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__NOTOC__
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{{#seo:
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|title=PRIMO.ai
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|titlemode=append
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|keywords=Game, design, ChatGPT, artificial, intelligence, machine, learning, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Gemini, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools 
  
== Overview ==
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<!-- Google tag (gtag.js) -->
* [[How do I leverage AI?]]
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<script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script>
* [[Courses]]
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<script>
* [[Reading Material]]
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  window.dataLayer = window.dataLayer || [];
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  function gtag(){dataLayer.push(arguments);}
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  gtag('js', new Date());
  
==== Background ====
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  gtag('config', 'G-4GCWLBVJ7T');
* [[What is AI?]]
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</script>
* [[History of AI]]
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}}
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On {{LOCALDAYNAME}} {{LOCALMONTHNAME}} {{LOCALDAY}}, {{LOCALYEAR}} PRIMO.ai has {{NUMBEROFPAGES}} pages
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<b>Primo.ai</b> provides links to articles and videos on Artificial intelligence (AI) concepts and techniques such as [[Generative AI]], [[Natural Language Processing (NLP)]], [[Vision|Computer Vision]], [[Deep Learning]], [[Reinforcement Learning (RL)]], and [[Quantum|Quantum Technology]] -- providing [[Perspective|perspectives]] for individuals who are passionate about learning and developing new skills.   
 +
 
 +
= Getting Started =
 +
* [[How do I leverage Artificial Intelligence (AI)?]]
 +
* [[What is Artificial Intelligence (AI)?]]
 +
** [[History of Artificial Intelligence (AI)]]
 +
** [[Courses & Certifications]]
 +
** [[Reading Material & Glossary]]
 +
** [[Podcasts]]
 
* [[Current State]]
 
* [[Current State]]
 +
* [[Life~Meaning#Can_Meaning_Exist_in_Artificial_Systems|Can ''Meaning'' Exist in Artificial Systems?]] ... Explore the condition that separates simulation from ''Meaning''
 +
 +
=== AI Fun ===
 +
* Try [[ChatGPT]] | [[OpenAI]]
 +
* Create your own [[music]] with [https://www.udio.com/ Udio]
 +
* Try [[Stability_AI#DreamStudio | DreamStudio]] | Stability AI ... text-to-image [[Diffusion|diffusion]] model capable of generating photo-realistic images
 +
* [https://experiments.withgoogle.com/collection/ai Google AI Experiments]
 +
* [https://playground.tensorflow.org TensorFlow Playground] [[TensorFlow Playground|...learn more]]
 +
* [https://js.tensorflow.org/ TensorFlow.js Demos]
 +
* [[Google AIY Projects Program]]  - Do-it-yourself artificial intelligence
 +
* [https://www.nvidia.com/en-us/research/ai-playground/ NVIDIA Playground]
 +
* [[Competitions]]
 +
* [https://colab.research.google.com/github/nickwalton/AIDungeon/blob/master/AIDungeon_2.ipynb AI Dungeon 2] AI generated text adventure  ... [[Gaming]] ... [[Game Design | Design]]
  
==== AI Breakthroughs ====
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<i>.. more [[Natural Language Processing (NLP)]] fun...</i>
* [[Case Studies]]
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* [https://corenlp.run/ CoreNLP - see NLP parsing techniques by pasting your text | Stanford]
* [[Capabilities]]
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* [https://nlp.stanford.edu:8080/sentiment/rntnDemo.html Sentiment Treebank Analysis Demo]
* [http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=0&p=1&f=S&l=50&Query=%28%28abst%2F%28intelligence+and+%28artificial+or+machine%29%29%29+or+%28aclm%2F%28intelligence+and+%28artificial+or+machine%29%29%29%29+and++%28ISD%2F1%2F1%2F2014-%3E1%2F1%2F2050%29&d=PTXT AI Patents after 2013]
 
  
==== AI Fun ====
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=== How to... ===
*[http://experiments.withgoogle.com/collection/ai Google AI Experiments]
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* [[Strategy & Tactics]] for developing AI investments
*[http://playground.tensorflow.org TensorFlow Playground]
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* [[AI Solver]] for determining possible algorithms for your needs
*[http://js.tensorflow.org/ TensorFlow.js Demos]
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* [[Evaluation]]  ... Prompts for assessing AI projects
 +
* [[Checklists]] for ensuring consistency and completeness
  
==== How to... ====
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=== Forward Thinking ===
*[[AI Solver]]
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* [[Moonshots]]  ... a project or goal that aims to achieve a major breakthrough in artificial intelligence that has the potential to transform society or address significant global challenges
*[[Strategy & Tactics]]
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* [[Artificial General Intelligence (AGI) to Singularity]] ... a hypothetical future event in which artificial intelligence (AI) surpasses human intelligence in a way that fundamentally changes human society and civilization
*[[Checklists]]
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* [https://www.uspto.gov/initiatives/artificial-intelligence Artificial Intelligence | United States Patent and Trademark Office] --> [https://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=0&p=1&f=S&l=50&Query=%28%28abst%2F%28intelligence+and+%28artificial+or+machine%29%29%29+or+%28aclm%2F%28intelligence+and+%28artificial+or+machine%29%29%29%29+and++%28ISD%2F1%2F1%2F2014-%3E1%2F1%2F2050%29&d=PTXT AI Patents after 2013]
 +
* [[Creatives]]   ... individuals who have significantly contributed to the development, advancement, or popularization of AI
 +
* [[Books, Radio & Movies - Exploring Possibilities]]
  
