- Autoencoders
- Keras (Neural Network Library)
- Model Evaluation
- Image Analysis
- Transfer Learning
- Regression Analysis
- Convolutional Neural Networks
- Classification And Regression Tree (CART)
- Machine Learning
- Artificial Neural Networks
- Applied Machine Learning
- Recurrent Neural Networks (RNNs)
Introduction to Deep Learning & Neural Networks with Keras
Completed by GURDEEP SINGH BHATIA
April 18, 2020
10 hours (approximately)
GURDEEP SINGH BHATIA'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
