- Convolutional Neural Networks
- Transfer Learning
- Model Evaluation
- Deep Learning
- Image Analysis
- Artificial Neural Networks
- Classification And Regression Tree (CART)
- Natural Language Processing
- Autoencoders
- Regression Analysis
- Applied Machine Learning
- Keras (Neural Network Library)
Introduction to Deep Learning & Neural Networks with Keras
Completed by Dominik Porębski
March 14, 2025
10 hours (approximately)
Dominik Porębski's account is verified. Coursera certifies their successful completion of Introduction to Deep Learning & Neural Networks with Keras
What you will learn
Describe the foundational concepts of deep learning, neurons, and artificial neural networks to solve real-world problems
Explain the core concepts and components of neural networks and the challenges of training deep networks
Build deep learning models for regression and classification using the Keras library, interpreting model performance metrics effectively.
Design advanced architectures, such as CNNs, RNNs, and transformers, for solving specific problems like image classification and language modeling
Skills you will gain
