- Network Architecture
- Regression Analysis
- Image Analysis
- Artificial Neural Networks
- Deep Learning
- Model Evaluation
- Machine Learning
- Keras (Neural Network Library)
- Convolutional Neural Networks
- Classification And Regression Tree (CART)
- Recurrent Neural Networks (RNNs)
- Transfer Learning
Introduction to Deep Learning & Neural Networks with Keras
Completed by RYUF FUAD A ALDAKAN RYUF FUAD A ALDAKAN
August 3, 2024
10 hours (approximately)
RYUF FUAD A ALDAKAN RYUF FUAD A ALDAKAN'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
