Published By
Neha Firdous 
Digital Marketing Specialist at Wothus
Published at 15th June 2022
TensorFlow: TensorFlow is a powerful open-source software library for machine learning developed by Google Brain Team. It is designed to work with neural networks and offers a wide range of features for building and training deep learning models. TensorFlow has a large community of users and contributors, making it one of the most popular machine learning libraries available today.
PyTorch: PyTorch is another popular open-source machine learning library that provides an easy-to-use interface for building and training neural networks. It is known for its dynamic computation graph, which allows for easy debugging and flexibility in model development. PyTorch is widely used in academia and industry, and it has a growing community of users and contributors.
Keras: Keras is a high-level neural networks API written in Python and capable of running on top of TensorFlow, Theano, and CNTK. It provides a simple and intuitive interface for building and training neural networks, making it a popular choice for beginners and experts alike. Keras is known for its ease of use, flexibility, and ability to build complex models with few lines of code.
Scikit-learn: Scikit-learn is a free and open-source machine learning library for Python that provides a range of tools for data mining and data analysis. It includes various classification, regression, and clustering algorithms, as well as tools for data preprocessing, model selection, and evaluation. Scikit-learn is widely used in academia and industry, and it is known for its user-friendly API and ease of use.
OpenCV: OpenCV is an open-source computer vision library that provides tools for image and video processing, object detection, and face recognition. It includes various algorithms for feature detection, image filtering, and pattern recognition, making it a powerful tool for computer vision applications. OpenCV is widely used in research and industry, and it has a large community of users and contributors.
IBM Watson: IBM Watson is a suite of AI-powered tools and services that can be used for developing natural language processing (NLP) applications, chatbots, and recommendation systems. It includes various APIs and services for speech-to-text, text-to-speech, natural language understanding, and machine learning, making it a powerful tool for building intelligent applications.
Amazon SageMaker: Amazon SageMaker is a fully managed machine learning service that provides tools for building, training, and deploying machine learning models in the cloud. It includes various tools for data preparation, model training, and deployment, as well as built-in algorithms and frameworks for popular machine learning tasks. Amazon SageMaker is widely used in industry, and it is known for its scalability and ease of use.
Microsoft Cognitive Services: Microsoft Cognitive Services is a set of APIs and SDKs that can be used for developing intelligent applications that can see, hear, speak, understand, and interpret natural language. It includes various tools for speech recognition, text analysis, and computer vision, making it a powerful tool for building intelligent applications. Microsoft Cognitive Services is widely used in industry, and it is known for its ease of use and integration with other Microsoft products and services.
Caffe: A deep learning framework developed by Berkeley AI Research (BAIR) that is known for its speed and efficiency in training convolutional neural networks.
Hugging Face: An open-source library for natural language processing that provides state-of-the-art models and tools for text classification, question answering, and language generation.
Fast.ai: A practical deep learning library that provides an easy-to-use interface for building and training neural networks. It includes various pre-trained models and tools for data preparation and visualization.
Theano: An open-source numerical computation library that can be used for building and training deep learning models. It is known for its efficiency and speed in computing gradients, making it a popular choice for building complex models.
Torch: A scientific computing framework that provides tools for building and training neural networks. It is widely used in academia and industry, and it is known for its ease of use and flexibility.
IBM Watson Studio: A cloud-based platform for building, training, and deploying machine learning models. It includes various tools for data preparation, model training, and deployment, as well as a collaborative environment for data scientists and developers.
NVIDIA Deep Learning SDK: A collection of tools and libraries for building and deploying deep learning models on NVIDIA GPUs. It includes various optimized libraries for neural network training and inference, as well as tools for data processing and visualization.