Tag: Training

>> ReLU vs. LeakyReLU vs. PReLU

>> How to Handle Missing Data in Logistic Regression?

>> How to Handle Unbalanced Data With SMOTE?

>> What Is TinyML?

>> Prevent the Vanishing Gradient Problem with LSTM

>> How to Do Early Stopping?

>> Bagging, Boosting, and Stacking in Machine Learning

>> Machine Learning: Analytical Learning

>> How to Analyze Loss vs. Epoch Graphs?

>> What’s a Non-trainable Parameter?

>> Lazy vs. Eager Learning

>> What Are Downstream Tasks?

>> Automated Machine Learning Explained

>> What Is Federated Learning?

>> What Does Learning Rate Warm-up Mean?

>> What Is End-to-End Deep Learning?

>> Natural Language Processing: Bleu Score

>> Introduction to Triplet Loss

>> ADAM Optimizer

>> Differences Between Hinge Loss and Logistic Loss

>> Machine Learning: How to Format Images for Training

>> What Is Multi-Task Learning?

>> One-Hot Encoding Explained

>> Feature Selection in Machine Learning

>> Differences Between Transfer Learning and Meta-Learning

>> Why Use a Surrogate Loss

>> Parameters vs. Hyperparameters

>> Differences Between Bias and Error

>> How to Handle Large Images to Train CNNs?

>> Data Augmentation

>> What Is Fine-Tuning in Neural Networks?

>> How to Use Gabor Filters to Generate Features for Machine Learning

>> What Does Pre-training a Neural Network Mean?

>> Neural Networks: What Is Weight Decay Loss?

>> Differences Between Gradient, Stochastic and Mini Batch Gradient Descent

>> Scale-Invariant Feature Transform

>> F-Beta Score

>> What’s a Hypothesis Space?

>> 0-1 Loss Function Explained

>> Differences Between Epoch, Batch, and Mini-batch

>> Differences Between Backpropagation and Feedforward Networks

>> Bias Update in Neural Network Backpropagation

>> Transfer Learning vs Domain Adaptation

>> An Introduction to Self-Supervised Learning

>> An Introduction to Contrastive Learning

>> Training and Validation Loss in Deep Learning

>> ML: Train, Validate, and Test

>> Differences Between SGD and Backpropagation

>> Relation Between Learning Rate and Batch Size

>> Stratified Sampling in Machine Learning

>> Choosing a Learning Rate

>> Underfitting and Overfitting in Machine Learning

>> How to Calculate the Regularization Parameter in Linear Regression

>> Feature Selection and Reduction for Text Classification

>> Why Mini-Batch Size Is Better Than One Single “Batch” With All Training Data

>> Instance vs Batch Normalization

>> Why Feature Scaling in SVM?

>> Normalization vs Standardization in Linear Regression

>> Normalize Features of a Table

>> Gradient Descent Equation in Logistic Regression

>> Interpretation of Loss and Accuracy for a Machine Learning Model

>> Splitting a Dataset into Train and Test Sets

>> Epoch in Neural Networks

>> Random Initialization of Weights in a Neural Network

>> Training Data for Sentiment Analysis

>> Why Does the Cost Function of Logistic Regression Have a Logarithmic Expression?