- PyTorch (Machine Learning Library)
- Deep Learning
- Artificial Neural Networks
- Performance Tuning
- Supervised Learning
- Computer Vision
- Machine Learning Algorithms
- Machine Learning
- Network Architecture
- Decision Tree Learning
Mastering Neural Networks and Model Regularization
Completed by Malini Ramesh
August 20, 2025
16 hours (approximately)
Malini Ramesh'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
