- Keras (Neural Network Library)
- Natural Language Processing
- Model Evaluation
- Deep Learning
- Classification And Regression Tree (CART)
- Regression Analysis
- Network Architecture
- Recurrent Neural Networks (RNNs)
- Image Analysis
- Machine Learning
- Convolutional Neural Networks
- Artificial Neural Networks
Introduction to Deep Learning & Neural Networks with Keras
Completed by hemanth venkata krishna kandula
November 27, 2020
10 hours (approximately)
hemanth venkata krishna kandula'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
