Mastering Neural Networks and Model Regularization
Completed by Manasi Sawant
August 17, 2026
16 hours (approximately)
Manasi Sawant'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: Model Evaluation
- Category: Decision Tree Learning
- Category: Artificial Neural Networks
- Category: Machine Learning
- Category: Convolutional Neural Networks
- Category: Model Optimization
- Category: Model Training
- Category: Applied Machine Learning
- Category: Supervised Learning
- Category: PyTorch (Machine Learning Library)
- Category: Deep Learning
