- Network Architecture
- Artificial Neural Networks
- Deep Learning
- Regression Analysis
- Natural Language Processing
- Keras (Neural Network Library)
- Classification And Regression Tree (CART)
- Transfer Learning
- Machine Learning
- Model Evaluation
- Image Analysis
- Convolutional Neural Networks
Introduction to Deep Learning & Neural Networks with Keras
Completed by Stéphane Nguyen
June 14, 2021
10 hours (approximately)
Stéphane Nguyen'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
