[MXNLP-1-05] Word2Vec - [3]: Implementation of Skip-Gram models

[MXNLP-1-04] Word2Vec - [2]: Implementation of a CBOW model

[MXML-2-02] Decision Trees [2/11] - ID3/C4.5, Impurity, Gini index, Entropy, Information Gain (IG)

[MXML-2-01] Decision Trees [1/11] - Introduction to Decision Trees

[MXNLP-1-03] Word2Vec: CBOW and Skip-Gram - [1]

[MXNLP-1-02] Neural Probabilistic Language Model (NPLM) - [2]

[MXNLP-1-01] Neural Probabilistic Language Model (NPLM) - [1]

[MXDL-15-12] Self-Organizing Maps (SOMs)

[MXDL-15-11] Competitive Learning Model

[MXDL-15-10] Contrastive Hebbian Learning (CHL)

[MXDL-15-09] Restricted Boltzmann machines (RBMs) - (2)

[MXDL-15-08] Restricted Boltzmann machines (RBMs)

[MXDL-15-07] Boltzmann Machine

[MXDL-15-06] Hopfield Network (4) - Storkey learning rule for a Hopfield network

[MXDL-15-05] Hopfield Network (3) - Storage capacity and Pseudo-inverse learning rule

[MXDL-15-04] Hopfield Network (2) - Ising model and Energy function

[MXDL-15-03] Hopfield Network (1) - Storing and recalling information

[MXDL-15-02] Hebbian learning (2) - Weight decay

[MXDL-15-01] Hebbian learning (1)

[MXDL-14-11] GAN [11/11] - Dual Discriminator Generative Adversarial Networks (D2GAN)

[MXDL-14-10] GAN [10/11] - Wasserstein GAN with gradient penalty (WGAN-GP)

[MXML-1-07] K-Nearest Neighbors (KNN) [7/7] - KNN regression

[MXML-1-06] K-Nearest Neighbors (KNN) [6/7] - Inverse Occurrence Frequency (IOF) similarity

[MXML-1-05] K-Nearest Neighbors (KNN) [5/7] - Matching coefficient and Jaccard coefficient

[MXML-1-04] K-Nearest Neighbors (KNN) [4/7] - Weighted KNN (WKNN)

[MXML-1-03] K-Nearest Neighbors (KNN) [3/7] - The curse of dimensionality and lazy learner

[MXML-1-02] K-Nearest Neighbors (KNN) [2/7] - Optimal K value and decision boundary

[MXML-1-01] K-Nearest Neighbors (KNN) [1/7] - The basics of KNN classification algorithm

[MXDL-14-09] GAN [9/11] - Wasserstein GAN (2)

[MXDL-14-08] GAN [8/11] - Wasserstein GAN (1)

[MXDL-14-07] GAN [7/11] - Implementation of Least Squares GANs (LSGANs)

[MXDL-14-06] GAN [6/11] - Least Squares GAN (LSGAN)

[MXDL-14-05] GAN [5/11] - Implementation of unrolled GAN models

[MXDL-14-04] GAN [4/11] - Unrolled GAN

[MXDL-14-03] GAN [3/11] - Deep Convolutional GAN (DCGAN)

[MXDL-14-02] GAN [2/11] - Implementation of a standard GAN model

[MXDL-14-01] GAN [1/11] - The architecture of the standard GAN

[MXDL-13-06] Autoencoder [6/6] - Implementation of a Variational Autoencoder

[MXDL-13-05] Autoencoder [5/6] - Variational Autoencoder (VAE)

[MXDL-13-04] Autoencoder [4/6] - Anomaly Detection

[MXDL-13-03] Autoencoder [3/6] - Sparse autoencoder

[MXDL-13-02] Autoencoder [2/6] - Denoising, Deblurring Autoencoder

[MXDL-13-01] Autoencoder [1/6] - Dimensionality reduction

[MXDL-12-06] Convolutional Neural Networks (CNN) [6/6] - Convolutional LSTM

[MXDL-12-05] Convolutional Neural Networks (CNN) [5/6] - 3D Convolution

[MXDL-12-04] Convolutional Neural Networks (CNN) [4/6] - Residual Neural Network

[MXDL-12-03] Convolutional Neural Networks (CNN) [3/6] - 2D convolutional layer and 2D pooling layer

[MXDL-12-02] Convolutional Neural Networks (CNN) [2/6] - 1D convolutional layer and 1D pooling layer

[MXDL-12-01] Convolutional Neural Networks (CNN) [1/6] - The basics of CNN

[MXDL-11-07] Attention Networks [7/7] - Stock price prediction using a Transformer model

[MXDL-11-06] Attention Networks [6/7] - Time series forecasting using a Transformer model

[MXDL-11-05] Attention Networks [5/7] - Transformer model

[MXDL-11-04] Attention Networks [4/7] - Seq2Seq-Attention model using input-feeding method

[MXDL-11-03] Attention Networks [3/7] - Seq2Seq-Attention model for time series prediction

[MXDL-11-02] Attention Networks [2/7] - Implementing a Seq2Seq model for time series forecasting

[MXDL-11-01] Attention Networks [1/7] - Sequence-to-Sequence Networks (Seq2Seq)

[MXDL-10-08] Recurrent Neural Networks (RNN) [8/8] - Multi-layer and Bi-directional RNN

[MXDL-10-07] Recurrent Neural Networks (RNN) [7/8] - Gated Recurrent Unit (GRU)

[MXDL-10-06] Recurrent Neural Networks (RNN) [6/8]- Peephole LSTM models and time series forecasting

[MXDL-10-05] Recurrent Neural Networks (RNN) [5/8] - Build LSTM models for time series forecasting