applications
- Machine learning in team sports : performance analysis and talent identification in beach soccer and sepak-takraw
- Machine learning in the cloud with Azure machine learning