==== Forward Thinking ====
+
<hr>
* [[Moonshots]]
 
* [[Journey to Singularity]]
 
* [[Creatives]]
 
  
== Datasets & Information Analysis ==
+
= Information Analysis =
* [[Datasets]]
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* [[Context]] ... the next AI frontier
* [[Data Preprocessing & Feature Exploration]]
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* [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]]  
** [[Hyperparameters]]
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* [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
 +
* [[Natural Language Processing (NLP)#Managed Vocabularies |Managed Vocabularies]]
 +
* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
 
* [[Visualization]]
 
* [[Visualization]]
 +
* [[Analytics]] 
 +
* [[Algorithm Administration#Hyperparameter|Hyperparameter]]s
  
== Algorithms ==
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= <span id="Algorithms"></span>[[Algorithms]] =
*[[About Algorithms & Neural Network Models]]
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* [https://huggingface.co/models Models | Hugging Face] ... click on Sort: Trending
 +
* [[Algorithms]]; the engines of AI
 +
* [[Model Zoos]]
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* [[Graphical Tools for Modeling AI Components]]
  
=== [[Supervised]] ===
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== [[Generative AI| Generative AI (Gen AI)]] ==
Labeled (desired solution) data is fed into the algorithm. The training data set has inputs as well as the desired output. During the training session, the model will adjust its variables to map inputs to the corresponding output.
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The ability to generate new content or solutions, such as [[Writing/Publishing|writing]] or designing new products, using techniques such as [[Generative Adversarial Network (GAN)]] or neural [[Style Transfer|style transfer]].
  
* ...predict values 
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* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Grok]] | [https://x.ai/ xAI] ... [[Groq]] ... [[Ernie]] | [[Baidu]] ... [[DeepSeek]]
**[[Linear Regression]]
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** [[Prompt Engineering (PE)]] ...[[Prompt Engineering (PE)#PromptBase|PromptBase]] ... [[Prompt Injection Attack]]
**[[Bayesian Linear Regression]]
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** [[Generative AI for Business Analysis]]
**[[Support Vector Regression (SVR)]]
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* [[Large Language Model (LLM)#Multimodal|Multimodal Language Model]]s
**[[Ordinal Regression]]
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* [[Video/Image]]
**[[Poisson Regression]]
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* [[Synthesize Speech]]
**[[Tree-based...]]
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* [[Game Development with Generative AI]] ... [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]]
***[[Fast Forest Quantile Regression]]
 
***[[Decision Forest Regression]]
 
**[[Boosted Decision Tree Regression]]
 
**[[General Regression Neural Network (GRNN)]]
 
  
* Anomaly Detection [[...find outliers]]
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== Predict values - [[Regression]] ==
**[[One-class Support Vector Machine (SVM)]]
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Analyze large amounts of data and make predictions or recommendations based on that data.
**[[Principal Components Analysis (PCA)-based Anomaly Detection]]
 
  
* Classification [[...predict categories]]
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* [[Linear Regression]]
**[[Perceptron (P)]] ...and Multi-layer Perceptron (MLP)
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* [[Ridge Regression]]
***[[Feed Forward Neural Network (FF or FFNN)]]
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* [[Lasso Regression]]
***[[Artificial Neural Network (ANN)]]
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* [[Elastic Net Regression]]
***[[Deep Neural Network (DNN)]]
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* [[Bayes#Bayesian Linear Regression|Bayesian Linear Regression]]
**[[Support Vector Machine (SVM)]]
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* [[Bayes#Bayesian Deep Learning (BDL)|Bayesian Deep Learning (BDL)]]
**[[K-Nearest Neighbors (KNN)]]
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* [[Logistic Regression (LR)]]
**[[Logistic Regression]]
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* [[Support Vector Regression (SVR)]]
**[[Naive Bayes]]
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* [[Ordinal Regression]]
**[[Tree-based...]]
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* [[Poisson Regression]]
***[[Boosted Decision Tree]]
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* Tree-based...
***[[Random Forest (or) Random Decision Forest]]
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** [[Fast Forest Quantile Regression]]
***[[Decision Jungle]]
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** [[Decision Forest Regression]]
 +
* [[General Regression Neural Network (GRNN)]]
 +
* [[One-class Support Vector Machine (SVM)]]
 +
* [[Gradient Boosting Machine (GBM)]]
  
