4 Dipping toes in Deep Learning
This chapter covers
- Implementing popular deep learning algorithms (i.e. fully connected networks, convolution neural networks and recurrent networks) using Keras
- Training the models defined with Keras using appropriate datasets
- Understanding and tweaking various parameters in Keras in order to improve model performance
Deep learning is a broad term that has many different algorithms under its wings. Deep learning algorithms come in many different flavors and colors. They can be classified by many criteria; the type of data they consume (e.g. structured data, images, time-series data), depth (shallow, deep and very-deep), etc. The main types of deep networks we are going to discuss, and implement are,
- Fully connected networks
- Convolution neural networks
- Recurrent neural networks
Having the knowledge to comfortably implement the above types of neural networks are a key skill to be successful in the field, whether you are a graduate student, a data scientist or a research scientist. This knowledge directly extends to becoming skillful in implementing more complex deep neural networks that deliver state-of-the-art performance in various problem domains.