Given the great boom in algorithm development, business and the sight of large companies such as Apple, Amazon, Google to mention a few in the accelerated development of Artificial Intelligence (AI), the interest to learn and strengthen (for those already experienced) knowledge in this field, there are various options available open-source Framework for programming in this specific field.
Even though the process of IA applications is in constant development and improvement as engineers discover new capabilities and abilities to improve their use, the available libraries allow great support for many programs in this field, and in this article, we bring you the most outstanding in open source for their application.

Let's remember that AI is the Intelligence exhibited by machines, also called computational Intelligence; and as we have already described in another opportunity; Deep Learning, robotics and AI are three very different but interrelated branches, of which the last one is perhaps the one that today has the greatest boom due to its diversity of applications in different sectors of everyday life: from apps to search engines on the net as Google use it to improve the user experience.

TensorFlow


As its official website indicates, it is an open-source software library for high-performance numerical computation. It was developed by the Google Brian team based on the advances in deep learning neural networks they had with DistBelief in 2011.
TensorFlow due to its flexible architecture can run on multiple CPUs and GPUs with additional CUDA extensions for general purpose computing on GPUs. It is available on 64-bit Linux, MacOS and Android and iOS mobile platforms.
TensorFlow is for numerical computation using data flow graphs. The nodes in the graph represent mathematical operations, while the edges of the graph represent the multidimensional data sets (tensors) communicated between them.
TensorFlow provides multiple APIs. The lower-level API, TensorFlow Core, gives you complete programming control. Top-level APIs are based on TensorFlow Core. These top-level APIs are generally easier to learn and use than TensorFlow Core.
Also, the higher level APIS allows the respective tasks to be easier and more consistent between the different developers. A high-level API such as tf.estimator helps you manage data sets, estimators, training, and inference.

Thanks to this Google can meet their needs in systems and can develop and train neural networks that allow you to detect and decipher patterns and correlations analogous to learning and reasoning used by humans.

Among the applications for which TensorFlow is the foundation, is the DeepDream automated image processing software. There is also RankBrian, officially implemented on October 26, 2015, supported by TensorFlow, to handle a substantial number of search queries on google.com.
Another very popular application based on TensorFlow is SmartReply, Deep LSTM model to automatically generate email responses.
In addition to Google, numerous recognized companies use TensorFlow for their IA applications: Airbnb, AMD, Nvidia, Uber, SAP, Kakao, Coca-Cola, among others.

Apache SystemML



It is a flexible computer learning system that automatically scales to Spark and Hadoop clusters. It was created in 2010 by IBM wrote in Java language and released as open source code on November 2, 2015. It is a kind of deep learning and Machine Learning.
An ideal workplace for automatic learning with large data. It has reached one of the top level project levels at the Apache Software Foundation. Its most important main features are:
1) Customization algorithm through R-like and Python-like languages.
2) Multiple execution modes, including Spark MLContext, Spark Batch, Hadoop Batch, Standalone and JMLC (Java Machine Learning Connector).
3) Automatic optimization based on cluster data and characteristics to ensure efficiency and scalability.

It can run on Apache Spark and is considered as the SQL for automatic learning. Future algorithm developments of SystemML include additional in-depth learning with GPU capabilities, such as importing and executing neural network architectures and pre-formed models for training.
Among the most outstanding libraries of this application, we find the JMLC for System ML, which is a programmatic interface to interact with SystemML in an embedded way, where in addition to being used as a scoring API, incorporated can be used for tasks such as unsupervised learning.

Caffe



It was initiated by Yangqing during his Ph.D. at UC Berkeley and then developed by Berkeley AI Research (BAIR) and community collaborators.
It focuses primarily on convolutional networks for computer vision applications. Caffe is a solid and popular choice for machine vision tasks and many successful models made by Caffe Model Zero users can be downloaded.
Caffe supports different types of deep learning focused on image classification and segmentation. It is used by CNN, RCNN, LSTM and fully connected to neural network designs. Supports GPU and kernel-based CPU from computational acceleration libraries such as Nvidia CuDNN and Intel MKL.
Yahoo integrated Caffe with Apache Spark to create CaffeOnSpark, a deep distributed learning framework.
Expressive architecture fosters application and innovation. Models and optimization are defined by configuration without rigid coding. Speed makes Caffe perfect for industry research and implementation experiments.

Torch


An open-source machine learning library, a scientific computing framework and a scripting language based on the Lua programming language. Among its features we have:
1) A powerful N-dimensional matrix
2) Many routines for indexing, cutting, transposing, etc.
3) Incredible interface for C, through LuaJIT
4) Linear Algebra Routines
5) Neural network and energy-based models
5) Numerical optimization routines
6) The fast and efficient GPU support
7) Inscrutable, with ports for iOS and Android backends

It has been expanded for use in Android and iOS and has been used to create hardware implementations for data streams such as those found in neural networks.
PyTorch is an open source machine learning library for Python, which is used for applications such as natural language processing. It is mainly developed by Facebook's Artificial Intelligence research group and Uber's "Pyro" software for probabilistic programming.

Mycroft



Affirming that it is the world's first open source wizard and can be used in anything from a science project to an enterprise software application.
Mycroft works anywhere: on a desktop computer, inside a car, or a Raspberry Pi. This is open source software that can be freely remixed, expanded and enhanced.
Mycroft is a virtual assistant and browser knowledge of free and open source software for Linux systems that uses natural language to receive commands to run.
Mycroft uses the syntax analyzer called Adapt to convert the natural language to data structures processed by the computer. For voice synthesis, it uses Mimic, which is based on the Festival Lite system. It is designed to be modular, so users can change their components.
It is a classic practical assistant: it can tell you the date, time, weather, set alarms or listen to the news, as well as answer all kinds of objective questions.
These are just some of the Automatic Learning projects that have been developed and improved for the optimization of programming languages and algorithm development. However, other of these projects can be found, and several programmers assure that they will always be in constant evolution so that IA and modern technologies will innovate more and more to have a better life and a better future.
As far as optimization is concerned, different programs have several approaches, which include training customers and vendor partners on ways to make their machine learning code run faster on the hardware, as well as implementing some learning functions on the parts, which is always faster.

Finally, engineers and algorithm developers are constantly in the field, talking to IT and line of business managers to get insights into how predictive analytics based on machine learning can solve real business problems.

Show All Articles