Anubis - Resources

Deep Learning Frameworks

 

  • PyTorch – A popular open-source machine learning library used for developing deep learning models.
  • TensorFlow – A powerful open-source software library for dataflow and differentiable programming across a range of tasks.
  • Keras – A high-level neural networks API, written in Python and capable of running on top of TensorFlow, Theano, or CNTK.
  • Apache MXNet – An open-source deep learning software framework that allows developers to build and train neural networks with a variety of programming languages.
  • Caffe2 – A lightweight, modular, and scalable deep learning framework that is primarily used for computer vision.
  • Theano – A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
  • DeepLearning4J – A commercial-grade, open-source, distributed deep-learning library written for Java and Scala.
  • Chainer – An open-source neural network framework for deep learning that is designed to be intuitive, flexible, and performant.
  • Arm NN – An open-source software library that provides developers with a set of tools for building and deploying neural networks on Arm-based platforms.
  • Neonious AI – A small and powerful deep learning hardware platform for edge computing that can be programmed using JavaScript and TensorFlow.js.

 

Machine Learning Frameworks

 

  • scikit-learn – An open-source machine learning library for the Python programming language that is designed to work with numerical and scientific libraries like NumPy and SciPy.
  • TensorFlow Extended (TFX) – A platform for building and deploying production-grade machine learning pipelines.
  • Databricks AutoML – A fully automated machine learning service that enables users to quickly and easily build, evaluate, and deploy models.
  • Azure Machine Learning – A cloud-based machine learning service that enables users to build, train, and deploy machine learning models.
  • Amazon SageMaker – A fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at scale.

 

Natural Language Processing (NLP) Frameworks

 

  • spaCy – An open-source NLP library designed to help developers build natural language processing systems.
  • Natural Language Toolkit (NLTK) – A platform for building Python programs to work with human language data.
  • Hugging Face – An open-source platform for building and sharing models for natural language processing.
  • gensim – A Python library for topic modelling, document indexing, and similarity retrieval with large corpora.
  • StanfordNLP – A Python NLP library that provides a wide range of tools for building applications that can understand human language.
  • Apache OpenNLP – An open-source Java library for natural language processing that provides tools for sentence detection, tokenization, part-of-speech tagging, and more.
  • Google Cloud Natural Language – A cloud-based natural language processing service that provides pre-trained models for sentiment analysis, entity recognition, and more.
  • IBM Watson Natural Language Processing – A cloud-based service that provides natural language processing capabilities for building conversational interfaces, extracting insights from unstructured data, and more.
  • Amazon Comprehend – A natural language processing service that uses machine learning to find insights and relationships in text.
  • Stanford CoreNLP – A suite of Java tools for working with human language data that can be used to analyze text for sentiment, named entity recognition, and more.

 

Computer Vision Frameworks

 

  • OpenCV – An open-source computer vision library that provides tools for image and video processing, object detection, and more.
  • PyImageSearch – A blog and online community for developers working with computer vision and deep learning.
  • NVIDIA CUDA-accelerated OpenCV – An optimized version of the OpenCV library that leverages the power of NVIDIA GPUs to provide faster performance for computer vision applications.
  • TensorFlow Hub – A repository of pre-trained models for computer vision, natural language processing, and more.
  • PyTorch Lightning – A lightweight PyTorch wrapper for high-performance AI research that provides a simple interface for building and training models.

 

AI Applications and Use Cases

 

  • IBM Watson Health – A set of tools for developers and researchers to build and deploy health-related AI applications.
  • Microsoft Project Bonsai – A platform for building and deploying autonomous systems using machine teaching.
  • DeepMind for Google – A set of tools and services that enable Google to create intelligent products and services by integrating DeepMind’s AI technology.
  • OpenAI – An AI research organization that works on advancing artificial intelligence in a safe and beneficial way.
  • DeepMind – A UK-based AI research company that is focused on developing systems that can learn from data and apply what they have learned to new situations.
  • Automated Machine Learning (AutoML) – A subfield of AI that focuses on automating the machine learning process to make it more accessible to non-experts.
  • AI in Business – An article that provides examples of how AI is being used in various industries, such as healthcare, finance, and retail.
  • Real-World Use Cases of AI in Business – An article that provides real-world examples of how AI is being used to improve business operations and customer experiences.
  • AI in Healthcare – A resource that explores how AI is being used in the healthcare industry to improve patient outcomes and reduce costs.
  • AI in Business Operations – An article that explores how AI is being used to streamline business operations and reduce costs.
  • AI in Banking – A resource that explores how AI is being used in the banking industry to improve customer experiences and reduce fraud.
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