A Beginner’s Guide to Important Topics in AI, Machine Learning, and Deep Learning.

• Accuracy, Precision, Recall, and F1
• AI Infrastructure
• AI vs. ML vs. DL
• AI Winter
• Attention, Memory Networks & Transformers
• Automated Machine Learning & AI
• Backpropagation
• Bag of Words & TF-IDF
• Bayes’ Theorem & Naive Bayes Classifiers
• Climate Change & AI’s Impact
• Comparison of AI Frameworks
• Convolutional Neural Network (CNN)
• Data for Deep Learning
• Datasets and Machine Learning
• Decision Intelligence and Machine Learning
• Decision Tree
• Deep Autoencoders
• Deep-Belief Networks
• Deep Reinforcement Learning
• Deep Learning Resources
• Define Artificial Intelligence (AI)
• Denoising Autoencoders
• Differentiable Programming
• Eigenvectors, Eigenvalues, PCA, Covariance and Entropy
• Evolutionary & Genetic Algorithms
• Fraud and Anomaly Detection
• Gaussian Processes & Machine Learning
• Generative Adversarial Network (GAN)
• AI and Machine Learning Glossary
• Graph Analytics
• Hopfield Networks
• Industrial Operations and AI
• Java Tooling for AI
• Java for Data Science
• Logistic Regression
• LSTMs & RNNs
• Machine Learning Algorithms
• Machine Learning Demos
• Machine Learning Research Groups & Labs
• Machine Learning Workflows
• Machine Learning definition
• Markov Chain Monte Carlo
• MNIST database
• Multilayer Perceptron
• Natural Language Processing (NLP)
• Neural Network Tuning
• Neural Networks & Deep Learning
• Open Datasets
• Operations Research Optimization
• Python Tooling for AI
• Questions When Applying Deep Learning
• Radial Basis Function Networks
• Random Forest
• Recurrent Network (RNN)
• Recursive Neural Tensor Network
• Reinforcement Learning Definitions
• Restricted Boltzmann Machine (RBM)
• Robotic Process Automation (RPA) & AI
• Scala Tooling for AI
• Simulation, AI and Optimization
• Spiking Neural Networks
• Strong AI vs. Weak AI
• Supervised Learning
• Supply Chains, AI and Machine Learning
• Symbolic Reasoning
• Thought Vectors
• Unsupervised Learning
• Deep Learning Use Cases
• Variational Autoencoder (VAE)
• Word2Vec, Doc2Vec and Neural Word Embeddings

The Artificial Intelligence Wiki

Pathmind’s artificial intelligence wiki is a beginner’s guide to important topics in AI, machine learning, and deep learning. The goal is to give readers an intuition for how powerful new algorithms work and how they are used, along with code examples where possible.

Advances in the field of machine learning (algorithms that adjust themselves when exposed to data) are driving progress more widely in AI. But many terms and concepts will seem incomprehensible to the intelligent outsider, the beginner, and even the former student of AI returning to a transformed discipline after years away. We hope this wiki helps you better understand AI, the software used to build it, and what is at stake in its development.

The line between mathematics and philosophy is blurry when we talk about artificial intelligence, because with AI, we ask the mineral called silicon to perceive and to think – actions once thought exclusive to meat, and now possible with computation. We hope that by reading this wiki, you will find new ways of thinking about life and intelligence, just as we have by writing it.

Getting Started

You might start by reading our comparison of artificial Intelligence, machine learning and deep learning.

If you are curious about neural networks, reinforcement learning, LSTMs, convolutional networks (CNNs) or generative adversarial networks (GANs), we have devoted introductory posts to those popular algorithms, as well as more widely applicable mathematical concepts like eigenvectors and Markov Chains.

For industry-focused topics, see the Wiki pages on robotic process automation (RPA) and digital twins.

Machine Learning Glossary

As you read the articles, please refer to our AI glossary for definitions of many of the terms used in artificial intelligence and machine