Deep Learning and Neural Networks

Deep learning is a specialized subset of machine learning that involves training neural networks with many layers, allowing for the processing of vast amounts of data. Neural networks are inspired by the human brain and consist of neurons, which are nodes connected by edges. These networks can recognize patterns in data, such as identifying objects in images or recognizing speech.

In this chapter, we will explore how convolutional neural networks (CNNs) are used in computer vision tasks like image classification and object detection. We’ll also discuss recurrent neural networks (RNNs), which are particularly useful for sequence data, such as time-series analysis or language modeling.

The concept of backpropagation is central to deep learning, as it allows the neural network to adjust weights and minimize errors through a process of optimization. We’ll discuss how training deep learning models requires significant computational power and the role of frameworks like TensorFlow and Keras in simplifying these processes.

By the end of this chapter, you’ll have a basic understanding of deep learning and neural networks, and how they are used in some of the most exciting applications of AI today, such as self-driving cars and speech recognition.