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Machine learning
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The concept ** Machine learning** represents the subject, aboutness, idea or notion of resources found in **Missouri University of Science & Technology Library**.

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Machine learning
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**Machine learning**represents the subject, aboutness, idea or notion of resources found in**Missouri University of Science & Technology Library**.- Label
- Machine learning

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- 2015 International Conference on Machine Learning and Cybernetics (ICMLC)
- 3D Shape Analysis : Fundamentals, Theory and Applications
- 3D hand pose estimation via a lightweight deep learning model
- 3D neural network visualization with TensorSpace
- 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
- A Data Science Approach to Extracting Insights About Cities and Zones Using Open Government Data
- A deep learning method for protein model quality assessment
- A learning receiver for communication in three-component multipath channels
- A learning receiver for communication in three-component multipath channels
- A machine-learning decision-support tool for travel-demand modeling
- A primer on machine learning for materials and its relevance to Army challenges
- AI and Machine Learning for Coders
- AI and machine learning
- AI and machine learning
- 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 emerging verticals : human-robot computing, sensing and networking
- AI for finance
- AI for marketing and product innovation : powerful new tools for predicting trends, connecting with customers, and closing sales
- AI for marketing and product innovation : powerful new tools for predicting trends, connecting with customers, and closing sales
- AUTOMATED MACHINE LEARNING : hyperparameter optimization, neural architecture search, and... algorithm selection with cloud platforms
- Academic Press library in signal processing, Volume 1, Signal processing theory and machine learning
- Accelerate deep learning on Raspberry Pi
- Accessibility of big data imagery for next generation machine learning applications
- Achieving real business outcomes from artificial intelligence : enterprise considerations for AI initiatives
- Adaptive information filtering : concepts and algorithms
- Adaptive temporal difference learning of spatial memory in the water maze task
- Advanced Machine Learning with Python
- Advanced NLP projects with TensorFlow 2.0
- Advanced R statistical programming and data models : analysis, machine learning, and visualization
- Advanced applied deep learning : convolutional neural networks and object detection
- Advanced computer vision with TensorFlow
- Advanced data analytics using Python : with machine learning, deep learning and NLP examples
- Advanced deep learning with Keras
- 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 forecasting with Python : with state-of-the-art-models including LSTMs, Facebook's Prophet, and Amazon's DeepAR
- 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 Computing and Data Sciences : 4th International Conference, ICACDS 2020, Valletta, Malta, April 24-25, 2020, Revised Selected Papers
- Advances in Quantitative Ethnography : First International Conference, ICQE 2019, Madison, WI, USA, October 20-22, 2019, Proceedings
- Advances in domain adaptation theory
- Advances in financial machine learning
- Advances in independent component analysis and learning machines
- Advances in machine learning and data mining for astronomy
- 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
- Amazon SageMaker Best Practices : Proven Tips and Tricks to Build Successful Machine Learning Solutions on Amazon SageMaker
- Amazon machine learning
- An inductive logic programming approach to statistical relational learning
- An inductive logic programming approach to statistical relational learning
- An intelligence in our image : the risks of bias and errors in artificial intelligence
- An introduction to machine learning in quantitative finance
- 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
- Analysis and design of machine learning techniques : evolutionary solutions for regression, prediction, and control problems
- Analysis of Images, Social Networks and Texts : 8th International Conference, AIST 2019, Kazan, Russia, July 17-19, 2019, Revised Selected Papers
- Analysis of flame images in gas-fired furnaces
- Analyzing and visualizing data with F#
- Apache Mahout Cookbook
- 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 Machine Learning Blueprints
- 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 learning classifier systems
- Applications of machine learning in wireless communications
- Applied Supervised Learning with R : Use Machine Learning Libraries of R to Build Models That Solve Business Problems and Predict Future Trends
- Applied Technologies : First International Conference, ICAT 2019, Quito, Ecuador, December 3-5, 2019, Proceedings, Part II
- Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques
- Applied data science using Pyspark : learn the end-to-end predictive model-building cycle
- 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 and deep learning with R
- Applied machine learning for health and fitness : a practical guide to machine learning with deep vision, sensors, IoT, and VR
- Applied machine learning for healthcare
- Applied machine learning for spreading financial statements
- Applied machine learning with Python
- Applied machine learning with R
- Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing
- Applied neural networks with TensorFlow 2 : API oriented deep learning with Python
- 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
- Approximation Methods for Efficient Learning of Bayesian Networks
- Approximation methods for efficient learning of Bayesian networks
- Architecting system of systems: artificial life analysis of financial market behavior
- Architecting system of systems: artificial life analysis of financial market behavior
- 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 by Example : Acquire Advanced AI, Machine Learning, and Deep Learning Design Skills.
