In this course, you will learn:
- You'll discover about intriguing deep learning applications and why learning how to harness deep learning capabilities is so rewarding.
- Neural networks will be discussed, as well as how they learn and update their weights and biases.
- The vanishing gradient problem will be discussed.
- You'll learn how to use the Keras library to create a regression model.
- You'll learn how to use the Keras library to create a classification model.
- You'll learn about supervised deep learning models like convolutional neural networks and recurrent neural networks, as well as how to use the Keras library to build a convolutional neural network.
- Unsupervised learning models such as autoencoders will be discussed.
1. Introduction to Deep Learning
- Introduction to Deep Learning
- Biological Neural Networks
- Artificial Neural Networks - Forward Propagation
2. Artificial Neural Networks
- Gradient Descent
- Vanishing Gradient
- Activation Functions
3. Deep Learning Libraries
- Introduction to Deep Learning Libraries
- Regression Models with Keras
- Classification Models with Keras
4. Deep Learning Models
- Shallow and Deep Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks