Top 15 Machine Learning Frameworks You Must Know
In this tutorial, we will learn about top frameworks of machine learning that every experts must know. Let’s start!!!
Machine Learning frameworks
Machine learning is a vast topic. There are many frameworks that help us practically apply ML concepts. Each one of these takes time to learn and some are easier to master than others. This article provides a gentle introduction to the various frameworks available so you can choose the one that aligns with your requirements
Machine Learning algorithms are numerous, impossibly mathematical and hard to master.
A framework simplifies these algorithms and makes them easier to understand. Frameworks can take the form of tools, libraries or interfaces that make it possible to apply ML models to data without needing to understand the complex details.
Top 15 Frameworks of Machine Learning
1. Tensorflow
Tensorflow, developed by Google, is among the most popular frameworks today. It’s an open source framework that uses statistical flow graphs to do mathematical calculations. TensorFlow uses data flow graphs, in which a collection of algorithms specified by a graph may process batches of data called tensors.
2. Sci-kit Learn
Probably one of the best applications of machine learning is Scikit-learn. It’s the best option for both unsupervised and supervised training algorithms. For ordinary ML and web mining tasks, this framework requires a number of computations, such as grouping, regression, and sorting.
3. Caffe
Caffe is yet another common learning framework that emphasizes fluency, quickness, and measurable accuracy. The Berkeley Vision and Learning Center (BVLC) and internet contributors collaborated to construct it. Google’s DeepDream uses Caffe. This structure is a Python-interfaced BSD-authorized C++ library.
4. Amazon Machine Learning
Amazon Machine Learning is a solution that renders machine learning accessible to programmers of any and all skill sets. Amazon Machine Learning offers visualization aids and guides to work with you to create machine learning (ML) algorithms without needing to master difficult ML methods or technologies. It links to data held in Amazon S3, Redshift, or RDS and may develop models from it using classification tasks, multiclass categorization, or regression.
5. Massive Online Analysis (MOA)
Massive Online Analysis (MOA) is by far the most prevalent open – source approach to data stream mining, and it has a thriving community. It comprises assessment tools as well as a set of machine learning algorithms (classification, regression, clustering, outlier identification, concept drift detection, and recommender systems). MOA is similar to the WEKA project in that it is developed in Java and scales to more difficult challenges.
6. mlpack
According to the library’s developers, mlpack is a C++-based machine learning library that was first released in 2011 and is built for “scalability, speed, and ease-of-use.” For quick-and-dirty, “black box” operations, mlpack can be implemented using a cache of command-line executables, or a C++ API for more advanced tasks. Mlpack provides these as command-line scripts or C++ classes.
7. Pattern
Pattern is a Python programming language web mining package. It has data mining tools (Google, Twitter, and Wikipedia APIs, a web crawler, and an HTML DOM parser), natural language processing tools (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning tools (vector space model, clustering, and SVM), network analysis tools, and HTML canvas visualization tools.
8. MLlib
Apache Spark’s machine learning library is called MLlib (Spark). Its purpose is to make scalable and simple machine learning a reality. It has algorithms for classification, regression, clustering, collaborative filtering, dimensionality reduction tasks among others.
9. H20
H20 is a machine learning platform that is open-source. It is a business-oriented artificial intelligence application that assists in making data-driven decisions and allows the user to derive insights. Predictive modeling, risk and fraud analysis, insurance analytics, advertising technology, and healthcare are the most common applications.
10. Shogun
Shogun is one of most established machine learning libraries. It was first released in 1999 and is built in C++, but it isn’t confined to that language. Shogun integrates seamlessly in programs and platforms including Java, Python, C#, Ruby, R, Lua, Octave, and Matlab thanks to the SWIG library. Shogun is a large-scale learning platform that supports a wide range of feature types and learning contexts, such as classification, regression, and exploratory data analysis.
11. Veles
Veles is a decentralized platform for deep-learning programs that is developed in C++ but uses Python to automate and coordinate node communication. Before being supplied to the clusters, data may be evaluated and automatically normalized, and the trained model can be utilized in production right away thanks to a REST API. It emphasizes efficiency and adaptability. It features few hard-coded elements and can learn all of the common topologies, such as fully connected nets, convolutional networks, and recurrent nets, among others.
12. Apache Singa
Apache Singa is a general-purpose decentralized deep learning framework that can train massive deep learning models on huge datasets. It’s built on a simple programming approach based on layer abstraction. Feed-forward models such as convolutional neural networks (CNN), energy models such as restricted Boltzmann machine (RBM), and recurrent neural networks are all supported (RNN). Users have access to a variety of built-in levels.
13. Torch
Torch is a scientific computing platform that prioritizes GPUs and has extensive functionality for machine learning methods. LuaJIT, an easy and quick scripting language, with an underlying C/CUDA implementation makes it simple to use and efficient. Torch’s purpose is to provide you the most flexibility and speed when creating scientific procedures while keeping the process as easy as possible. Torch is based on Lua and comprises a vast ecosystem of open source packages in machine learning, computer vision, signal processing and parallel processing among other areas.
14. Sonnet
Sonnet is a high-level framework created by DeepMind for creating complicated neural network architectures in TensorFlow. As you might expect, TensorFlow serves as the foundation for this Deep Learning system. Sonnet is a Python project that tries to design and build the core Python objects that correspond to different parts of a neural network.
These items are then joined to the computational TensorFlow graph in their own right. The method of constructing Python objects separately and attaching them to a graph simplifies the construction of high-level structures.
15. Theano
Theano is a wrapper for Keras. Keras has the advantage of being a lightweight Python library for deep learning that works with either Theano or TensorFlow. It continues to run on Python 2.7 or 3.5 and can continuously operate on GPUs and CPUs.
Summary
Frameworks are tools that one can use to navigate the vast world of machine learning. This article has given you a brief introduction to 15 of the most popular frameworks in the field. Now that you are armed with this knowledge, you can pick the one that suits your needs the best.