- Deep Learning
- Image Analysis
- Autoencoders
- Transfer Learning
- Model Evaluation
- Regression Analysis
- Keras (Neural Network Library)
- Artificial Neural Networks
- Convolutional Neural Networks
- Natural Language Processing
- Machine Learning
- Classification And Regression Tree (CART)
Introduction to Deep Learning & Neural Networks with Keras
Completed by Elica Efremova
June 30, 2020
10 hours (approximately)
Elica Efremova'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
