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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 **Merrimack Valley Library Consortium**.

The Resource
Machine learning
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The concept

**Machine learning**represents the subject, aboutness, idea or notion of resources found in**Merrimack Valley Library Consortium**.- Label
- Machine learning

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- 3D neural network visualization with TensorSpace
- 5 questions on artificial intelligence
- 5 questions on artificial intelligence
- A/B testing for data science
- AI and machine learning for healthcare : an overview of tools and challenges for building a health-tech data pipeline
- AI for data science : artificial intelligence frameworks and functionality for deep learning, optimization, and beyond
- AI for finance
- AI optimization methods for data science
- AWS SageMaker, machine learning and AI with Python
- Accelerate deep learning on Raspberry Pi
- Achieving real business outcomes from artificial intelligence : enterprise considerations for AI initiatives
- Adaptive learning methods for nonlinear system modeling
- 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 projects
- 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 : design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
- Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R
- Advanced machine learning with Python : solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
- Advanced machine learning with R
- Advanced machine learning with Spark 2.x
- Advanced machine learning with scikit-learn : tools and techniques for predictive analytics in Python
- Advanced neural networks with TensorFlow
- Advanced practical reinforcement learning : agents and environments
- Advanced predictive techniques with Scikit-Learn and TensorFlow
- Advanced statistics and data mining for data science
- Advanced statistics for machine learning
- Advanced tools and techniques beyond base R
- Advances in financial machine learning
- Agile machine learning : effective machine learning inspired by the agile manifesto
- Amazon machine learning
- An introduction into machine learning C++ libraries
- 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
- Analytics series : Google Analytics (GA) made simple
- Analytics with Pandas complete guide
- 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 beyond the basics
- Apache Spark deep learning advanced recipes
- Apache Spark deep learning cookbook : over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
- Apache Spark deep learning recipes
- Apache Spark in 7 days
- 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
- 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 with TensorFlow and Google Cloud AI
- Applied machine learning and deep learning with R
- Applied machine learning for healthcare
- Applied machine learning in finance
- Applied machine learning in finance
- Applied machine learning with R
- Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing
- 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 : the simplest way
- Artificial intelligence and data science series : natural language processing test your knowledge
- Artificial intelligence and machine learning (recorded live at Data Modeling Zone US)
- 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 in 3 hours
- Artificial intelligence now : current perspectives from O'Reilly Media
- 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 cognitive services for developers
- Azure masterclass : manage Azure cloud with ARM templates
- Basic data analysis with Java
- Beginning AI bot frameworks : getting started with bot development
- Beginning MATLAB and Simulink : from novice to professional
- Beginning anomaly detection using Python-based deep learning : with Keras and PyTorch
- 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 data science with Python and Jupyter
- Beginning machine learning in iOS : CoreML framework
- Beginning machine learning with AWS
- Best practices for bringing AI to the enterprise
- Big data analytics for intelligent healthcare management
- Big data analytics projects with Apache Spark
- Big data analytics using Apache Spark
- Bringing data to life : combining machine learning and art to tell a data story
- 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 intelligent systems : a guide to machine learning engineering
- Building machine learning and deep learning models on Google Cloud Platform : a comprehensive guide for beginners
- 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 TensorFlow
- Building natural language applications with TensorFlow
- Building practical recommendation engines, Part 1
- Building predictive models with machine learning and Python
- C# machine learning projects : nine real-world projects to build robust and high-performing machine learning models with C#
- C++ deep learning with Caffe
- Clojure for data science : statistics, big data, and machine learning for Clojure programmers
- Clojure for machine learning : successfully leverage advanced machine learning techniques using the Clojure ecosystem
- Clustering & classification with machine learning in R : harness the power of machine learning for unsupervised & supervised learning in R
- Clustering and classification with machine learning in Python
- 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 trust models and machine learning
- Computer vision projects with OpenCV and Python 3 : six end-to-end projects build using machine learning with OpenCV, Python, and TensorFlow
- Computer vision projects with Python 3
- Conformal prediction for reliable machine learning : theory, adaptations and applications
- 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 analysis and exploration with Pandas
- Data analysis with Python : a modern approach
- Data analytics and machine learning fundamentals : LiveLessons
- Data analytics with R
- Data and social good : using data science to improve lives, fight injustice, and support democracy
- Data catalogs may be the new black, but metadata is not cabbage
- Data modeling with machine learning in the real world
