Artificial Intelligence (AI) is now in trend because people are looking for some sought of technology that makes their lives more easy and valuable. Even the smartphones are shifting their focus towards Artificial Intelligence. Big companies like Google, Amazon, and Facebook are already working on it and contributing in the form of Opensource AI Tools. For example, Facebook came up with an open-source AI project called Torchnet to accelerate the AI research and in the same way, Google open-source AI project is DeepMind Lab. A Recent study at Standford University stated that the AI (report) will show it huge impact in coming years. So, today in this article we are going to show some variety of useful open-source artificial intelligence software that helps in building your AI projects.
Top Opensource Artificial Intelligence Tools for AI projects
All the given Open source tools are well documented to create one of your own, have a look.
Caffe
Caffe was developed by Berkeley Vision and Learning Center. Caffe is a deep learning framework released under open source license and made with expression, speed, and modularity in mind. graphics processors from NVidia are used to ensure that the solution has sufficient performance. The software is available as an open-source system. It can process 60 million images per day with a single NVIDIA K40 GPU.
Caffe2 can be used for neural networks and also works with smartphones. The software can be a valuable asset to those working on AR and social network. Combining AR and the artificial world will yield new types of programs that can interact with the real world.
Caffe can be used, for example, for speech recognition, the recognition and classification of images or for the development of natural languages in AI devices. You can also take a demo of it using the link given below.
Website Link: caffe.berkeleyvision
To install it on Ubuntu here is the commands:
For the only CPU
sudo apt install caffe-cpuCuda version or Graphics card
sudo apt install caffe-cudaTensorflow
Tensorflow is an open-source software library for numerical computation Intelligence. It performs the computation using the data flow graphs. It has a very flexible architecture that can deploy the computation using a single API on multiple CPU or GPU. IT was developed by the Google Brain Team within Google’s Machine Intelligence research.
Website Link: www.tensorflow.org
Deeplearning4j
Deeplearning4j means Deep Learning for Java is an open-source deep-learning library for Java Virtual Machine (JVM). It is designed to run with enterprise application such as Hadoop and Apache Spark.
Website Link: Deeplearning4j.org
H20: Open Source AI Platform
H20 is an open-source deep learning platform. It is an artificial intelligence tool which is business-oriented and help them to make a decision from data and enables the user to draw insights. There are two open-source versions of it: one is standard H2O and other is paid version Sparkling Water. It can be used for predictive modelling, risk and fraud analysis, insurance analytics, advertising technology, healthcare and customer intelligence.
H2O can directly access HDFS, but also data from Yarn, a big data analysis system, and MapReduce. H2O can also be started directly in Amazon AWS EC2 instances. It can communicate with Hadoop via Java, but Python, R and Scala can also be used, including all supported packages.
Website Link: www.h2o.ai
MLlib – Machine Learning Library
MLlib is an open-source Spark’s scalable machine learning library. It easily integrates with Hadoop and includes a host of machine learning algorithms for classification, regression, decision trees, recommendation, clustering, topic modelling, feature transformations, model evaluation, ML pipeline construction, ML persistence and survival analysis.
Website Link: Spark.apache.org/mllib
Mahout
Mahout is an open-source machine learning framework and offers three major features: programming environment and framework for building scalable algorithms, wide variety of premade algorithms for Scala + Apache Spark, H2O, Apache Flink and Samsara, a vector math experimentation environment with R-like syntax which works at scale.
Website Link: http://mahout.apache.org/
Distributed Machine Learning Toolkit
It is an open-source artificial intelligence tool from Microsoft. This toolkit is designed to use in big data applications. It designed to run train AI systems faster. It consists of three key components: the DMTK framework, the LightLDA topic model algorithm, and the Distributed (Multisense) Word Embedding algorithm.
Website Link: http://www.dmtk.io/
NuPIC
NuPIC is an open-source artificial intelligence project based on a theory called Hierarchical Temporal Memory. Numenta is tackling one of the most important scientific challenges of all time: reverse-engineering the neocortex.
Website Link: numenta.com
Scikit-learn – machine learning in Python
Scikit-learn is available under BSD license and can be used commercially free of charge. We can use it to create artificial intelligence-based applications such as bots, Spam detection, image recognition and voice assistants. Furthermore, Scikit-learn can also be used for data mining and data analysis.
The Scikit-learn library, derived from SciPy Toolkit, is based on the Python programming language. Packages like NumPy, SciPy, or Matplotlib are used by Scikit-learn to write mathematical, scientific or statistical programs in Python.
If you want to work with machine learning and artificial intelligence-based on Python, you should take a look at the possibilities of Scikit learning.
Open Neural Networks Library (OpenNN)
OpenNN is also an open-source class library written in C++ for deep learning, it is used to instigate neural networks. OpenNN is an open-source class library written in C++ programming language. It implements neural networks, the main area of machine learning research. It’s characterized by a deep architecture and high performance.
Website Site: www.opennn.net
Oryx 2
Oryx 2 developed on Apache Spark and Apache Kafka and initially called Oryx project. It has a specialization for real-time large-scale machine learning. Also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering.
Website Link: oryx.io
OpenCyc
OpenCyc is an open-source portal, it is basically a general knowledge base and commonsense reasoning engine of the world. It has Rich domain modelling, Domain-specific expert systems, Text understanding, Semantic data integration, AI games and more.
Website Link: www.cyc.com/platform/opencyc/
Apache SystemML
Apache SystemML is an open-source artificial intelligence platform for machine learning ideal for big data. Its main features:
- Runs on R Python-like syntax
- Focused on big data
- Designed specifically for high-level math
Website Link: systemml.apache.org
ONNX- Open Neural Network Exchange
ONNX is an open format to represent deep learning models. It helps the AI developers to easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners. It comes under the Facebook open source project and supported by Microsoft and Aws.
Website: https://onnx.ai/
Mycroft
It also an Open Source Artificial Intelligence project that uses Raspberry Pi. For more info: https://mycroft.ai/
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OPEN SOURCE AI
I do like the H20: Open Source AI Platform that you mentioned in your blog. Can you tell me how exactly it can be useful for customer intelligence? And is it also helps to improve customer experience?
AI is all around us today. It has huge implications for the future of many industries and those who explore its applications early on are the ones that are going to get the biggest benefits later. It’s also likely that the industry around AI is going to evolve at a very rapid pace, so entering the game now is a move that will also allow you to keep up with the pace. CSAT.AI, Salesforce Einstein, MaestroQA are few of the tools which are constantly upgrading their services to provide better customer experience.
Thank you for this great read. I think its a very informative article! I had experience working with Tensorflow and Caffe, and I must say they are some awesome tools to get the planned results out of our projects. I plan to try Oryx 2, and I’m really interested in how it will work for me. I love how you listed these options, I will definitely go back to this article to seek out more tools!
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