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.
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- Machine learning
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- http://id.loc.gov/authorities/subjects/sh85079324
762 Items that share the Concept Machine learning
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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
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- Machine learning algorithms : reference guide for popular algorithms for data science and machine learning
- Machine learning algorithms in 7 days
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- 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