Machine Learning is a type of Artificial Intelligence that allows software applications to learn from the data and become more precise in predicting outcomes without human intervention.
A machine learning framework is an interface that allows developers to build and deploy machine learning models faster and easier.
There are three types of Machine Learning like,
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
When considering whether to build or buy a machine learning framework, it is important to:
- Understand the costs and benefits of both options.
- Figure out the technical resources you would need to maintain an ongoing machine learning lifecycle in both conditions.
- Be a champion of the transformational capabilities of enterprise machine learning at your organization.
Top 10 Machine Learning Frameworks:
- Tensor Flow: TensorFlow is a popular, open source, free framework that offers many tools and libraries for Machine Learning. Based on JavaScript, it helps in building models with high quality APIs and provides many generalized options to choose from.
- Sonnet: Sonnet is best utilized for creating complicated neural network structures in TensorFlow. Easy to use – integrate and possesses efficient libraries to support.
- Keras: It is considered a preferred choice for beginners; Python based developers and assists in coding precisely and effectively. There is an inbuilt support for data parallelism.
- Caffe: Caffe signifies “Convolutional Architecture for Fast Feature Embedding”. It is a popular deep learning framework that is written in C++.
- Apache Mahout: Apache Mahout has its main focus on linear algebra and statistical engines.
- Apache Spark: Apache Spark is an open source ML framework that offers programming interface for complete clusters.
- Accord .Net: Accord .NET is a popular ML framework that offers complete focus on areas like neural networks, regression, statistics, clustering etc. It also handles audio / video processing libraries. These libraries exist as the base code as well as different packages.
- Gluon: Gluon has been a current add-on to the popular ML frameworks. It allows the users to choose from a dynamic neural network and any structure they wish to select from.
- Firebase ML Kit: Google came up with a novel idea of Firebase ML Kit – a strong contender in the list of ML frameworks. Firebase ML Kit gives image labelling, text recognition and object categorization.
- Apple’s Core ML: it was made for iOS, TVOS apps and macOS. Apple’s Core ML is a user-friendly framework, easily adaptable by novices.
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