Early Stopping Courses and Certifications

CAP5415 - Training Neural Networks Part 2 - Fall 2020 - Lecture 7

Explore advanced neural network training techniques, including loss functions, backpropagation, gradient descent optimization, and strategies to address common challenges in deep learning.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

MITCBMM

Statistical Learning Theory and Applications - Class 7

Explore statistical learning theory, covering least squares, gradient descent, regularization, and induction, with insights into historical context and practical applications.

• YouTube
• 1 hour 27 minutes
• On-Demand
• Free Video

Databricks

Ray - A Framework for Scaling and Distributing Python and ML Applications

Learn to scale Python and ML applications with Ray, a distributed framework for efficient compute-intensive workloads. Explore architecture, core concepts, and practical applications through XGBoost demo.

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video

Alexander Amini;  MIT

MIT Introduction to Deep Learning

Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Alexander Amini;  MIT

Introduction to Deep Learning

Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Alexander Amini;  MIT

Introduction to Deep Learning

Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.

• YouTube
• 53 minutes
• On-Demand
• Free Video

Jeff Heaton

Early Stopping and Encoding a Feature Vector for Deep Neural Networks

Learn to implement early stopping, encode feature vectors, and use validation sets in Keras neural networks for improved deep learning model performance and efficiency.

• YouTube
• 22 minutes
• On-Demand
• Free Video

Julia Silge

Tune XGBoost With Early Stopping to Predict Shelter Animal Status

Learn to optimize xgboost models using early stopping techniques for predicting animal shelter outcomes, featuring practical examples and efficient coding strategies.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Valerio Velardo - The Sound of AI

Solving Overfitting in Neural Networks

Learn techniques to identify and prevent overfitting in neural networks, including early stopping, audio data augmentation, dropout, and L1/L2 regularization, with practical implementation in a music genre classifier.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Troubleshooting and Improving Neural Network Performance

This course will teach you neural network troubleshooting and performance tuning from a data scientist's perspective.

• Pluralsight
• 1 hour 33 minutes
• On-Demand
• Free Trial Available

Simons Institute

The Elusive Generalization: Classical Bounds to Double Descent to Grokking

Explore the evolution of generalization in machine learning, from classical bounds to double descent and grokking, challenging traditional assumptions and practices in the field.

• YouTube
• 1 hour 20 minutes
• On-Demand
• Free Video