* Other
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== [[Classification]] [[...predict categories]] ==
**[[Hopfield Network (HN)]]
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* <span id="Supervised"></span>[[Supervised]]
**[[Energy-based Model (EBN)]]
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** Naive [[Bayes]]
**[[Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)]]
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** [[K-Nearest Neighbors (KNN)]]
 +
** [[Perceptron (P)]] ...and Multi-layer Perceptron (MLP)
 +
** [[Feed Forward Neural Network (FF or FFNN)]]
 +
** [[Neural Network]]
 +
*** [[Deep Learning]] - [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]]
 +
** Kernel Approximation - [[Kernel Trick]]
 +
*** [[Support Vector Machine (SVM)]]
 +
** [[Logistic Regression (LR)]]
 +
*** [[Softmax]] Regression; Multinominal Logistic Regression
 +
** Tree-based...
 +
*** [[(Boosted) Decision Tree]]
 +
*** [[Random Forest (or) Random Decision Forest]]
 +
*** [[Decision Jungle]]
 +
** [[Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis]]
 +
** [[Markov Model (Chain, Discrete Time, Continuous Time, Hidden)]]
 +
* <span id="Unsupervised"></span>[[Unsupervised]]
 +
** [[Radial Basis Function Network (RBFN)]]
 +
** <span id="Self-Supervised"></span>[[Self-Supervised]]
 +
*** [[Autoencoder (AE) / Encoder-Decoder]]
 +
*** [[(Stacked) Denoising Autoencoder (DAE)]]
 +
*** [[Sparse Autoencoder (SAE)]]
  
==== Convolutional; Image & Object Recognition ====
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== [[Recommendation]] ==
*[[(Deep) Convolutional Neural Network (DCNN/CNN)]]
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* [[Alternating Least Squares (ALS)]]
*[[(Deep) Residual Network (DRN) - ResNet]]
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* [[Matrix Factorization]]
**[[ResNet-50]]
 
  
==== Deonvolutional ====
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== [[Clustering]] - Continuous - [[Dimensional Reduction]] ==
*[[Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)]]
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* [[Singular Value Decomposition (SVD)]]
 +
* [[Principal Component Analysis (PCA)]]
 +
* [[K-Means]]
 +
* [[Fuzzy C-Means (FCM)]]
 +
* [[K-Modes]]
 +
* [[Association Rule Learning]]
 +
* [[Mean-Shift Clustering]]
 +
* [[Density-Based Spatial Clustering of Applications with Noise (DBSCAN)]]
 +
* [[Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)]]
 +
* [[Restricted Boltzmann Machine (RBM)]]
 +
* [[Variational Autoencoder (VAE)]]
 +
* [[Biclustering]]
 +
* [https://en.wikipedia.org/wiki/Multidimensional_scaling Multidimensional Scaling (MDS)]
  
==== Sequence ====
+
=== [[Hierarchical]] ===
*[[Attention Model]]
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* [[Hierarchical Cluster Analysis (HCA)]]
*[[Sequence to Sequence (Seq2Seq)]]
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* [[Hierarchical Clustering;  Agglomerative (HAC) & Divisive (HDC)]]
*[[Neural Turing Machine]]
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* [[Hierarchical Temporal Memory (HTM)]] Time
 +
* [[Mixture Models; Gaussian]]
  
* Speech Recognition & Text Processing; at character level - time-series analysis
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=== Convolutional ===
**[[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]]
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* [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
 +
* [[(Deep) Residual Network (DRN) - ResNet]]
 +
** [[ResNet-50]]
  
* Object Recognition & Text Processing 
+
=== Deconvolutional ===
**[[(Tree) Recursive Neural (Tensor) Network (RNTN)]]
+
*[[Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)]]
  
=== [[Unsupervised]] Generative Front-end, [[Supervised]] Classification; Image Recognition Back-end ===
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== Graph ==
* [[Deep Belief Network (DBN)]]
+
- includes social networks, sensor networks, the entire Internet, 3D Objects ([[Point Cloud]])
 +
* [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
 +
* [[Point Cloud]]  
 +
* [https://techxplore.com/news/2019-04-hierarchical-rnn-based-scene-graphs-images.html A hierarchical RNN-based model to predict scene graphs for images]
 +
* [https://techxplore.com/news/2019-01-multi-granularity-framework-social-recognition.html A multi-granularity reasoning framework for social relation recognition]
 +
* [[Neural Structured Learning (NSL)]]
  
=== [[Unsupervised]] ===
+
== [[Time#Sequence/Time-based Algorithms|Sequence/Time-based Algorithms]] ==
There is not a target outcome. The algorithms will cluster the data set for different groups. Some uses of Unsupervised Learning are (1) data compression, (2) classification, (3) clustering, and (4) outlier detection
+
* [[Mamba]]
  
==== Unsupervised: Probabilistic/Generative ====
+
== Competitive  ==
A probability distribution over a set of classes for each input sample. Unlabeled data is classified as (1) conditional probability of the target Y, or (2) conditional probability of the observable X given a target Y
+
* [[Generative Adversarial Network (GAN)]]
 +
* [[Image-to-Image Translation]]
 +
* [[Conditional Adversarial Architecture (CAA)]]
 +
* [[Kohonen Network (KN)/Self Organizing Maps (SOM)]]
 +
* [[Quantum Generative Adversarial Learning (QuGAN - QGAN)]]
  
