Artificial intelligence has transformed our life into smart life by its intervention in every sphere of daily activities. No matter whether it is communication or transportation, it is an undeniable fact that we are addicted to artificial intelligence. This rapid advancement of artificial intelligence also inspired talent and resources to dedicate themselves to accelerating the growth of technologies. There come to the open-source tools for artificial intelligence, which has widened the scope of various development and activities of artificial intelligence.
This blog will discuss the 10 most-used open-source tools for artificial intelligence that have contributed to various arms of artificial intelligence like machine learning (ML), deep learning, etc.
1 . Caffe
This tool is the brainchild of a UC Berkeley Ph.D. candidate. Caffe is a deep learning framework based on extensible code and expressive architecture. This tool is useful for both enterprise use as well as for research for its speed. According to its website, this open-source tool for artificial intelligence can process more than 60 million images in a single day using just one NVIDIA K40 GPU. Managed by the Berkeley Vision and Learning Center (BVLC), this tool has got grants from NVIDIA and Amazon for its development.
It is a dynamic open-source AI tool written in C++ and has a Python interface. Its impressive architecture allows you to switch between the CPU and GPU. Also, this AI tool is vastly used by academic researchers. Besides, it has a broad scale application in the multimedia and vision domains.
The expressive architecture of Caffe encourages innovation and application. You can model and optimize with configuration without hard-coding. Furthermore, you can switch between CPU and GPU by setting a single flag. Then you can deploy to mobile devices and commodity clusters. At the same time, the extensible code of Caffe fosters active development. Caffe holds a large community that includes research associates, startups, and even large-scale industrial applications in multimedia, vision, and speech.
Link: http://caffe.berkeleyvision.org.
2. H2O
This open-source ML software tool, designed by H2O.ai, aims to democratize AI. AI researchers and developers use it. This open-source tool for AI is mainly used for predictive data analytics and is written in the R, Python, and Java programming languages. AI researchers and developers can use this tool in whichever language they’re familiar with. The tool can also analyze data sets in Apache Hadoop file systems and the cloud. H2O supports different operating systems, including Microsoft Windows, Linux, and MacOS.
This is an open-source deep learning platform that helps in decision making based on the in-depth analysis of data. Hence, the user can obtain useful insights. The tool has two open-source versions and is widely used in predictive modeling, fraud analysis, and healthcare.
Link: https://www.h2o.ai/
Related post – Top 10 Artificial Intelligence Software
3. TensorFlow
This open-source tool for artificial intelligence comprised an ML library launched by Google Brain for better AI applications. Later, in 2015, TensorFlow was launched under the Apache 2.0 open source license. You can write libraries for dataflow programming using this tool. Besides, it can be used with two programming languages — Python and C++. Its launch’s initial purpose was to use it with Gmail, Google search, and Google photos.
At the same time, programmers use TensorFlow for numerical computations. It is compatible with different platforms for its architecture and easy-to-use interface. Also, it is compatible with CPUs, TPUs, and GPUs. You can use it on your PC, laptop, and mobile. This tool is effectively being used in the research field for numerical computation and neural network applications.
There are multiple APIs of TensorFlow. The lowest level API is TensorFlow Core, which gives you complete programming control. This is the base for higher-level APIs. Tensor is the central unit of data, which consists of a set of primitive values. These values are shaped into an array of any number of dimensions, which is known as rank.
Link: https://www.tensorflow.org/
4. Apache SystemML
This open-source tool for artificial intelligence is flexible, scalable, and deploys machine learning algorithms for big data. This is an invention of IBM and has become an optimal platform for machine learning using Big data. The tool uses declarative machine learning language or DML, a high-level programming language and very similar to PyDML used by Python developers for algorithm deployment.
Based on cluster characteristics and data, the tool can automatically optimize usage and improves efficiency. Furthermore, it can have multiple execution modes that include Spark Batch, Spark MLContext, Standalone, Hadoop batch, and JMLC.
You can run SystemML on top of Apache Spark and scale data automatically. In the coming future, SystemML will include deep learning capabilities that can import and run neural network architectures.