- Data science : mindset, methodologies, and misconceptions
- 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 artificial intelligence
- Data science and engineering at enterprise scale : notebook-driven results and analysis
- Data science and machine learning series : Building web crawlers for data acquisition with Python Scrapy
- Data science and machine learning series : Closed form solution of linear regression and locally weighted regressions (LOWESS)
- Data science and machine learning series : Data visualization and data generation using Python and Matplotlib
- Data science and machine learning series : Linear algebra made simple
- Data science and machine learning series : Manipulating matrices using NumPy
- Data science and machine learning series : Multivariate linear regression (multiple-linear regression) complete guide
- Data science and machine learning series : Probability distribution, statistics, and data analysis using Pandas
- Data science and machine learning series : Statistics distributions
- Data science and machine learning series : Using Python to master the concepts of object oriented programming (OOP), file handling, and iteration
- Data science and machine learning series : advanced convolutional neural networks (CNNs) and transfer learning
- Data science and machine learning series : advanced neural networks
- Data science and machine learning series : convolutional neural networks (CNNs)
- Data science and machine learning with Python--Hands on!
- Data science essentials advanced algorithms and visualizations
- 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 Python
- Data science in the cloud with Microsoft Azure machine learning and R : 2015 update
- Data science with Java : practical methods for scientists and engineers
- 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 science with SQL server quick start guide : integrate SQL server with data science
- Data visualization recipes in Python
- Dealing with real-world data, Part 1, Introduction to real-world machine learning
- Deep Belief Nets in C++ and CUDA C, Volume 2, Autoencoding in the complex domain
- Deep learning : a practitioner's approach
- Deep learning : facts, frameworks, and functionality
- 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 adventures with PyTorch
- Deep learning and neural networks in PyTorch for beginners
- 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 Python developers
- 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 strategic decision makers : understanding deep learning and how it produces business value
- Deep learning for the life sciences : applying deep learning to genomics, microscopy, drug discovery, and more
- Deep learning for time series data
- Deep learning from scratch : building with Python from first principles
- Deep learning fundamentals
- Deep learning illustrated : a visual, interactive guide to artificial intelligence
- Deep learning in the browser
- Deep learning pipeline : building a deep learning model with TensorFlow
- Deep learning projects with JavaScript
- 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 Azure : building and deploying artificial intelligence solutions on the Microsoft AI platform
- Deep learning with Java
- 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 1.x : implement deep learning techniques and neural network architecture variants using 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 video edition
- Deep learning with R
- Deep learning with R
- Deep learning with R in motion
- 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 : explore neural networks and build intelligent systems with Python
- 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, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
- 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 NLP applications using NLTK in Python
- Developing an image classifier using TensorFlow : convolutional neural networks
- Distributed deep learning with Apache Spark
- Diversity heuristic
- Dynamic neural network programming with PyTorch
- Effective Amazon machine learning : machine learning in the Cloud
- Effective enterprise architecture
- Ensemble learning
- Ensemble machine learning cookbook : over 35 practical recipes to explore ensemble machine learning techniques using Python
- Ensemble machine learning techniques
- Ethics and data science
- Evaluating machine learning models : a beginner's guide to key concepts and pitfalls
- Executive briefing : why machine-learned models crash and burn in production and what to do about it
- Extending machine learning algorithms
- Extreme Learning Machines (ELMs) within artificial intelligence (AI)
- 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
- Federated learning in TensorFlow
- Foundations of deep reinforcement learning : theory and practice in Python
- Fraud detection without feature engineering
- From 0 to 1 : Machine learning, NLP & Python : cut to the chase
- From 0 to 1 : Spark for data science with Python
- Fundamentals of deep learning : designing next-generation machine intelligence algorithms
- Fundamentals of machine learning with scikit-learn
- Fundamentals of statistical modeling and machine learning techniques
- Game engines and machine learning
- Generative adversarial networks cookbook : over 100 recipes to build generative models using Python, TensorFlow, and Keras
- Generative adversarial networks projects : build next-generation generative models using TensorFlow and Keras
- Generative deep learning : teaching machines to paint, write, compose, and play
- Genetic algorithms and machine learning for programmers : create AI models and evolve solutions
- Getting started with Java deep learning
- Getting started with MATLAB machine learning
- Getting started with NLP and deep learning with Python
- Getting started with SAS Enterprise Miner for machine learning : learning to perform segmentation and predictive modeling
- Getting started with TensorFlow
- Getting started with TensorFlow 2.0 for Deep Learning
- Getting started with TensorFlow : get up and running with the latest numerical computing library by Google and dive deeper into your data!
