- Image Analysis
- Transfer Learning
- Network Architecture
- Classification And Regression Tree (CART)
- Regression Analysis
- Recurrent Neural Networks (RNNs)
- Natural Language Processing
- Convolutional Neural Networks
- Artificial Neural Networks
- Model Evaluation
- Machine Learning
- Keras (Neural Network Library)
Introduction to Deep Learning & Neural Networks with Keras
Completed by MD TANVIR RAHMAN
January 24, 2020
10 hours (approximately)
MD TANVIR RAHMAN'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
