- Recurrent Neural Networks (RNNs)
- Model Evaluation
- Regression Analysis
- Deep Learning
- Transfer Learning
- Machine Learning
- Classification And Regression Tree (CART)
- Natural Language Processing
- Network Architecture
- Convolutional Neural Networks
- Keras (Neural Network Library)
- Image Analysis
Introduction to Deep Learning & Neural Networks with Keras
Completed by QINGXIN WANG
July 10, 2020
10 hours (approximately)
QINGXIN WANG'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