* Classification
+
== <span id="Semi-Supervised"></span>[[Semi-Supervised]] ==
**[[Radial Basis Function Network (RBFN)]]
+
In many practical situations, the cost to label is quite high, since it requires skilled human experts to do that. So, in the absence of labels in the majority of the observations but present in few, semi-supervised algorithms are the best candidates for the model building. These methods exploit the idea that even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters. Reference: [[Learning Techniques]]
 +
* [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]]
 +
* [[Context-Conditional Generative Adversarial Network (CC-GAN)]]
  
* Clustering  
+
== <span id="Natural Language"></span>Natural Language ==
**[[Restricted Boltzmann Machine (RBM)]]
 
**[[Variational Autoencoder (VAE)]]
 
**[[K-Means]]
 
**[[Mean-Shift Clustering]]
 
**[[Density-Based Spatial Clustering of Applications with Noise (DBSCAN)]]
 
**[[Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)]]
 
  
* Hierarchical; to include clustering
+
* [[Natural Language Processing (NLP)]] involves speech recognition, (speech) translation, understanding (semantic parsing) complete sentences, understanding synonyms of matching words, and sentiment analysis
**[[Hierarchical Cluster Analysis (HCA)]]
+
** [[Natural Language Generation (NLG)]]  
**[[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]]
+
** [[Natural Language Classification (NLC)]]  
**[[Hierarchical Temporal Memory (HTM)]]
+
** [[Large Language Model (LLM)]] 
 +
** [[Natural Language Tools & Services]]
 +
*** [[Embedding]]
 +
*** [[Fine-tuning]]
 +
*** [[Agents#AI-Powered Search|Search]] (where results are ranked by relevance to a query string)
 +
*** [[Clustering]] (where text strings are grouped by similarity)
 +
*** [[Recommendation]]s (where items with related text strings are recommended)
 +
*** [[Anomaly Detection]] (where outliers with little relatedness are identified)
 +
*** [[Classification]] (where text strings are classified by their most similar label)
 +
*** [[Dimensional Reduction]]
 +
*** [[...find outliers]] ... diversity measurement (where similarity distributions are analyzed)
  
==== Competitive ====
+
== <span id="Reinforcement Learning (RL)"></span>[[Reinforcement Learning (RL)]] ==
*[[Generative Adversarial Network (GAN)]]
+
an algorithm receives a delayed reward in the next time step to evaluate its previous action. Therefore based on those decisions, the algorithm will train itself based on the success/error of output. In combination with Neural Networks it is capable of solving more complex tasks. [[Policy Gradient (PG)]] methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by [[Gradient Descent Optimization & Challenges |gradient descent]].
*[[Kohonen Network (KN)/Self Organizing Maps (SOM)]]
 
  
==== Unsupervised: Non-Probabilistic; e.g. Deterministic  ====
+
* [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning
Unlabeled data is fed into the algorithm with the algorithm seperating the feature space and return the class associated with the space where a sample originates from.
+
* [[Markov Decision Process (MDP)]]
*[[Autoencoder (AE) / Encoder-Decoder]]
+
* [[State-Action-Reward-State-Action (SARSA)]]
*[[(Stacked) Denoising Autoencoder (DAE)]]
+
* [[Q Learning]]
*[[Sparse Autoencoder (SAE)]]
+
** [[Deep Q Network (DQN)]]
 +
* [[Deep Reinforcement Learning (DRL)]] DeepRL
 +
* [[Distributed Deep Reinforcement Learning (DDRL)]]
 +
* [[Evolutionary Computation / Genetic Algorithms]]
 +
* [[Actor Critic]]
 +
** [[Asynchronous Advantage Actor Critic (A3C)]]
 +
** [[Advanced Actor Critic (A2C)]]
 +
** [[Lifelong Latent Actor-Critic (LILAC)]]
 +
* [[Hierarchical Reinforcement Learning (HRL)]]
 +
* [[Reinforcement Learning (RL) from Human Feedback (RLHF)]]
  
=== [[Semi-Supervised]] ===
+
== [[Neuro-Symbolic]] ==
* [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]]
+
the “connectionists” seek to construct artificial [[Neural Network]]s, inspired by biology, to learn about the world, while the “symbolists” seek to build intelligent machines by coding in logical rules and representations of the world. Neuro-Symbolic combines the fruits of group.
* [[Context-Conditional Generative Adversarial Network (CC-GAN)]]
 
  
=== [[Reinforcement (RL)]] ===
+
* [[Neuro-Symbolic]] ... [[Symbolic Artificial Intelligence]]
An 'agent' algorithm receives a delayed reward in the next time step to evaluate its previous action. Therefore based on those decisions, the algorithm will train itself based on the success/error of output. In combination with Neural Networks it is capable of solving more complex tasks.
+
* [[Neuro-Symbolic Concept Learner (NS-CL)]]
* [[Markov Decision Process (MDP)]]
 
