Machine Learning notes

On Logistic_Regression

pytorch logistic regression,   Logistic Regression (Logit)

On NMIST

pytorch nmist example,   pytorch nmist example

On PIL

k-means example of reduced color quantization image

On ROC

ROC (Receiver operating characteristic)

On algebra

Lagrange multiplier,   Probability distributions,   Delta de Kronecker,   Derivative of $Tanh(x)$,   Derivative of Sigmoid ($\sigma$),   Derivatives Algebra,   Derivative of softmax

On back_propagation

Neural network L1 and L2 regulatization,   Neural network back propagation recursive,   Neural network back propagation

On bayes

Bayes theorem (solving Monty Hall problem)

On datasets

MNIST dataset

On derivatives

Derivative of $Tanh(x)$,   Derivative of Sigmoid ($\sigma$),   Derivatives Algebra,   Derivative of softmax

On distributions

Probability distributions

On feature_selection

Recursive Feature Elimination (RFE)

On forward_propagation

Neural network forward propagation

On get_dummies

Transform category columns to boolean

On gradient

sgd momentum comparison,   Gradiend descent

On jupyter

phyton 3.6 jupyter notebook environment

On k-means

k-means example of reduced color quantization image

On neural_network

Neural network L1 and L2 regulatization,   Neural network back propagation recursive,   Neural network back propagation,   Neural network forward propagation

On neural_networks

MNIST dataset,   Neural Convolutional layers

On notebook

phyton 3.6 jupyter notebook environment

On pandas

Transform category columns to boolean

On png

k-means example of reduced color quantization image

On probability

Probability distributions

On python

Cycling learning rate,   sgd momentum comparison,   Clustering k-Means,   Tensorflow Playground (Neural network),   Logistic Regression (Logit),   Recursive Feature Elimination (RFE),   Transform category columns to boolean,   ROC (Receiver operating characteristic)

On pytorch

pytorch nmist example,   pytorch nmist example,   pytorch logistic regression

On recomendation

recomendation systems

On regularization

Neural network L1 and L2 regulatization

On reinforcement_learning

MDPs and Reinforcement Learning

On sigmoid

Derivative of Sigmoid ($\sigma$)

On sklearn

Logistic Regression (Logit),   Recursive Feature Elimination (RFE)

On softmax

Derivative of softmax

On statistics

Precision and Recall

On tanh

Derivative of $Tanh(x)$

On tensorflow

Minimize function in Tensorflow,   Tensorflow Playground (Neural network)

On unsupervised_learning

Clustering k-Means

On visualization

Distributed Stochastic Neighbor Embedding