Chapter 1. Getting Started with Deep Learning
This chapter explains some of the basic concepts of Machine Learning (ML) and Deep Learning (DL) that will be used in all the subsequent chapters. We will start with a brief introduction to ML. Then we will move to DL, which is a branch of ML based on a set of algorithms that attempt to model high-level abstractions in data.
We will briefly discuss some of the most well-known and widely used neural network architectures, before moving on to coding with TensorFlow in Chapter 2, A First Look at TensorFlow. In this chapter, we will look at various features of DL frameworks and libraries, such as the native language of the framework, multi-GPU support, and aspects of usability.
In a nutshell, the following topics will be covered:
- A soft introduction to ML
- Artificial neural networks
- ML versus DL
- DL neural network architectures
- Available DL frameworks
