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- It is problems like this which machine learning is trying to solve.[1]
- “Deep learning” – another hot topic buzzword – is simply machine learning which is derived from “deep” neural nets.[1]
- Without a doubt, machine learning is proving itself to be a technology with far-reaching transformative powers.[1]
- Machine learning is the key which has unlocked it, and its potential future applications are almost unlimited.[1]
- Create ML lets you quickly build and train Core ML models right on your Mac with no code.[2]
- Machine learning brings together computer science and statistics to harness that predictive power.[3]
- The past decade has brought tremendous advances in an exciting dimension of artificial intelligence—machine learning.[4]
- A prediction, in the context of machine learning, is an information output that comes from entering some data and running an algorithm.[4]
- It can also be dangerously easy to introduce biases into machine learning, especially if multiple factors are in play.[4]
- In many ways, building a sustainable business in machine learning is much like building a sustainable business in any industry.[4]
- For example, machine learning can reveal customers who are likely to churn, likely fraudulent insurance claims, and more.[5]
- Companies that effectively implement machine learning and other AI technologies gain a massive competitive advantage.[5]
- Machine learning could also be used for security applications, such as analysing email communications or internet usage.[6]
- The project will focus on current and near-term (5-10 years) applications of machine learning.[6]
- The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field.[7]
- As with any concept, machine learning may have a slightly different definition, depending on whom you ask.[7]
- – Stanford “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.[7]
- Emerj helps businesses get started with artificial intelligence and machine learning.[7]
- Funding for research and development in the fields of machine learning and artificial intelligence is growing at a rapid pace.[8]
- The field of machine learning is booming and having the right skills and experience can help you get a path to a lucrative career.[8]
- From Apple to Google to Toyota, companies across the world are pouring resources into developing AI systems with machine learning.[9]
- While there are different forms of AI, machine learning (ML) represents today's most widely valued mechanism for reaching intelligence.[9]
- Machine learning became popular in the 1990s, and returned to the public eye when Google's DeepMind beat the world champion of Go in 2016.[9]
- Since then, ML applications and machine learning's popularity have only increased.[9]
- ML is one of the most exciting technologies that one would have ever come across.[10]
- Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data.[11]
- Game-playing machine learning is strongly successful for checkers, chess, shogi, and Go, having beaten human world champions.[11]
- Machine learning depends on a number of algorithms for turning a data set into a model.[11]
- As with all machine learning, you need to check the predictions of the neural network against a separate test data set.[11]
- A diverse community of developers, enterprises and researchers are using ML to solve challenging, real-world problems.[12]
- Natural Language Derive insights from unstructured text using Google machine learning.[13]
- Translation Dynamically translate between languages using Google machine learning.[13]
- A very important group of algorithms for both supervised and unsupervised machine learning are neural networks.[14]
- DeepMind continue to break new ground in the field of machine learning.[14]
- But beyond these very visible manifestations of machine learning, systems are starting to find a use in just about every industry.[14]
- Machine learning enables analysis of massive quantities of data.[15]
- The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.[16]
- Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.[16]
- As a scientific endeavor, machine learning grew out of the quest for artificial intelligence.[16]
- However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning.[16]
- In machine learning, a situation in which a model's predictions influence the training data for the same model or another model.[17]
- More typically in machine learning, a hyperplane is the boundary separating a high-dimensional space.[17]
- An i.i.d. is the ideal gas of machine learning—a useful mathematical construct but almost never exactly found in the real world.[17]
- In machine learning, often refers to the process of making predictions by applying the trained model to unlabeled examples.[17]
- The main intend of machine learning is to build a model that performs well on both the training set and the test set.[18]
- Machine learning is the concept that a computer program can learn and adapt to new data without human intervention.[19]
- The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer.[19]
- Machine learning is used in different sectors for various reasons.[19]
- The supply of able ML designers has yet to catch up to this demand.[20]
- Determining which inputs to use is an important part of ML design.[20]
- The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing.[20]
- Fortunately, the iterative approach taken by ML systems is much more resilient in the face of such complexity.[20]
- – Make revolutionary advances in machine learning and AI.[21]
- Machine learning and AI are often discussed together, and the terms are sometimes used interchangeably, but they don’t mean the same thing.[22]
- Machine learning and the technology around it are developing rapidly, and we're just beginning to scratch the surface of its capabilities.[22]
- All ML tasks can be represented this way, or it's not an ML problem from the beginning.[23]
- That's why selecting the right features usually takes way longer than all the other ML parts.[23]
- That's why the phrase "will neural nets replace machine learning" sounds like "will the wheels replace cars".[23]
- 1.1 Supervised Learning Classical machine learning is often divided into two categories – Supervised and Unsupervised Learning.[23]
- This type of machine learning involves algorithms that train on unlabeled data.[24]
- This approach to machine learning involves a mix of the two preceding types.[24]
- Uses of machine learning Today, machine learning is used in a wide range of applications.[24]
- But just because some industries have seen benefits doesn't mean machine learning is without its downsides.[24]
소스
- ↑ 1.0 1.1 1.2 1.3 What Is Machine Learning - A Complete Beginner's Guide
- ↑ Machine Learning
- ↑ Introduction to Machine Learning Course
- ↑ 4.0 4.1 4.2 4.3 How to Win with Machine Learning
- ↑ 5.0 5.1 DataRobot Artificial Intelligence Wiki
- ↑ 6.0 6.1 What is machine learning?
- ↑ 7.0 7.1 7.2 7.3 What is Machine Learning?
- ↑ 8.0 8.1 Learn Machine Learning with Online Courses and Classes
- ↑ 9.0 9.1 9.2 9.3 Machine learning: A cheat sheet
- ↑ Machine Learning
- ↑ 11.0 11.1 11.2 11.3 What is machine learning? Intelligence derived from data
- ↑ TensorFlow
- ↑ 13.0 13.1 Google Cloud
- ↑ 14.0 14.1 14.2 What is machine learning? Everything you need to know
- ↑ What is Machine Learning? A definition - Expert System
- ↑ 16.0 16.1 16.2 16.3 Machine learning
- ↑ 17.0 17.1 17.2 17.3 Machine Learning Glossary
- ↑ Machine Learning - an overview
- ↑ 19.0 19.1 19.2 Machine Learning
- ↑ 20.0 20.1 20.2 20.3 An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples
- ↑ What is Deep Learning?
- ↑ 22.0 22.1 What is machine learning?
- ↑ 23.0 23.1 23.2 23.3 Machine Learning for Everyone
- ↑ 24.0 24.1 24.2 24.3 What is Machine learning (ML)?