- Getting started with TensorFlow for deep learning
- 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
- Getting started with machine learning with R
- Getting started with neural nets in R
- 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
- Google Cloud machine learning with TensorFlow
- Grokking deep learning
- Grokking deep learning in motion
- Hands-on AI with Python and Keras
- Hands-on Generative Adversarial Networks with PyTorch 1.x : implement next-generation neural networks to build powerful GAN models using Python
- 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 OpenCV 4 with Python
- Hands-on Python deep learning
- 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
- Hands-on TensorFlow Lite for intelligent mobile apps
- Hands-on TensorFlow for smart application development
- 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 search : building intelligent applications and perform enterprise searches
- Hands-on artificial intelligence with Java for beginners : build intelligent apps using machine learning and deep learning with Deeplearning4j
- Hands-on artificial intelligence with TensorFlow
- 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 analytics for beginners with Google Colaboratory
- Hands-on data analytics with R
- Hands-on data science and Python machine learning : perform data mining and machine learning efficiently using Python and Spark
- Hands-on data science for marketing : improve your marketing strategies with machine learning using Python and R
- Hands-on data science with Anaconda
- Hands-on data science with Anaconda : utilize the right mix of tools to create high-performance data science applications
- Hands-on data science with Java
- Hands-on deep Q-Learning
- Hands-on deep learning algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow
- Hands-on deep learning for computer vision
- Hands-on deep learning for games : leverage the power of neural networks and reinforcement learning to build intelligent games
- 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
- Hands-on deep learning with TensorFlow 2.0
- 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 feature engineering with Python
- 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 algorithmic trading : design and implement investment strategies based on smart algorithms that learn from data using Python
- 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 Amazon SageMaker
- Hands-on machine learning using JavaScript
- Hands-on machine learning wih Auto-Keras
- Hands-on machine learning with Azure : build powerful models with cognitive machine learning and artificial intelligence
- Hands-on machine learning with C# : build smart, speedy, and reliable data-intensive applications using machine learning
- 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 OpenCV
- Hands-on machine learning with Python and Scikit-Learn
- 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 Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems
- Hands-on machine learning with TensorFlow
- Hands-on machine learning with TensorFlow.js
- 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 natural language processing with PyTorch
- Hands-on neural network programming with TensorFlow (V)
- Hands-on neural networks with Keras : design and create neural networks using deep learning and artificial intelligence principles
- Hands-on problem solving for machine learning
- Hands-on reinforcement learning with Java
- Hands-on reinforcement learning with PyTorch
- Hands-on reinforcement learning with Python
- Hands-on reinforcement learning with R : get up to speed with building self-learning systems using R 3.x
- Hands-on serverless deep learning with TensorFlow and AWS Lambda : training serverless deep learning models using AWS infrastructure
- Hands-on supervised machine learning with Python
- 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
- Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
- Hands-on unsupervised learning with TensorFlow 2.0
- 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 to build privacy and security into deep learning models
- Hyperledger Fabric Blockchain deep dive
- Hyperledger Fabric and Composer : first practical Blockchain
- IBM PowerAI : deep learning unleashed on IBM Power Systems Servers
- IBM Watson for beginners
- IBM Watson projects : eight exciting projects that put artificial intelligence into practice for optimal business performance
- IOT, AI, and Blockchain for .NET : building a next -generation application from the ground up
- Identifying behaviour patterns using machine learning techniques
- Image analysis and text classification using CNNs in PyTorch : learn to build powerful image and document classifiers in minutes
- Implementing AI to play games
- Implementing deep learning algorithms with TensorFlow 2.0
- Implementing serverless microservices architecture patterns
