- Image Analysis
- Model Evaluation
- Artificial Neural Networks
- Regression Analysis
- Deep Learning
- Natural Language Processing
- Transfer Learning
- Convolutional Neural Networks
- Network Architecture
- Keras (Neural Network Library)
- Classification And Regression Tree (CART)
- Recurrent Neural Networks (RNNs)
Introduction to Deep Learning & Neural Networks with Keras
Completed by ERFAN ABDOLMALEKI
June 19, 2022
10 hours (approximately)
ERFAN ABDOLMALEKI'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
