Artificial Intelligence (AI) Patterns, Neurons and Neural Networks

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Data Wrangling Patterns Feature Encoding Feature Imputation Feature Scaling Feature Encoding Dimensionality Reduction Supervised Learning Patterns Supervised Network Configuration Image Recognition Sequence Identification Unsupervised Learning Patterns Pattern Identification Content Filtering Model Evaluation Patterns Training Performance Evaluation Prediction Performance Evaluation Baseline Modeling Model Optimization Patterns Overfitting Avoidance Frequent Model Retraining Transfer Learning Fundamental Neural Network Architectures Perceptron (P) Feedforward (FF) Radial Basis Forward (RBF) Deep Feedforward (DFF) Architectures with Encoding and Decoding Layers AutoEncoder (AE) Sparse AutoEncoder (SAE) Variational AutoEncoder (VAE) Denoising AutoEncoder (DAE) Recurrent Cell-based Architectures Recurrent Neural Networks (RNNs) Echo State Network (ESN) Memory-Influenced Architectures Long/Short Term Memory (LSTM) Neural Turing Machine (NTM) Parabolicity Cell-Driven Architectures Boltzmann Machine (BM) Restricted Boltzmann Machine (RBM) Deep Belief Network (DBN) Markov Chain (MC) Pool and Kernel Influenced Architectures Deep Convolutional Network (DCN) Deconvolutional Network (DN) Deep Convolutional Inverse Graphics Network (DCIGN) Hidden Cell Connection Architectures Extreme Learning Machine (ELM) Deep Residual Network (DRN) Support Machine Vector (SVM) Kohonen Network (KN) Other Neural Network Architectures Hopfield Network Generative Adversarial Network (GAN) Liquid State Machine (LSM) Neuron Cell Types Backfed Input Neuron Convolution Kernel/Pool Neuron Different Memory Neuron Hidden Neuron Input Neuron Kernel Neuron Match Input/Output Neuron Memory Neuron Noisy Input Neuron Output Neuron Probabilistic Hidden Neuron Recurrent Memory Neuron Recurrent Neuron Spiking Hidden Neuron