Tag: Training

>> How to Analyze Loss vs. Epoch Graphs?

>> Lazy vs. Eager Learning

>> What Are Downstream Tasks?

>> 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

>> Parameters vs. Hyperparameters

>> How to Handle Large Images to Train CNNs?

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

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

>> 0-1 Loss Function Explained

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

>> Bias Update in Neural Network Backpropagation

>> Training and Validation Loss in Deep Learning

>> Differences Between SGD and Backpropagation

>> Relation Between Learning Rate and Batch Size

>> Choosing a Learning Rate

>> How to Calculate the Regularization Parameter in Linear Regression

>> 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?