- Artificial intelligence : a guide for thinking humans
- Artificial intelligence : the simplest way
- 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 basics : a non-technical introduction
- Artificial intelligence design and solution for risk and security
- Artificial intelligence for business optimization : research and applications
- Artificial intelligence in 3 hours
- Artificial intelligence now : current perspectives from O'Reilly Media
- Artificial intelligence on human behavior : new insights into customer segmentation
- Artificial neural networks for engineering applications
- Artificial neural networks with TensorFlow 2 : ANN architecture machine learning projects
- AudioCNN : Audio Event Classification With Deep Learning Based Multi-Channel Fusion Networks
- Automated End-to-End Management of the Deep Learning Lifecycle
- Automated prediction of hepatic arterial stenosis
- Automating DevOps for machine learning
- Autonomous cars : deep learning and computer vision in Python
- 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 cognitive services for developers
- Azure masterclass : manage Azure cloud with ARM templates
- BETO 2021 peer review : inverse bioproduct design through machine learning and molecular simulation
- Basic data analysis with Java
- Bayesian Learning for Neural Networks
- Bayesian artificial intelligence
- Bayesian learning for neural networks
- Bayesian networks and decision graphs
- 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 MLOps with MLFlow : deploy models in AWS SageMaker, Google Cloud, and Microsoft Azure
- Beginning application development with TensorFlow and Keras
- Beginning application development with TensorFlow and Keras : learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications
- Beginning artificial intelligence with the Raspberry Pi
- Beginning data science with Python and Jupyter
- Beginning machine learning in iOS : CoreML framework
- Beginning machine learning in the browser : quick-start guide to gait analysis with JavaScript and TensorFlow.js
- Beginning machine learning with AWS
- Best practices for bringing AI to the enterprise
- Big Data : 7th CCF Conference, BigData 2019, Wuhan, China, September 26-28, 2019, Proceedings
- Big Data and Machine Learning in Quantitative Investment
- Big data analytics for intelligent healthcare management
- Big data analytics using Apache Spark
- Big data and machine learning in quantitative investment
- Bioinformatics : the machine learning approach
- Brain, body and machine : proceedings of an International Symposium on the Occasion of the 25th Anniversary of McGill University Centre for Intelligent Machines
- Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I
- Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II
- Bridging the gap between graph edit distance and kernel machines
- Bringing data to life : combining machine learning and art to tell a data story
- Build more inclusive TensorFlow pipelines with fairness indicators
- Building Machine Learning Systems with Python
- Building Machine Learning Systems with Python : Explore Machine Learning and Deep Learning Techniques for Building Intelligent Systems Using Scikit-Learn and TensorFlow, 3rd Edition
- Building Recommender systems with machine learning and AI
- Building a big data analytics stack
- Building a recommendation engine with Scala : learn to use Scala to build a recommendation engine from scratch and empower your website users
- 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 and deep learning models on Google Cloud Platform : a comprehensive guide for beginners
- Building machine learning pipelines : automating model life cycles with TensorFlow
- Building machine learning powered applications : going from idea to product
- Building machine learning projects with TensorFlow : engaging projects that will teach you how complex data can be exploited to gain the most insight
- Building machine learning systems with Python : explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow
- Building machine learning systems with Python : get more from your data through creating practical machine learning systems with Python
- Building machine learning systems with TensorFlow
- Business data science : combining machine learning and economics to optimize, automate, and accelerate business decisions
- 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
- C# machine learning projects : nine real-world projects to build robust and high-performing machine learning models with C#
- C4.5 : programs for machine learning
- Can data science help us find what makes a hit television show
- Challenges and applications for implementing machine learning in computer vision
- Challenges in machine learning from model building to deployment at scale
- Class Representative Projection for Text-based Zero-Shot Learning
- Classification and learning using genetic algorithms : applications in bioinformatics and web intelligence
- Classification of human postural and gestural movements using center of pressure parameters derived from force platforms
- Clojure for data science : statistics, big data, and machine learning for Clojure programmers
- Cloud computing for machine learning and cognitive applications
- Clustering & classification with machine learning in R : harness the power of machine learning for unsupervised & supervised learning in R
