- Machine Learning Methods
- Image Analysis
- Artificial Neural Networks
- Keras (Neural Network Library)
- Natural Language Processing
- Convolutional Neural Networks
- Regression Analysis
- Autoencoders
- Recurrent Neural Networks (RNNs)
- Deep Learning
- Network Architecture
- Model Evaluation
Introduction to Deep Learning & Neural Networks with Keras
Completed by Dilip Kumar Bandaru
July 6, 2024
10 hours (approximately)
Dilip Kumar Bandaru'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
