Artisan AI - WordPress WIX GoDaddy Widgets

---

Thank you to all the explorers and inventors and technology

Google:

TensorFlow: An open-source machine learning framework for building and deploying various AI models.

PyTorch: A popular open-source machine learning library favored for its dynamic computation graphs and natural language processing capabilities.

Keras: A user-friendly API for building and experimenting with neural networks, often used as a frontend for TensorFlow.

Scikit-learn: A widely used Python library for classical machine learning algorithms, offering simple and efficient tools for data mining and analysis.

Caffe: A deep learning framework known for its speed and effectiveness in image recognition tasks.

Microsoft Cognitive Toolkit (CNTK): An open-source deep learning framework focusing on performance, scalability, and flexibility.

Apache MXNet: An open-source deep learning framework known for its scalability and distributed computing capabilities.

Theano: A Python library for defining, optimizing, and evaluating mathematical expressions, especially useful for deep learning research.

OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms.

RapidMiner: An integrated data science platform facilitating building machine learning models without extensive coding knowledge.

H2O.ai: An open-source machine learning platform designed for enterprises, offering scalable machine learning and deep learning solutions.

IBM Watson Studio: IBM's cloud-based data science platform integrating various tools for data analysis, AI model development, and deployment.

Apache Spark MLlib: A scalable machine learning library built on top of Apache Spark, offering distributed algorithms for data processing and machine learning tasks.

NLTK (Natural Language Toolkit): A Python library for working with human language data, providing tools for tokenization, stemming, tagging, parsing, and more.

GPT (Generative Pre-trained Transformer): A family of language generation models known for their capabilities in natural language understanding and generation.

BERT (Bidirectional Encoder Representations from Transformers): A transformer-based language representation model excelling in understanding context in natural language processing tasks.

XGBoost: An efficient and scalable gradient boosting library used for supervised learning tasks, known for its performance in structured/tabular data problems.

fast.ai: A high-level deep learning library built on top of PyTorch, providing simplified APIs for training models and conducting cutting-edge research.

AutoML (Automated Machine Learning): Various platforms and libraries automate the process of building machine learning models.

AllenNLP: A natural language processing library built on PyTorch, specifically designed for research in deep learning-based NLP.

Stanford CoreNLP: A suite of NLP tools providing various language analysis capabilities.

Dlib: A C++ library used for machine learning, computer vision, and image processing tasks, known for its effectiveness in face recognition and object detection.

Julia: A programming language offering high performance for technical computing tasks, including machine learning and scientific computing.

PaddlePaddle: A deep learning platform developed by Baidu, offering tools and libraries for building and deploying machine learning models.

Microsoft:

Azure Machine Learning: Microsoft's cloud-based machine learning platform for building, training, and deploying machine learning models at scale.

Azure Cognitive Services: A suite of AI services providing pre-built APIs for vision, speech, language, and decision-making capabilities.

Azure Databricks: A unified analytics platform that integrates with Azure to accelerate big data analytics and AI tasks.

Microsoft Cognitive Toolkit (CNTK): An open-source deep learning framework developed by Microsoft, known for its scalability and performance.

Microsoft Bot Framework: A platform for building, deploying, and managing intelligent bots across various channels.

Azure Custom Vision: Allows users to build and deploy custom image recognition models using machine learning.

Azure Speech Services: Provides speech-to-text and text-to-speech capabilities, enabling developers to integrate speech into applications.

Azure Translator Text API: Offers text translation capabilities between languages using neural machine translation technology.

Azure Form Recognizer: A service that extracts information from forms and documents using AI-powered machine learning models.

Microsoft Azure Face API: Enables face detection, recognition, and identification in images and videos.

Azure Language Understanding (LUIS): Helps developers build natural language understanding into applications for intent recognition and entity extraction.

Microsoft AI School: Offers online courses, tutorials, and resources for learning about Microsoft's AI technologies and tools.

Microsoft Research AI: Microsoft's research division focused on advancing the field

Other Companies:

IBM Watson: IBM's AI platform offering various services for natural language understanding, speech recognition, and machine learning.

Amazon Web Services (AWS) AI: Provides AI and machine learning services on the AWS cloud, including SageMaker for building ML models.

NVIDIA Deep Learning Institute (DLI): Offers training and certification in AI, deep learning, and accelerated computing.

PyTorch: An open-source machine learning library developed by Facebook's AI Research lab, known for its flexibility and ease of use.

Apple Core ML: Apple's framework for integrating machine learning models into iOS, macOS, watchOS, and tvOS apps.