- Clustering : Methodology, hybrid systems, visualization, validation and implementation
- Clustering and unsupervised learning, Part 4, Introduction to real-world machine learning
- Cognitive Radio-Oriented Wireless Networks : 14th EAI International Conference, CrownCom 2019, Poznan, Poland, June 11-12, 2019, Proceedings
- Cognitive carpentry : a blueprint for how to build a person
- Cognitive computing recipes : artificial intelligence solutions using Microsoft cognitive services and Tensorflow
- Cognitive computing with IBM Watson : build smart applications using artificial intelligence as a service
- Cognitive systems
- Combinatorial machine learning : a rough set approach
- Computational Linguistics : 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Hanoi, Vietnam, October 11-13, 2019, Revised Selected Papers
- Computational intelligence in business analytics : concepts, methods, and tools for big data applications
- Computational learning and probabilistic reasoning
- Computational models of learning
- Computational trust models and machine learning
- Computer Supported Cooperative Work and Social Computing : 15th CCF Conference, ChineseCSCW 2020, Shenzhen, China, November 7-9, 2020, Revised Selected Papers
- Computer Vision Applications : Third Workshop, WCVA 2018, Held in Conjunction with ICVGIP 2018, Hyderabad, India, December 18, 2018, Revised Selected Papers
- Computer Vision and Image Processing : 4th International Conference, CVIP 2019, Jaipur, India, September 27-29, 2019, Revised Selected Papers, Part I
- Computer Vision and Image Processing : 4th International Conference, CVIP 2019, Jaipur, India, September 27-29, 2019, Revised Selected Papers, Part II
- Computer Vision and Image Processing : 5th International Conference, CVIP 2020, Prayagraj, India, December 4-6, 2020, Revised Selected Papers, Part II
- Computer Vision, Imaging and Computer Graphics Theory and Applications : 14th International Joint Conference, VISIGRAPP 2019, Prague, Czech Republic, February 25-27, 2019, Revised Selected Papers
- Computer systems that learn : classification and prediction methods from statistics, neural nets, machine learning, and expert systems
- Computer vision projects with OpenCV and Python 3 : six end-to-end projects build using machine learning with OpenCV, Python, and TensorFlow
- Computer vision with maker tech : detecting people with a Raspberry Pi, a thermal camera, and machine learning
- Concept Data Analysis
- Conformal prediction for reliable machine learning : theory, adaptations and applications
- Connectionist models and their implications : readings from cognitive science
- Considering TensorFlow for the enterprise : an overview of the deep learning ecosystem
- Constraining the Major Merging History of Massive Galaxies : A Comprehensive Analysis of Close Pairs and Tidal Features Using Empirical and Simulated Data
- Customizing state-of-the-art deep learning models for new computer vision solutions
- Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health : International 2019 Cyberspace Congress, CyberDI and CyberLife, Beijing, China, December 16-18, 2019, Proceedings, Part II
- DEEP LEARNING GRUNDLAGEN UND IMPLEMENTIERUNG;NEURONALE NETZE MIT PYTHON UND PYTORCH PROGRAMMIEREN
- DL-DI : a deep learning framework for distributed, incremental image classification
- DMLA : a dynamic model-based Lambda Architecture for learning and recognition of features in Big Data
- Data Mining : 16th Australasian Conference, AusDM 2018, Bahrurst, NSW, Australia, November 28-30, 2018, Revised Selected Papers
- Data Mining : 17th Australasian Conference, AusDM 2019, Adelaide, SA, Australia, December 2-5, 2019, Proceedings
- Data Mining and Machine Learning in Building Energy Analysis
- Data Mining and Machine Learning in Cybersecurity
- Data Science Programming All-In-One for Dummies
- 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 fusion by using machine learning and computational intelligence techniques for medical image analysis and classification
- Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification
- Data intelligence and risk analytics : Industrial Management & Data Systems
- Data mining and machine learning in cybersecurity
- Data mining with decision trees : theory and applications
- Data pipelines with Apache Airflow
- Data science algorithms in a week : data analysis, machine learning, and more
- 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 programming all-in-one
- 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 revealed : with feature engineering, data visualization, pipeline development, and hyperparameter tuning
- Data science solutions on Azure : tools and techniques using Databricks, Azure Synapse, and MLOps
- 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-driven security assessment of power grids based on machine learning approach : preprint
- Database and Expert Systems Applications : DEXA 2019 International Workshops BIOKDD, IWCFS, MLKgraphs and TIR, Linz, Austria, August 26-29, 2019, Proceedings
- Dataset shift in machine learning
- 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 and Parallel Computing Environment for Bio-Engineering Systems
- Deep Learning for Beginners
- Deep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
- Deep Learning for the Life Sciences : Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
- 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 Pytorch 1. x : Implement Deep Learning Techniques and Neural Network Architecture Variants Using Python, 2nd Edition
- Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition
- Deep Reinforcement Learning in Action
- Deep learning
- Deep learning : a practitioner's approach
- Deep learning : a visual approach
- 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 neural networks using Python - Keras : the complete beginners guide
- Deep learning and the game of Go
- Deep learning architecture for building artificial neural networks
- Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks
- Deep learning cookbook : practical recipes to get started quickly
- Deep learning crash course
- Deep learning for coders with fastai and PyTorch : AI applications without a PhD
- Deep learning for computer vision with SAS : an introduction
- 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 remote sensing images with open source software
- 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 computer vision : principles and applications
- Deep learning mit R und Keras : Das Praxis-Handbuch : von Entwicklern von Keras und RStudio
- Deep learning neural networks : design and case studies
- Deep learning on Windows : building deep learning computer vision systems on Microsoft Windows
- Deep learning pipeline : building a deep learning model with TensorFlow
- Deep learning projects using TensorFlow 2 : neural network development with Python and Keras
- Deep learning receptury
- Deep learning through sparse and low-rank modeling
- Deep learning through sparse and low-rank modeling
- Deep learning using OpenPose : learn Pose estimation models and build 5 AI apps
- Deep learning with Apache Spark
- Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow
- Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework
- 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
- Deep learning with Python : a hands-on introduction
- Deep learning with Python : learn best practices of deep learning models with PyTorch
- Deep learning with Python video edition
- 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 Swift for TensorFlow : differentiable programming with Swift
- Deep learning with TensorFlow
- 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 applications using Python : chatbots and face, object, and speech recognition with TensorFlow and Keras
- 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
- Deep reinforcement learning hands-on : apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
- DeepSampling : Image Sampling Technique for Cost-Effective Deep Learning
- Demand-driven associative classification
- Deploy machine learning models to production : with Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform
- Deploying AI in the enterprise : IT approaches for design, DevOps, governance, change management, blockchain, and quantum computing
- 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
- Design and analysis of learning classifier systems : a probabilistic approach
- Design and control of intelligent robotic systems
- Design of Multi-modality Deep Fusion Architecture for Deep Acoustic Analytics
- Detection and identification of rare audiovisual cues
- Developing an image classifier using TensorFlow : convolutional neural networks
- Developing and Implementing the Strategy of the Forest Machine Group : Rauma Repola
- Developing the Forest Machine Group : Rauma Repola
- Digital TV and Wireless Multimedia Communication : 16th International Forum, IFTC 2019, Shanghai, China, September 19-20, 2019, Revised Selected Papers
- Digital decision-making : the building blocks of machine learning and artificial intelligence : hearing before the Subcommittee on Communications, Technology, Innovation, and the Internet of the Committee on Commerce, Science, and Transportation, United States Senate, One Hundred Fifteenth Congress, first session, December 12, 2017
- Disaster and infrastructure scene understanding
- Distributed Collaborative Framework for Deep Learning in Object Detection
- Distributed deep learning with Apache Spark
- Dynamic fuzzy machine learning
- Dynamic model generation and semantic search for open source projects using big data analytics
- Dynamic neural network programming with PyTorch
- EDGE INTELLIGENCE IN THE MAKING : optimization, deep learning, and applications
- END-TO-END DATA SCIENCE WITH SAS : a hands-on programming guide;a hands-on programming guide
- Economics of Grids, Clouds, Systems, and Services : 15th International Conference, GECON 2018, Pisa, Italy, September 18-20, 2018, Proceedings
- 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
- Elements of causal inference : foundations and learning algorithms
- Emerging paradigms in machine learning and applications
- Emerging trends in disruptive technology management for sustainable development
- Enhance recommendations in Uber Eats with graph convolutional networks
- Enriching Knowledge Graphs Using Machine Learning Techniques
- Ensemble Machine Learning
- Ensemble machine learning cookbook : over 35 practical recipes to explore ensemble machine learning techniques using Python
- Ensemble machine learning techniques
- Ensembles in machine learning applications
- Ethics and data science
- Evaluating machine learning models : a beginner's guide to key concepts and pitfalls
- Evolvable machines : theory & practice
- Executive briefing : usable machine learning - lessons from Stanford and beyond