Early Stopping and Encoding a Feature Vector for Deep Neural Networks

Learn how to implement early stopping in Keras neural networks using training and validation splits. Explore techniques for encoding feature vectors and handling various data types, including numeric data and missing values. Discover preprocessing methods and understand the importance of train-test splits in deep learning. This video, part of a comprehensive deep learning course from Washington University in St. Louis, provides practical insights into improving neural network performance and preparing data for effective model training.

Introduction
Feature Vector
Data Sets
Numeric Data
Encoding Functions
Missing Median
Preprocessing
Training
Training Error
Validation Sets
Train Test Split