Канал: AMILE - Machine Learning with Christian Nabert

Dropout - a Method to Regularize the Training of Deep Neural Networks [Lecture 6.4]

Dropout - a Method to Regularize the Training of Deep Neural Networks [Lecture 6.4]

How does Batch Normalization really works? [Lecture 6.3]

How does Batch Normalization really works? [Lecture 6.3]

Increasing Variance of Deep Neural Networks - Xavier Initalization [Lecture 6.2]

Increasing Variance of Deep Neural Networks - Xavier Initalization [Lecture 6.2]

Vanishing Gradient Problem - Why it is Difficult to Train Deep Neural Networks [Lecture 6.1]

Vanishing Gradient Problem - Why it is Difficult to Train Deep Neural Networks [Lecture 6.1]

Gradient Based Training of Neural Networks [Lecture 5.7]

Gradient Based Training of Neural Networks [Lecture 5.7]

Derive Backpropagation Algorithm for Neural Networt Training [Lecture 5.6]

Derive Backpropagation Algorithm for Neural Networt Training [Lecture 5.6]

Delta Rule for Neural Network Training as Basis for Backpropagation [Lecture 5.5]

Delta Rule for Neural Network Training as Basis for Backpropagation [Lecture 5.5]

How Neural Networks Really Work - From Logistic to Piecewise Linear Regression [Lecture 5.4]

How Neural Networks Really Work - From Logistic to Piecewise Linear Regression [Lecture 5.4]

Activation Function of Neural Networks - Step, Sigmoid, Tanh, ReLU, LeakyReLU, Softmax [Lecture 5.3]

Activation Function of Neural Networks - Step, Sigmoid, Tanh, ReLU, LeakyReLU, Softmax [Lecture 5.3]

Building Neural Networks - Neuron, Single Layer Perceptron, Multi Layer Perceptron [Lecture 5.2]

Building Neural Networks - Neuron, Single Layer Perceptron, Multi Layer Perceptron [Lecture 5.2]

Why Neural Networks for Machine Learning? [Lecture 5.1]

Why Neural Networks for Machine Learning? [Lecture 5.1]

When to Stop the Training of a Decision Tree? - Hyperparameters of Decision Trees [Lecture 4.3]

When to Stop the Training of a Decision Tree? - Hyperparameters of Decision Trees [Lecture 4.3]

How to Make a Decision Tree - Mathematical Theory of Training with Gini Impurity [Lecture 4.2]

How to Make a Decision Tree - Mathematical Theory of Training with Gini Impurity [Lecture 4.2]

Decision Tree Classifier - Main Ideas! [Lecture 4.1]

Decision Tree Classifier - Main Ideas! [Lecture 4.1]

Support Vector Regression - in Comparison to Linear Regression [Lecture 3.6]

Support Vector Regression - in Comparison to Linear Regression [Lecture 3.6]

Soft Margin for Support Vector Machines [Lecture 3.5]

Soft Margin for Support Vector Machines [Lecture 3.5]

Kernel Trick for Support Vector Machines [Lecture 3.4]

Kernel Trick for Support Vector Machines [Lecture 3.4]

Derive the Dual Formulation for Support Vector Machines [Lecture 3.3]

Derive the Dual Formulation for Support Vector Machines [Lecture 3.3]

Training of Support Vector Machines [Lecture 3.2]

Training of Support Vector Machines [Lecture 3.2]

Support Vector Machines - Main Ideas! [Lecture 3.1]

Support Vector Machines - Main Ideas! [Lecture 3.1]

How to Evaluate Classification Models - Confusion Matrix and Precision-Recall Curve [Lecture 2.7]

How to Evaluate Classification Models - Confusion Matrix and Precision-Recall Curve [Lecture 2.7]

What is the Meaning of Cross Entropy/ Log Loss as Cost Function for Classification? [Lecture 2.6]

What is the Meaning of Cross Entropy/ Log Loss as Cost Function for Classification? [Lecture 2.6]

Cross Entropy vs. MSE as Cost Function for Logistic Regression for Classification [Lecture 2.5]

Cross Entropy vs. MSE as Cost Function for Logistic Regression for Classification [Lecture 2.5]

One vs One and One vs All for Multiple Class Classifications [Lecture 2.4]

One vs One and One vs All for Multiple Class Classifications [Lecture 2.4]

Softmax Regression as a Generalization of Logistic Regression for Classification [Lecture 2.3]

Softmax Regression as a Generalization of Logistic Regression for Classification [Lecture 2.3]

Classification with the k-Nearest Neighbor Algorithm - kNN [Lecture 2.2]

Classification with the k-Nearest Neighbor Algorithm - kNN [Lecture 2.2]

Logistic Regression and the Classification Task of Machine Learning [Lecture 2.1]

Logistic Regression and the Classification Task of Machine Learning [Lecture 2.1]

Regularization - Early Stopping, Ridge Regression (L2) and Lasso Regression (L1) [Lecture 1.6]

Regularization - Early Stopping, Ridge Regression (L2) and Lasso Regression (L1) [Lecture 1.6]

Cross Validation - How to Select the best Machine Learning Model? [Lecture 1.5]

Cross Validation - How to Select the best Machine Learning Model? [Lecture 1.5]

Data Preprocessing - Normalization, Outliers, Missing Data, Variable Transformation [Lecture 1.4]

Data Preprocessing - Normalization, Outliers, Missing Data, Variable Transformation [Lecture 1.4]

How Model Training with Gradient Based Minimization Really Works [Lecture 1.3]

How Model Training with Gradient Based Minimization Really Works [Lecture 1.3]

The Different Cost Functions for Regression - Understand MAE, MSE, WMAE [Lecture 1.2]

The Different Cost Functions for Regression - Understand MAE, MSE, WMAE [Lecture 1.2]

Regression in Machine Learning and the Linear Regression Model [Lecture 1.1]

Regression in Machine Learning and the Linear Regression Model [Lecture 1.1]

How Machine Learning Really Works - Understand the Algorithms [Lecture 0]

How Machine Learning Really Works - Understand the Algorithms [Lecture 0]