This chapter provides a whirlwind tour of deep learning essentials, starting from the very basics of what deep learning really means, and then moving on to other essential concepts and terminology around neural networks. The reader will be given an overview of the basic building blocks of neural networks, and how deep neural networks are trained. Concepts surrounding model training, including activation functions, loss functions, backpropagation, and hyperparameter-tuning strategies will be covered. These foundational concepts will be of great help for both beginners and experienced data scientists who are venturing into deep neural network models. Special focus has been given to how to set up a robust cloud-based deep learning environment with GPU support, along with tips for setting up an in-house deep learning environment. This should be very useful for readers looking to build large-scale deep learning models on their own. The following topics will be covered in the chapter:

• What is deep learning?
• Deep learning fundamentals
• Setting up a robust, cloud-based deep learning environment with GPU support
• Setting up a robust, on-premise deep learning environment with GPU support
• Neural network basics