* [[Deep Reinforcement Learning (DRL)]]
 
* [[Deep Q Learning (DQN)]]
 
* [[Neural Coreference]]
 
* [[State-Action-Reward-State-Action (SARSA)]]
 
* [[Deep Deterministic Policy Gradient (DDPG)]]
 
* [[Trust Region Policy Optimization (TRPO)]]
 
* [[Proximal Policy Optimization (PPO)]]
 
  
==== Generative ====
+
== Other ==
 +
* [[Hopfield Network (HN)]]
 +
* [[Energy-based Model (EBN)]] ... non-normalized probabilistic model
 
* [[Generative Query Network (GQN)]]
 
* [[Generative Query Network (GQN)]]
  
== Libraries & Frameworks ==
+
= Techniques =
* [[Libraries & Frameworks Overview]]
+
* [[Math for Intelligence]] ... [[Finding Paul Revere]]
* [[Libraries & Frameworks other than TensorFlow]]
+
* [https://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research
==== TensorFlow ====
+
* [[Theory-free Science]]
* [[TensorFlow Overview & Tutorials]]
 
* [[TensorBoard]]
 
* [[TensorFlow.js]] 
 
* [http://playground.tensorflow.org TensorFlow Playground]
 
* [[TensorFlow Lite]]
 
* [[TensorFlow Serving]]
 
* Related...
 
** [[Keras]]
 
** [[TFLearn]]
 
** [[Swift]]
 
 
 
== Techniques ==
 
==== Mathematical Background ====
 
* [[Math for Intelligence]]
 
  
==== Algorithms ====
+
=== Methods & Concepts ===
 
* [[Backpropagation]]
 
* [[Backpropagation]]
* [[Gradient Boosting Algorithms]]
+
* [[Gradient Descent Optimization & Challenges|Stochastic Gradient Descent]]
* [[Repositories & Other Algorithms]]
+
* [[Gradient Descent Optimization & Challenges#Learning Rate Decay|Learning Rate Decay]]
* [[Dimensional Reduction Algorithms]]
+
* [[Pooling / Sub-sampling: Max, Mean|Max Pooling]]
** [[Pooling]]
+
* [[Data Quality#Batch Norm(alization) & Standardization|Batch Normalization]]
 
+
* [[Overfitting Challenge]]
==== Bag of Tricks ====
+
** [[Regularization]]
 +
** [[Dropout]]
 +
* [[Manifold Hypothesis]] and [[Dimensional Reduction]]; identification - what influences an observed outcome
 
* [[Activation Functions]]
 
* [[Activation Functions]]
 +
* [[Memory]]
 +
** [[Memory Networks]]
 
* [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
 
* [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
* [[Object Detection; Faster R-CNN, YOLO, SSD]]
+
* [[Optimizer]]s
* [[Optimizers]]
+
** [[Optimization Methods]]
 
** [[Objective vs. Cost vs. Loss vs. Error Function]]
 
** [[Objective vs. Cost vs. Loss vs. Error Function]]
** [[Backpropagation]]
 
 
** [[Gradient Descent Optimization & Challenges]]
 
** [[Gradient Descent Optimization & Challenges]]
 
** [[Parameter Initialization]]
 
** [[Parameter Initialization]]
** [http://www.kdnuggets.com/2018/04/right-metric-evaluating-machine-learning-models-1.html Choosing the Right Metric for Evaluating Machine Learning Models]
+
* [[Neural Network Pruning]]
** [[Approach to Bias and Variances]]
+
* [[Repositories & Other Algorithms]]
** [[Classification Performance]]
+
* [https://dawn.cs.stanford.edu/benchmark/index.html DAWNBench] An End-to-End Deep Learning Benchmark and Competition
* [[Transfer Learning]]
+
* [[Knowledge Graphs]]
* [[Competitions]]
+
* [[Quantization]]
 +
* [[Causation vs. Correlation]]
 +
* [[Deep Features]]
 +
* [[Local Features]]
 +
* [[Loop#Unintended Feedback Loop|Unintended Feedback Loop]]
 +
* [[Backtesting]]
 +
* [[Digital Twin]]
 +
 
 +
==== [[Policy]] ====
 +
* [[Policy]]  ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
 +
 
