Mastering Neural Networks and Model Regularization
Completed by Dan Nielsen
June 27, 2025
16 hours (approximately)
Dan Nielsen's account is verified. Coursera certifies their successful completion of Mastering Neural Networks and Model Regularization
What you will learn
Build neural networks from scratch and apply them to real-world datasets like MNIST.
Apply back-propagation for optimizing neural network models and understand computational graphs.
Utilize L1, L2, drop-out regularization, and decision tree pruning to reduce model overfitting.
Implement convolutional neural networks (CNNs) and tensors using PyTorch for image and audio processing.
Skills you will gain
- Category: PyTorch (Machine Learning Library)
- Category: Model Optimization
- Category: Model Training
- Category: Convolutional Neural Networks
- Category: Supervised Learning
- Category: Artificial Neural Networks
- Category: Decision Tree Learning
- Category: Model Evaluation
- Category: Machine Learning
- Category: Deep Learning
- Category: Machine Learning Algorithms
