Software for artificial intelligence

Machine learning, neural networks and artificial intelligence have become dominant themes in the development of applications, bots, programs, and services.

Regardless of whether you are a simple developer, a startup, or already a large company, you need the right tools to get the job done. That is why, Gartner predicted that 80% of emerging technologies will have AI foundations by 2021.

In addition, as a result of its popularity, the developer community itself has grown, which also led to the emergence of AI frameworks, making it much easier to study artificial intelligence!

Artificial intelligence (AI) is slowly becoming more mainstream, as companies amass large amounts of data and look for the right technologies to analyze and leverage it.

Large and small retailers can use data science to predict goods demand during a certain period of time and place or analyze sentiments about a company, product, and service, or brand.

So you may find it useful to visit this website and learn more about data science frameworks that can help you launch any data science project.

In this post, we’ll talk about the best software that can help you develop and learn about artificial intelligence.

TensorFlow

TensorFlow is an open source, comprehensive machine learning platform. Like most deep learning frameworks, TensorFlow has a Python API on top of the C and C ++ engine, making it faster.

TensorFlow has a flexible ecosystem of tools, libraries, and community resources. This allows researchers to use the most advanced ML technologies, and developers to create and deploy applications based on machine learning.

The platform provides intuitive high-level APIs like Keras with fast execution, allowing immediate model iteration and easy debugging.

Due to its multi-platform nature, the solution allows you to train and deploy models in the cloud and locally, regardless of the language used by the user.

PyTorch

PyTorch is an open source Python machine learning framework that provides GPU-accelerated tensor computing. The framework is suitable for rapid prototyping in research, as well as for hobbyists and small projects.

The framework offers dynamic computation graphs that allow you to handle variable length input and output, which is useful, for example, when working with recurrent neural networks.

By deeply integrating the framework with C ++ code, developers can program in C and C ++ using the FFI-based Python API Extension.

Unlike TensorFlow, PyTorch is less flexible in supporting multiple platforms. It also lacks native data visualization tools, but has a third-party counterpart called tensorboardX.

Keras

Keras is an open source deep learning framework written in Python. The framework is aimed at operational work with neural networks and is compact, modular and extensible.

Suitable for small projects, since it is difficult to create something large-scale on it and it will obviously lose in the performance of neural networks to the same TensorFlow.

Keras works on top of TensorFlow, CNTK and Theano and provides an intuitive API that our engineers believe is the best of its kind so far.

The framework contains numerous implementations of widely used building blocks of neural networks, such as layers, target and transfer functions, optimizers, as well as many tools to simplify the work with images and text.

Darknet

Darknet is an open source framework written in C using the CUDA hardware / software parallel computing architecture. It’s fast, lightweight and easy to use. Darknet also supports CPU and GPU computing.

The Darknet stores the trained weights in a format that can be recognized using different methods on different platforms. However, this can be a problem if you decide to train a model on one heavy duty equipment and then use it on another.

Since the framework is written in C and has no other API, then in the case when the platform requirements or your own preferences force you to turn to another programming language, you will have to additionally fuss over its integration.

In addition, it is only distributed in source format, and the compilation process on some platforms can be somewhat problematic.

Caffe

Caffe is a deep learning framework. It is made with the expectation of expressiveness, speed and modularity. Initially, the framework was created for machine vision projects, but has since evolved and is now used for other tasks, including speech recognition and working with multimedia.

The main advantage of Caffe is speed. The framework is entirely written in C ++, supports CUDA, and, if necessary, can switch the processing flow between the processor and the video card. The package includes a set of free and open source reference models for standard classification tasks.

Caffe2, the second iteration of Facebook’s Caffe framework, originally designed for machine vision, now includes other uses such as speech and multimedia. The main features of Caffe2 are speed and modularity, so the framework is written primarily in C ++.

Microsoft Azure ML Studio

Given the massive amount of data and processing power required for machine learning, clouds are an ideal environment for ML applications. Azure ML Studio allows you to create and train models, turn them into APIs to provide other services.

One user account can be allocated up to 10 GB of storage space, although you can connect your own Azure storage. A wide variety of algorithms are available, both created by Microsoft and third parties.

To try the service, you don’t even need to create an account, you just need to sign in anonymously, and you can run Azure ML Studio for eight hours.

Microsoft Azure ML Studio allows users to create and train models, turn them into APIs and use them in other services. The free version has 10 GB of storage for training data.

The service includes many training algorithms from both Microsoft and third-party companies.

AI means integrating more data sources and using that data to improve the prioritization, personalization and content of marketing campaigns using machine learning algorithms.

An effective AI will eventually become an integral part of a successful and profitable business.

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