- Convolutional Neural Networks
- Network Architecture
- Autoencoders
- Classification And Regression Tree (CART)
- Image Analysis
- Model Evaluation
- Regression Analysis
- Keras (Neural Network Library)
- Applied Machine Learning
- Natural Language Processing
- Deep Learning
- Artificial Neural Networks
Introduction to Deep Learning & Neural Networks with Keras
Completed by Srishti Verma
October 18, 2024
10 hours (approximately)
Srishti Verma'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
