Learning Outcomes By the end of this section, you should be able to: AI and deep learning have progressed rapidly in the last decade. Some of the most significant breakthroughs have come from natural language processing, or NLP, which is an area…
Learning Outcomes By the end of this section, you should be able to: Convolutional neural networks (CNNs) are a powerful class of neural network models developed to process structured, grid-like data, such as images, making use of the mathematical operation of convolution (which…
Learning Outcomes By the end of this section, you should be able to: Deep learning is a general term for the training and implementation of neural networks with many layers to learn the relationships of structured representations of data. A subset…
Learning Outcomes By the end of this section, you should be able to: In the previous section, we discussed the single-layer perceptron. Training the perceptron is straightforward as it only requires adjusting a set of weights and biases that directly…
Learning Outcomes By the end of this section, you should be able to: Just imagine what must go on inside the human brain when tasked with recognizing digits, given the varying ways the digits 0-9 can be hand-drawn. Human readers…
Figure 7.1 Neural networks are central to modern AI, especially in the fields of deep learning and machine learning. Inspired by the human brain’s structure and functioning, neural networks consist of interconnected layers of artificial neurons designed to recognize patterns, learn from…
Learning Outcomes By the end of this section, you should be able to: So far, we have encountered a number of different machine learning algorithms suited to particular tasks. Naturally, the realm of machine learning extends well beyond this small…
Learning Outcomes By the end of this section, you should be able to: How do you make decisions? Often, we choose one product over another based on a combination of features. For instance, imagine you are considering purchasing a new…
Learning Outcomes By the end of this section, you should be able to: Regression is a term that applies to many different techniques in data analysis and machine learning. The main idea is to model a relationship between one or…
Learning Outcomes By the end of this section, you should be able to: Classification problems come in many flavors. For example, suppose you are tasked with creating an algorithm that diagnoses heart disease. The input features may include biological sex,…