Deep Learning with Keras
Deep Learning lies at the heart of many leading machine learning and artificial intelligence applications. This course, Deep Learning with Keras, shows you how to use Keras to quickly create powerful deep neural networks.
What you'll learn
There has been a revolution in artificial intelligence (AI) and machine learning, and deep learning-based solutions are leading the charge. Implementing these solutions can be tedious to create and require you to write many lines of complex code. Keras is a library that makes it much easier for you to create these deep learning solutions. In a few lines of code, you can create a model that could require hundreds of lines of conventional code.
This course, Deep Learning with Keras, will get you up to speed with both the theory and practice of using Keras to implement deep neural networks.
First, you will dive deep into learning how Keras implements various layers of neurons quickly and easily, with each layer defining the specific functionality needed to implement parts of your solution.
Next, you will discover how to use Keras’ various methods for interconnecting these layers to form the structure of your deep neural networks. Finally, you will learn how you use Keras to implement several state-of-the-art neural networks, such as the widely used Convolutional and Recurrent Neural Networks, to make these concepts come to life.
By the end of this course, you will gain the skills and experience required to effectively create deep neural networks through the course’s combination of lecture and hands-on coding.
Table of contents
- Introduction to CNNs 1m
- Why Do CNNs Exists? 4m
- How Do CNNs Work? 2m
- Convolution Layer 5m
- Convolution Layer Hyperparameters 4m
- Non-linear Activation 2m
- Pool Layer 5m
- Implementing CNNs in Keras 3m
- Coding Fashion MNIST 8m
- Transfer Learning Principles 4m
- Transfer Learning Implementation 11m
- Conclusion 3m