OpenAI: A research organization focused on developing artificial general intelligence, known for projects like GPT (Generative Pre-trained Transformer) models.

Fast.ai: Offers practical deep learning for coders, providing free courses and libraries built on PyTorch.

Salesforce Einstein: Salesforce's AI platform embedded in its CRM software, offering AI-driven insights and predictions.

Alibaba Cloud AI: Alibaba's cloud services with AI capabilities, including natural language processing, computer vision, and machine learning.

Baidu AI Cloud: Baidu's AI services and solutions, covering speech recognition, image analysis, and natural language processing.

Huawei HiAI: Huawei's AI platform focused on integrating AI capabilities into their devices and cloud services.

Caffe: A deep learning framework developed by Berkeley Vision and Learning Center (BVLC), known for its expressive architecture.

Kaggle: A platform for data science competitions and collaboration, providing datasets, notebooks, and AI challenges.

TensorRT: NVIDIA's high-performance deep learning inference optimizer and runtime for deploying trained models.

H2O.ai: Provides AI and machine learning platforms for data science and analytics, including AutoML functionalities.

Intel AI: Intel's AI technologies and frameworks, including tools optimized for AI workloads on Intel hardware.

SAS AI & Analytics: Offers AI-powered analytics solutions for businesses, covering areas like fraud detection and customer intelligence.

Databricks: A unified analytics platform built on Apache Spark, facilitating big data analytics and AI tasks.

DeepMind: A subsidiary of Alphabet (Google's parent company) focused on artificial general intelligence research and reinforcement learning.

Theano: A Python library used for defining, optimizing, and evaluating mathematical expressions, especially useful for deep learning research.

Apache MXNet: An open-source deep learning framework used for training and deploying neural networks.

Orange: An open-source data visualization and analysis tool with machine learning and AI components.

RapidMiner: An integrated data science platform offering machine learning, data preparation, and model deployment functionalities.

BigML: Provides a machine learning platform for predictive analytics and machine learning automation.

DataRobot: An automated machine learning platform designed to assist in building and deploying machine learning models.

Additional Resources:

OpenAI GPT-3: A language model based on transformers, utilizing 175 billion parameters for natural language processing tasks with extensive use in language generation and understanding.

DeepMind AlphaFold: An AI system utilizing deep learning and attention mechanisms to predict protein structure from amino acid sequences, advancing protein folding predictions in bioinformatics.

Facebook AI Research (FAIR): Facebook's research division focused on AI, employing convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for computer vision, natural language processing, and reinforcement learning.

Google Brain: Google's AI research division employing deep neural networks (DNNs), recurrent networks, and attention mechanisms for various AI applications across Google services.

AI Dungeon: An AI-generated text adventure game using language models like GPT-3 to generate interactive narratives based on user inputs.

Generative Adversarial Networks (GANs): A class of neural networks comprising a generator and a discriminator, used for unsupervised learning and generating realistic synthetic data.

NeuroSymbolic AI: A field combining neural networks with symbolic reasoning techniques, aiming to integrate neural networks' pattern recognition with logic-based reasoning systems.

Evolutionary Algorithms: Optimization algorithms inspired by biological evolution, using techniques like genetic algorithms and genetic programming for machine learning tasks.

Quantum Machine Learning: Exploring quantum computing principles like quantum gates and superposition for solving machine learning problems, potentially achieving faster computations for certain tasks.

Reinforcement Learning: A machine learning paradigm focused on learning to make sequences of decisions by interacting with an environment, utilizing methods like Q-learning and policy gradients.

Explainable AI (XAI): Research focused on interpretable models employing XAI to market

Tools and Resources:

IBM AI Explainability 360: A comprehensive open-source toolkit providing various explainability algorithms for machine learning models.

SHAP (SHapley Additive exPlanations): A model-agnostic approach for explaining individual predictions of machine learning models.

LIME (Local Interpretable Model-agnostic Explanations): Provides explanations for individual model predictions locally around the prediction to be explained.

DeepLIFT: A method for understanding the contributions of different input features to a specific output prediction.

Anchors: Identifies minimal subsets of features that are sufficient to explain model predictions.

Counterfactual Explanations: Explains model predictions by generating alternative scenarios where the prediction would have been different.

Model Cards: Document model capabilities, limitations, and biases, providing transparency and understanding of model behavior.

Fairness Tooling: Tools for assessing and mitigating potential biases in machine learning models, including fairness metrics and bias detection algorithms.

InterpretML: A Python library for interpreting black-box models using various explainability techniques.

Captum: A PyTorch library for gradient-based explainability methods, offering insights into model predictions.