7 popular tools and frameworks for developing AI applications

Artificial intelligence (AI) is a rapidly growing field with many applications, including computer vision, natural language processing (NLP), and speech recognition. To develop these AI applications, developers use a variety of tools and frameworks that provide a comprehensive platform for building and deploying machine learning models.

In this article, we’ll explore seven popular tools and frameworks used to develop AI applications: TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano, and Apache MXNet. The ease of use, scalability, and efficient execution of complex mathematical operations make these tools the go-to choice for developers.

Deep Learning Fabric (#FfDL), #Open Source A project that gives developers the flexibility to use popular deep learning libraries such as #TensorFlow, #Cafeand #torch https://t.co/DpTv5uRcDE pic.twitter.com/VozsIXPk8n

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TensorFlow

TensorFlow is an open source platform developed by Google that provides a comprehensive framework for building and deploying machine learning models across multiple platforms. It is widely used for various applications such as computer vision, natural language processing, and speech recognition. For example, it can be used to build chatbots that can understand and respond to natural language queries.

and @TensorFlowThis Expert Is Building Groundbreaking Machine Learning Models In Image And Speech Recognition → https://t.co/o2GMG9yYeu

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PyTorch

PyTorch is another popular open-source machine learning framework widely used for developing AI applications such as image recognition, natural language processing, and reinforcement learning. It provides dynamic computation, so you can easily experiment with different model architectures.

For example, it can be used to build image recognition systems that can detect and classify different objects in images.

Keras

Keras is an open source neural network library that runs on top of TensorFlow or Theano. It is a user-friendly platform that allows developers to create and train deep her learning models with just a few lines of code. You can use Keras to build a speech recognition system that can transcribe spoken words to text.

Related: 5 Natural Language Processing (NLP) Libraries to Use

Cafe

Caffe is a deep learning framework developed by Berkeley AI Research (BAIR) and community contributors. It is designed for fast training of convolutional neural networks and is commonly used for image and speech recognition.

Microsoft Cognitive Toolkit (CNTK)

CNTK is an open-source framework developed by Microsoft that provides a scalable and efficient platform for building deep learning models. It supports multiple programming languages, including C++, Python, and C#. It can be used to build machine translation systems that can translate text from one language to another.

Video: Using the Microsoft Cognitive Toolkit (#CNTK) to build #NeuralNetworkshttps://t.co/mAewijuJ04#machine learning #AI pic.twitter.com/TGdiP5rGqE

— Adnan Hashmi (عدنانھاشمی) (@adnan_hashmi) June 9, 2018

Theano

Theano is a popular Python library for numerical computation specifically designed for building and optimizing deep neural networks. It is known for its efficient execution of mathematical formulas and is useful for training complex models. For example, it can be used to build a sentiment analysis system that can identify the sentiment of a given piece of text.

Related: 5 Programming Languages ​​to Learn for AI Development

Apache MXNet

Apache MXNet is a scalable and efficient open-source deep learning framework that supports multiple programming languages ​​such as Python, R, and Scala. It is widely used for computer vision, NLP, and speech recognition applications. For example, it can be used to build systems that can identify different emotions in a given text or speech.