8 Deep learning: The foundational concepts
This chapter covers
- Core building blocks of deep learning
- Supervised and unsupervised learning approaches
- Convolutional and recurrent neural networks
- The Boltzmann learning rule and deep belief networks
- Python coding with TensorFlow and Keras
- Overview of deep learning libraries
The art of simplicity is a puzzle of complexity.
Welcome to the third part of the book. So far, you have covered a lot of concepts and case studies and Python code. From this chapter onward, the level of complexity will be even higher.
In the first two parts of the book, we covered various unsupervised learning algorithms like clustering, dimensionality reduction, etc. We discussed both simpler and advanced algorithms. We also covered working on text data in the second part of the book. Starting from this third part of the book, we will start our journey on deep learning.
Deep learning and neural networks have changed the world and the business domains. You have probably heard about deep learning and neural networks. Their implementations and sophistication result in better cancer detection, autonomous driving cars, improved disaster management systems, better pollution control systems, reduced fraud in transactions, and so on.