Top 30 AI tools for software

AI Tool Software

TensorFlow:

An open-source platform developed by Google, TensorFlow is one of the most popular AI tools used for building and training neural networks.

PyTorch:

Another open-source platform, PyTorch is widely used for developing deep learning models and building applications in the areas of natural language processing (NLP) and computer vision.

Keras:

A high-level neural networks API, Keras is easy to use and allows developers to build and train models quickly.

Scikit-learn:

This Python library is used for data analysis and predictive modeling. It includes a variety of machine learning algorithms and tools for data preprocessing, feature selection, and model evaluation.

IBM Watson:

IBM Watson is an AI platform that allows developers to build and deploy AI applications across a variety of industries.

Amazon SageMaker:

This cloud-based service from Amazon Web Services (AWS) makes it easy to build, train, and deploy machine learning models at scale.

Microsoft Azure Machine Learning:

A cloud-based service from Microsoft, Azure Machine Learning allows developers to build, deploy, and manage machine learning models.

H2O.ai:

H2O.ai is an open-source platform that provides a range of machine learning algorithms and tools for data preprocessing, model selection, and evaluation.

Google Cloud AI Platform: This cloud-based service from Google provides a range of tools and APIs for building and deploying machine learning models.

NVIDIA Deep Learning SDK: NVIDIA's Deep Learning SDK is a powerful toolkit for building and training deep learning models on NVIDIA GPUs. It includes a range of libraries and tools for data preprocessing, model training, and deployments

OpenCV:

OpenCV is an open-source computer vision library that is widely used for image and video processing, object detection, and face recognition.

Caffe:

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center. It is optimized for image classification and segmentation tasks.

Theano:

Theano is a Python library for building and training neural networks. It is optimized for CPU and GPU computation and can be used to build models for tasks such as image recognition and natural language processing.

MXNet:

MXNet is an open-source deep learning framework that is optimized for distributed computing. It supports a wide range of programming languages and can be used for tasks such as image recognition, speech recognition, and natural language processing.

RapidMiner:

RapidMiner is a data science platform that allows users to build and deploy predictive models without the need for programming.

BigDL:

BigDL is an open-source distributed deep learning library that is optimized for Intel architectures. It can be used to build and train deep learning models at scale.

DataRobot:

DataRobot is an automated machine learning platform that allows users to build and deploy predictive models without the need for programming.

PyBrain:

PyBrain is a Python library for building and training neural networks. It includes a range of algorithms and tools for tasks such as classification, regression, and reinforcement learning.

DeepCognition: DeepCognition is a cloud-based deep learning platform that allows users to build and train deep learning models without the need for programming.

AllenNLP:

AllenNLP is an open-source natural language processing library developed by the Allen Institute for Artificial Intelligence. It includes a range of models and tools for tasks such as question answering, sentiment analysis, and named entity recognition.

Spark MLlib:

Spark MLlib is a distributed machine learning library that is part of the Apache Spark project. It includes a range of algorithms and tools for tasks such as classification, regression, and clustering.

IBM Watson Studio:

IBM Watson Studio is an AI platform that allows users to build and deploy machine learning models using a range of tools and frameworks.

Deep Learning Studio:

Deep Learning Studio is a cloud-based platform for building and deploying deep learning models. It includes a range of pre-built models and tools for data preprocessing and model training.

Ludwig:

Ludwig is an open-source deep learning framework developed by Uber. It allows users to build and train deep learning models without the need for programming.

MLflow:

MLflow is an open-source platform for managing and tracking machine learning experiments. It includes tools for model training, tracking metrics, and visualizing results.

TensorFlow.js:

TensorFlow.js is a JavaScript library for building and training machine learning models in the browser or on Node.js.

FastAI:

FastAI is an open-source deep learning library developed by fast.ai. It includes a range of pre-built models and tools for data preprocessing, model training, and interpretation.

Neural Designer:

Neural Designer is a software for data analysis and predictive modeling. It includes a range of machine learning algorithms and tools for data preprocessing, feature selection, and model evaluation.

IBM Watson Assistant:

IBM Watson Assistant is a platform for building conversational interfaces using natural language processing (NLP) and machine learning.

OpenAI Gym:

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It includes a range of environments and tools for training and testing reinforcement learning models.