 +
=== <span id="Learning Techniques"></span>[[Learning Techniques]] ===
 +
* [[In-Context Learning (ICL)]] ... [[Context]]
 +
* [[Out-of-Distribution (OOD) Generalization]]
 +
* [[PRIMO.ai#Supervised|Supervised Learning]] ... [[PRIMO.ai#Semi-Supervised|Semi-Supervised Learning]] ... [[PRIMO.ai#Self-Supervised|Self-Supervised Learning]] ... [[PRIMO.ai#Unsupervised|Unsupervised Learning]]
 +
* [[PRIMO.ai#Reinforcement Learning (RL)|Reinforcement Learning (RL)]]
 +
* [[Reinforcement Learning (RL) from Human Feedback (RLHF)]]
 +
* [[Deep Learning]]
 +
* [[Transfer Learning]] a model trained on one task is re-purposed on a second related task
 +
** [[Text Transfer Learning]]
 +
** [[Image/Video Transfer Learning]]
 +
* [[Few Shot Learning]] ... [[Few Shot Learning#One-Shot Learning|One-Shot Learning]] ... [[Few Shot Learning#Zero-Shot Learning|Zero-Shot Learning]]
 +
* [[Ensemble Learning]]
 +
* [[Multi-Task Learning (MTL)]]
 +
* [[Apprenticeship Learning - Inverse Reinforcement Learning (IRL)]]
 +
* [[Imitation Learning (IL)]]
 +
* [[Lifelong Learning]] - Catastrophic Forgetting Challenge
 +
* [[Neural Structured Learning (NSL)]]
 +
* [[Meta-Learning]]
 +
* [[Online Learning]]
 +
* [[Human-in-the-Loop (HITL) Learning]] / Active Learning
 +
* [[Decentralized: Federated & Distributed]] Learning
 +
* [[Large Language Model (LLM)#Multimodal|Multimodal Machine Learning]]
 +
* [[Embodied AI| Action Learning ... Embodied AI]]
 +
* [[Simulated Environment Learning]]
 +
 
 +
=== Opportunities & Challenges ===
 +
* [[Generative AI]]
 +
* [[Inside Out - Curious Optimistic Reasoning]] 
 +
* Nature
 +
** [[Evolutionary Computation / Genetic Algorithms]]
 +
** [[Bio-inspired Computing]]
 +
* [[Connecting Brains]]
 +
** [[Molecular Artificial Intelligence (AI)]]
 +
** [[Neuroscience]]
 +
* [[Architectures]]
 +
** [[Deep Distributed Q Network Partial Observability]]
 +
** [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
 +
** [[Capsule Networks (CapNets)]]
 +
** [[Messaging & Routing]]
 +
** [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU]]
 +
* [[Integrity Forensics]]
 +
* [[Metaverse]]
 +
* [[Omniverse]]
 +
* [[Cybersecurity]]
 +
* [[Robotics]]
 +
* [[Other Challenges]] in Artificial Intelligence
 +
* [[Quantum]]
  
==== Coding ====
 
* [[Javascript]]
 
* [[Python]]
 
* [[Other Coding options]]
 
* [[Jupyter Notebooks]]
 
  
== Platforms: Machine Learning as a Service (MLaaS) ==
+
<hr>
 +
= <span id="Development & Implementation"></span>[[Development]] & Implementation =
 +
* [https://aitoptools.com/ Tool Assist | AI Top Tools] ... largest directory of AI Tools, Ranked with dynamic algorithms
 +
* [[Development]]
 +
** [[Project Management]]
 +
** [[Generative AI for Business Analysis]]
 +
** [[Diagrams for Business Analysis]]
 +
** [[Requirements Management]]
 +
** [[Risk, Compliance and Regulation]]
 +
** [[Evaluation]]
 +
*** [[Evaluation - Measures]]
 +
** [[Train, Validate, and Test]]
 +
* [[Building Your Environment]]
 +
* [[Algorithm Administration]]
 +
** [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]]
 +
* [[ChatGPT#Integration | ChatGPT Integration]]
 +
* [[Game Development with Generative AI]] ... [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]]
 +
* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 
* [[Service Capabilities]]
 
* [[Service Capabilities]]
* [[Other Platforms]]
+
* [[AI Marketplace & Toolkit/Model Interoperability]]
  
==== Amazon AWS ====
+
== No Coding ==
* [[AWS with TensorFlow]]
+
* [[Algorithm Administration#Automated Learning|Automated Learning]]
* [[DeepLens - deep learning enabled video camera]]
+
* [[Neural Architecture]] Search (NAS) Algorithm
** [[Getting Started & Project: Object Detection]]
+
* [[Codeless Options, Code Generators, Drag n' Drop]]
** [[More DeepLens Projects]]
 
* [[AWS Internet of Things (IoT)]]
 
** [[AWS IoT Button]]
 
* [[AmazonML]]
 
* [[Deep Learning (DL) Amazon Machine Image (AMI) - DLAMI]]
 
* [http://www.floydhub.com/ FloydHub - training and deploying your DL models]
 
* [http://aws.amazon.com/about-aws/events/monthlywebinarseries/on-demand/ On-Demand AWS Tech Talks]
 
* [http://aws.amazon.com/training/ AWS Training and Certification]
 
  
==== Google Cloud AI ====
+
== Coding ==
* [[Google Cloud AI With TensorFlow]]
+
* [[Development#AI Pair Programming Tools|AI Pair Programming Tools]]
* [http://colab.research.google.com/notebooks/welcome.ipynb Colaboratory] - Jupyter notebooks
+
* [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]]
* [http://codelabs.developers.google.com/ Google Developers Codelabs]
+
* [[R Project]]
* [http://experiments.withgoogle.com/collection/ai Google AI Experiments]
+
* [[Other Coding options]]
* [[ML Engine]]
 
