Hitting Complexity with Neural Networks
IN THIS CHAPTER
Upgrading the perceptron to the interconnection paradigm
Structuring neural architectures made of nodes and connections
Getting a glimpse of the backpropagation algorithm
Understanding what deep learning is and what it can achieve
“Computers will overtake humans … within the next 100 years. When that happens, we need to make sure the computers have goals that align with ours.”
— STEPHEN HAWKING
As you journey in the world of machine learning, you often see metaphors from the natural world to explain the details of algorithms. This chapter presents a family of learning algorithms that directly derives inspiration from how the brain works. They are neural networks, the core algorithms of the connectionists’ tribe.
Starting with the idea of reverse-engineering how a brain processes signals, the connectionists base neural networks on biological analogies and their components, using brain terms such as neurons and axons as names. However, you’ll discover that neural networks resemble nothing more than a sophisticated kind ...