- Improving machine learning with continuous learning models
- Industrial machine learning : using artificial intelligence as a transformational disruptor
- 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
- Interpretable and resilient AI for financial services
- Introducing data science : big data, machine learning, and more, using Python tools
- 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 classification models in machine learning using scikit-learn
- 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 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 deep learning with Caffe2
- Introduction to machine learning
- 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 essentials : dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java
- Java deep learning projects : implement 10 real-world deep learning applications using Deeplearning4j and open source APIs
- Java deep learning solutions
- Java for data science : examine the techniques and Java tools supporting the growing field of data science
- Java machine learning for computer vision
- KNIME essentials
- Keras 2.x projects : 3 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
- 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 deep learning projects
- 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
- Large scale machine learning with Python : learn to build powerful machine learning models quickly and deploy large-scale predictive applications
- Large scale machine learning with Spark : discover everything you need to build robust machine learning applications with Spark 2.0
- Learn Keras for deep neural networks : a fast-track approach to modern deep learning with Python
- Learn PySpark : build Python-based machine learning and deep learning models
- 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 algorithmic trading : build and deploy algorithmic trading systems and strategies using Python and advanced data analysis
- Learn artificial intelligence with TensorFlow
- Learn from the experts : artificial intelligence
- Learn from the experts : artificial intelligence
- Learn how to build intelligent data applications with Amazon Web Services (AWS) : understanding and using AWS products and services, AWS Data Pipeline, Kinesis Analytics, RDS and Redshift databases, and Amazon Machine Learning
- Learn machine learning in 3 hours
- 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 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 Microsoft Cognitive Services : use Cognitive Services APIs to add AI capabilities to your applications
- Learning Pandas
- Learning Python data analysis
- Learning Python for data science
- Learning Salesforce Einstein : artificial intelligence and deep learning for your Salesforce CRM
- Learning TensorFlow 2.0
- Learning TensorFlow : a guide to building deep learning systems
- Learning computer vision with TensorFlow
- Learning data analysis with R
- Learning from multiagent emergent behaviors in a simulated environment
- Learning generative adversarial networks
- Learning neural networks with TensorFlow
- Learning path : Python : machine and deep learning with Python
- Learning path : expert Python projects
- Learning path : machine learning
- Learning quantitative finance with R : implement machine learning, time-series analysis, algorithmic trading and more
- Learning scikit-learn : machine learning in Python : experience the benefits of machine learning techniques by applying them to real-world problems using Python and the open source scikit-learn library
- Leveraging data science in asset management
- Leveraging multi-CDN at Riot Games
- Linear algebra for data science in Python
- Linear regression, Part 2, Introduction to real-world machine learning
- Linux command line : from zero to expert
- Live-coding a machine learning model from scratch
- MATLAB deep learning : with machine learning, neural networks and artificial intelligence
- MATLAB for machine learning : functions, algorithms, and use cases
- MATLAB machine learning
- MATLAB machine learning recipes : a problem-solution approach
- ML at Twitter : a deep dive into Twitter's timeline
- Machine Learning : an algorithmic perspective
- Machine Learning with Microsoft technologies : selecting the right architecture and tools for your project
- Machine Learning with scikit-learn quick start guide : classification, regression, and clustering techniques in Python
- Machine learning 101 with Scikit-Learn and StatsModels
- Machine learning : a Bayesian and optimization perspective
- Machine learning : theory and applications
- Machine learning algorithms : popular algorithms for data science and machine learning
- 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 TensorFlow : the Google Cloud approach
- Machine learning and data science : an introduction to statistical learning methods with R
- Machine learning and data science series : Spark as a data science tool