* [[Prediction API]]
 
* [http://cloud.google.com/vision/ Cloud Vision API] - drag & drop picture on webpage
 
* [http://grow.google/ Grow with Google]  
 
* [http://ai.google/education/ Learn from ML experts at Google]
 
  
==== Kaggle ====
+
=== [[Libraries & Frameworks]] ===
* [[Kaggle Overview]]
+
* [[Libraries & Frameworks Overview]]
* [[Kaggle Kernels]]
 
* [[Kaggle Competitions]]
 
** [[Passenger Screening]]
 
* [http://www.kaggle.com/learn/overview Hands-On Data Science Education]
 
  
==== Microsoft Azure ====
+
==== [[TensorFlow]] ====
* [[Azure Process]]
+
* [[TensorBoard]]
* [[Azure with TensorFlow]]
+
* [[TensorFlow Playground]]
* [[ML Studio]]
+
* [https://js.tensorflow.org/ TensorFlow.js Demos]
* [[Cognitive Services]]
+
* [[TensorFlow.js]]
* [http://aischool.microsoft.com/learning-paths AI School]
+
* [[TensorFlow Lite]]
 +
* [[TensorFlow Serving]]
 +
* Related...
 +
** [[Keras.js]]
 +
** [[TFLearn]]
 +
** [[Swift]]
  
==== Intel ====
+
=== Tooling ===
* [[Neural Compute Stick (NCS)]]
+
* [[Model Search]]
* [http://software.intel.com/en-us/ai-academy AI Academy]
+
* [[Algorithm Administration#Model Monitoring|Model Monitoring]]
 +
* [[Notebooks]]; [[Jupyter]] and R Markdown
 +
 
 +
=== [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)]] ===
 +
* [[Amazon]] AWS 
 +
* [[Apple]]
 +
* [[Google]] Cloud Platform (GCP)
 +
* [[Hugging Face]]
 +
* [[IBM]]
 +
* [[Intel]]
 +
* [[Kaggle]]
 +
* [[Microsoft]] [[Azure AI Process|Azure Machine Learning]]
 +
* [https://modal.com/ Modal]
 +
* [[NVIDIA]]
 +
* [[OpenAI]]
 +
* [[Palantir]]
 +
* [[xAI]]
 +
 
 +
=== ... and other leading organizations ===
 +
* [[Meta]]
 +
* [[Sakana]]
 +
* [https://allenai.org/ Allen Institute for Artificial Intelligence, or AI2]
 +
* [[Government Services]]
 +
** [[National Institute of Standards and Technology (NIST)]]
 +
** [[U.S. Department of Homeland Security (DHS)]]
 +
** [[Defense]]
 +
* [https://ai.stanford.edu/ Stanford University], [https://www.csail.mit.edu/ MIT], [https://www2.eecs.berkeley.edu/Research/Areas/AI/ UC Berkeley], [https://ai.cs.cmu.edu/ Carnegie Mellon University], [https://aiml.cs.princeton.edu/ Princeton University], [https://www.cs.ox.ac.uk/research/ai_ml/ University of Oxford], [https://www.cs.utexas.edu/concentrations/mlai University of Texas Austin], [https://samueli.ucla.edu/big-data-artificial-intelligence-and-machine-learning/ UCLA], [https://www.cs.duke.edu/research/artificialintelligence Duke University], [https://www.epfl.ch/research/ EPFL], [https://digital.hbs.edu/topics/artificial-intelligence-machine-learning/ Harvard University], [https://www.cs.cornell.edu/research/ai Cornell University], [https://inf.ethz.ch/ ETH], [https://www.cs.tsinghua.edu.cn/publish/csen/4917/index.html Tsinghua University], [https://www.comp.nus.edu.sg/about/depts/cs/research/ai/ National University of Singapore], [https://priml.upenn.edu/ University of Pennsylvania], [https://www.technion.ac.il/en/technion-research-units-2/ Technion], [https://www.cs.washington.edu/research/ai University of Washington], [https://ai.ucsd.edu/ UC San Diego], [https://www.cs.umd.edu/researcharea/ai-and-robotics University of Maryland], [https://www.cil.pku.edu.cn/ Peking University], [https://ic.gatech.edu/content/artificial-intelligence-machine-learning Georgia Institute of Technology], [https://machinelearning.illinois.edu/ University of Illinois at Urbana-Champaign], [https://research.cs.wisc.edu/areas/ai/ University of Wisconsin Madison], [https://www.engineering.utoronto.ca/research-innovation/industry-partnerships-with-u-of-t-engineering/data-analytics-artificial-intelligence/ University of Toronto], [https://www.umontreal.ca/en/artificialintelligence/ Université de Montréal] - [https://mila.quebec/en/mila/ Mila], [https://www.kaist.ac.kr/en/html/research/04.html KAIST], [https://engineering.tamu.edu/cse/research/areas/artificial-intelligence.html Texas A&M University], [https://www.riken.jp/en/research/labs/aip/ RIKEN], [https://www.cl.cam.ac.uk/research/ai/ University of Cambridge], [https://www.cs.columbia.edu/areas/ai/ Columbia University], [https://www.cics.umass.edu/research/area/artificial-intelligence UMass Amherst], [https://www.inria.fr/en National Institute for Research in Digital Science and Technology (INRIA)], [https://engineering.nyu.edu/research-innovation/centers-and-institutes/ai-now New York University],  [https://www.ucl.ac.uk/ai-centre/ University College London], [https://www.cs.usc.edu/academic-programs/masters/artificial-intelligence/ University of Southern California], [https://cpsc.yale.edu/research/artificial-intelligence Yale University], [https://yandexdataschool.com/ Yandex], [https://en.sjtu.edu.cn/ Shanghai Jiao Tong University], [https://www.cs.umn.edu/research/research_areas/robotics-and-artificial-intelligence University of Minnesota], [https://voices.uchicago.edu/machinelearning/ University of Chicago], [https://www.mcgill.ca/desautels/category/tags/artificial-intellligence-ai McGill University], [https://cse.snu.ac.kr/en Seoul National University], [https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/studium/studiengaenge/machine-learning/ University of Tuebingen], [https://www.ualberta.ca/computing-science/research/research-areas/artificial-intelligence.html University of Alberta], [https://engineering.rice.edu/research-faculty/research-focus-areas/artificial-intelligence-machine-learning Rice University], [https://ep.jhu.edu/programs-and-courses/programs/artificial-intelligence Johns Hopkins University]
  