Link: https://systemml.incubator.apache.org/
5. Deeplearning4j
Deeplearning4j is an open-source tool for Artificial intelligence and specially designed for deep learning. It was released under the Apache License 2.0. It uses its own deep learning library, known as ND4J, created for Java Virtual Machine (JVM). The library works with both CPUs as well as GPUS.
Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API.
The tool framework is used to form neural nets as well as many advanced visualization tools. At the same time, this tool is used in the field of cybersecurity and image recognition. Besides, you can integrate this tool with other open-source AI tools like TensorFlow.
Link: https://deeplearning4j.org/
6. OpenCyc
Launched by Cycorp, OpenCyc is an open-source tool for artificial intelligence that works as a general knowledge database with text understanding. Cycorp facilitates the use of OpenCyc with unrestricted access and knowledge base so that the tool can be used in various applications. You can access the vast knowledgebase in two formats – ontology as well as semantic web endpoints. Furthermore, the knowledge base contains facts, concepts, assertions, rules, and taxonomies.
The tool enables applications to separate from an extensive database and process relevant information to interpret accurately. It helps to differentiate relative words with its synonyms against a particular key search. It instills human cognitive abilities in the functionality. This is the point where OpenCyc has revolutionized ML.
Link: http://www.cyc.com/opencyc/
7. Apache Mahout
This open-source tool for Artificial intelligence is basically a machine learning tool that comes under the Apache license. It is designed by the Apache Foundation and is built on Mahout Hadoop. It uses linear algebra and statistics to solve common math problems.
This tool is mainly used for complex and detailed Big data analysis. The algorithms used in this tool help users to grouping and clustering Big data. Also, it supports cross-platform operating systems and uses R-like syntax.
Link: https://mahout.apache.org/
8. ONNX (Open Neural Network Exchange)
ONNX is an open-source tool for artificial intelligence that is extensively used in deep learning models. It is primarily a Facebook open source project. However, also endorsed by AWS and Microsoft. This is a joint contribution of Facebook and Microsoft, where Microsoft contributed by adding its Cognitive Toolkit and Project Brainwave platform.
ONNX defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Each computation dataflow graph is structured as a list of nodes that form an acyclic graph. Nodes have one or more inputs and one or more outputs. Each node is a call to an operator. The graph also has metadata to help document its purpose, author, etc.
Operators are implemented externally to the graph, but the set of built-in operators are portable across frameworks. Every framework supporting ONNX will provide implementations of these operators on the applicable data types.
Link: https://onnx.ai/
9. MLlib
This tool is an initiative of Apache Spark and is a machine learning library for learning algorithms. MLib uses different widely used programming languages like R, Scala, Java, and Python. It can also run on many different platforms, including Hadoop, Kubernetes, or in the cloud. This library contains many core ML and deep learning algorithms. Hence, it makes ML easier and more practical for AI researchers.
Following ML algorithms are part of MLib:
- Classification: logistic regression, naive Bayes
- Regression: generalized linear regression, survival regression
- Decision trees, random forests, and gradient-boosted trees
- Recommendation: alternating least squares (ALS)
- Clustering: K-means, Gaussian mixtures (GMMs)
- Topic modeling: latent Dirichlet allocation (LDA)
- Frequent itemsets, association rules, and sequential pattern mining
Link: https://spark.apache.org/mllib/
10. Distributed Machine Learning Toolkit (DMTK)
This is an open-source ML tool that simplifies various tasks on Big Data. DMTK is a contribution of Microsoft that regularly add new and advanced algorithms. This tool is mostly useful for researchers in experimenting with algorithms. Researchers can modify and tweak the algorithms according to their needs. The DMTK enables innovation in both ML algorithms and the system’s algorithm.
The current version of DMTK includes the following components (more components will be added to the future versions):
• DMTK Framework: a flexible framework that supports a unified interface for data parallelization, a hybrid data structure for big model storage, model scheduling for big model training, and automatic pipelining for high training efficiency.
• LightLDA, an extremely fast and scalable topic model algorithm, with an O(1) Gibbs sampler and an efficient distributed implementation.
• Distributed (Multisense) Word Embedding, a distributed version of (multi-sense) word embedding algorithm.
• LightGBM: a very high-performance gradient boosting tree framework (supporting GBDT, GBRT, GBM, and MART), and its distributed implementation.
Link: http://www.dmtk.io/