- Classification And Regression Tree (CART)
- Autoencoders
- Convolutional Neural Networks
- Transfer Learning
- Deep Learning
- Artificial Neural Networks
- Regression Analysis
- Model Evaluation
- Recurrent Neural Networks (RNNs)
- Image Analysis
- Machine Learning Methods
- Machine Learning
Introduction to Deep Learning & Neural Networks with Keras
Completed by Vaibhav Arekar
May 31, 2020
10 hours (approximately)
Vaibhav Arekar'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