- Machine learning and data science series : preparing a data science presentation
- Machine learning and security : protecting systems with data and algorithms
- Machine learning applications using Python : cases studies from healthcare, retail, and finance
- Machine learning at enterprise scale : how real practitioners handle six common challenges
- Machine learning classification algorithms using MATLAB
- Machine learning for Android app development using ML Kit
- Machine learning for OpenCV : a practical introduction to the world of machine learning and image processing using OpenCV and Python
- Machine learning for OpenCV : advanced methods and deep learning
- Machine learning for OpenCV : supervised learning
- Machine learning for absolute beginners
- Machine learning for algorithmic trading bots with Python
- Machine learning for apps
- Machine learning for business : using Amazon SageMaker and Jupyter
- Machine learning for cybersecurity cookbook : over 80 recipes on how to implement machine learning algorithms for building security systems using Python
- Machine learning for data science
- Machine learning for data science
- Machine learning for decision makers : cognitive computing fundamentals for better decision making
- 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 finance : principles and practice for financial insiders
- Machine learning for healthcare analytics projects : build smart AI applications using neural network methodologies across the healthcare vertical market
- 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 data analysis : best practices in prediction and anomaly detection using Python
- Machine learning fundamentals
- Machine learning fundamentals
- Machine learning fundamentals with Amazon SageMaker on AWS : LiveLessons
- Machine learning in R : automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks
- Machine learning in image steganalysis
- Machine learning in production : developing and optimizing data science workflows and applications
- Machine learning in the AWS Cloud : add intelligence to applications with Amazon SageMaker and Amazon Rekognition
- Machine learning in the cloud with Azure machine learning
- Machine learning is changing the rules : ways business can utilize AI to innovate
- Machine learning logistics : model management in the real world
- Machine learning on iOS with CoreML
- Machine learning primer
- Machine learning projects for mobile applications : build Android and iOS applications using TensorFlow Lite and Core ML
- Machine learning projects with Java
- Machine learning quick reference : quick and essential machine learning hacks for training smart data models
- Machine learning series : K-Means Clustering in Python
- Machine learning series : decision tree algorithm in Python
- Machine learning series : logistic regression
- Machine learning series : the Support Vector Machine (SVM) in Python
- Machine learning series : the XGBoost Algorithm in Python
- Machine learning solutions : expert techniques to tackle complex machine learning problems using Python
- Machine learning systems : designs that scale
- Machine learning using Python
- Machine learning using R : a comprehensive guide to machine learning
- Machine learning using R : with time series and industry-based uses in R
- Machine learning with AWS : explore the power of cloud services for your machine learning and artificial intelligence projects
- Machine learning with Apache Spark quick start guide : uncover patterns, derive actionable insights, and learn from big data using MLlib
- Machine learning with C ++
- Machine learning with C++ : choosing the right algorithm
- Machine learning with Core ML : an iOS developer's guide to implementing machine learning in mobile apps
- Machine learning with Core ML in iOS 11
- Machine learning with Go
- Machine learning with Go : implement regression, classification, clustering, time-series models, neural networks, and more using the Go programming language
- Machine learning with Jupyter notebooks in Amazon AWS
- Machine learning with PySpark : with natural language processing and recommender systems
- Machine learning with PyTorch
- Machine learning with Python
- Machine learning with Python cookbook : practical solutions from preprocessing to deep learning
- Machine learning with Python for everyone
- Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5
- Machine learning with R series : K nearest neighbor (KNN), linear regression, and test mining
- Machine learning with Scikit-Learn and Tensorflow
- Machine learning with Scikit-learn
- Machine learning with Spark : create scalable machine learning applications to power a modern data-driven business using Spark
- Machine learning with Spark : develop intelligent machine learning systems with Spark 2.x
- Machine learning with Swift : artificial intelligence for iOS