Machine Learning with Neural Networks
Key Takeaways
Covers machine learning principles through artificial neural networks
Original Description
This course explores the principles of machine learning through the lens of one of its most powerful and versatile model classes: the artificial neural network. We will cover the fundamental machine learning concepts of modeling, training, and generalization. You will learn how to process the input data with feed-forward operations, how to train a neural network model using gradient-based optimization and the backpropagation algorithm, and how to ensure it performs well on new data using regularization. In the final module, we discuss Bayesian neural networks, learning how to build models that not only make predictions but also quantify their own uncertainty.
Watch on External: Coursera ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
More on: Neural Network Basics
View skill →Related Reads
📰
📰
📰
📰
Getting Started with Docling!
Medium · Machine Learning
Code Challenge of the Day — Evaluate reverse Polish (medium)
Dev.to · I Want To Learn Programming
Monte Carlo Simulation for Tournament Forecasting: From a Match Model to Bracket Probabilities
Dev.to · Neu Portal
Dars 1: Machine Learning Muhiti va Kirish (Õzbek tilda)
Medium · Deep Learning
🎓
Tutor Explanation
DeepCamp AI