== Research & Development ==
 
* [[Explainable Artificial Intelligence (EAI) - “Inceptionism”]] 
 
* [[Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)]]
 
* [[Connecting Brains]]
 
* [[Cybersecurity]]
 
* [[Self Learning Artificial Intelligence - AutoML & World Models]] 
 
** [[Evolutionary Computation / Genetic Algorithms]]
 
** [[3D Simulation Environments]]
 
* [[Architectures]]
 
** [[Deep Distributed Q Network Partial Observability]] 
 
** [[Capsule Networks (CapNets)]]
 
** [[Inside Out - Curious Optimistic Reasoning]]
 
** [[Messaging & Routing]]
 
** [[Pipelines]]
 
** [[Differentiable Neural Computer (DNC)]] 
 
** [[Processing Units - CPU, GPU, TPU, VPU, FPGA, QPU]]
 
* [[Other Challenges]]
 
  
== Other ==
 
  
* [http://businesscentricmethodology.com/ Business-Centric Methodology]
+
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* [[Getting Started - MediaWiki]]
 

Latest revision as of 08:47, 11 February 2026

On Friday July 3, 2026 PRIMO.ai has 825 pages

Primo.ai provides links to articles and videos on Artificial intelligence (AI) concepts and techniques such as Generative AI, Natural Language Processing (NLP), Computer Vision, Deep Learning, Reinforcement Learning (RL), and Quantum Technology -- providing perspectives for individuals who are passionate about learning and developing new skills.

Getting Started

AI Fun

.. more Natural Language Processing (NLP) fun...

How to...

Forward Thinking


Information Analysis

Algorithms

Generative AI (Gen AI)

The ability to generate new content or solutions, such as writing or designing new products, using techniques such as Generative Adversarial Network (GAN) or neural style transfer.

Predict values - Regression

Analyze large amounts of data and make predictions or recommendations based on that data.

Classification ...predict categories

Recommendation

Clustering - Continuous - Dimensional Reduction

Hierarchical

Convolutional

Deconvolutional

Graph

- includes social networks, sensor networks, the entire Internet, 3D Objects (Point Cloud)

Sequence/Time-based Algorithms

Competitive

Semi-Supervised

In many practical situations, the cost to label is quite high, since it requires skilled human experts to do that. So, in the absence of labels in the majority of the observations but present in few, semi-supervised algorithms are the best candidates for the model building. These methods exploit the idea that even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters. Reference: Learning Techniques

Natural Language

Reinforcement Learning (RL)

an algorithm receives a delayed reward in the next time step to evaluate its previous action. Therefore based on those decisions, the algorithm will train itself based on the success/error of output. In combination with Neural Networks it is capable of solving more complex tasks. Policy Gradient (PG) methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent.

Neuro-Symbolic

the “connectionists” seek to construct artificial Neural Networks, inspired by biology, to learn about the world, while the “symbolists” seek to build intelligent machines by coding in logical rules and representations of the world. Neuro-Symbolic combines the fruits of group.

Other

Techniques

Methods & Concepts

Policy

Learning Techniques

Opportunities & Challenges



Development & Implementation

No Coding

Coding

Libraries & Frameworks

TensorFlow

Tooling

Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)

... and other leading organizations


If you get a 502 or 503 error please try the webpage again, as your message is visiting the island which the server is located, perhaps deciding to relax in the Sun before returning. Thank you.