Deep Deterministic Policy Gradient (DDPG)
Definition Deep Deterministic Policy Gradient (DDPG) is an algorithm used in AI and reinforcement learning for finding optimal solutions in continuous action spaces. Being model-free and off-policy, it uses the concept of actor-critic methods for learning optimal policies. In marketing, DDPG can optimize decisions such as pricing, camera angles in digital ads, or personalized recommendations, […]
Decision Trees
Definition In marketing, Decision Trees refer to a type of Artificial Intelligence (AI) modeling tool that utilizes a tree-like model of decisions and their possible outcomes. This can assist in predictive analysis to aid marketers in making strategic choices. It visually maps out complex decision-making scenarios, providing a clearer understanding of potential risks and gains. […]
DCGANs (Deep Convolutional GANs)
Definition DCGANs, or Deep Convolutional GANs, refer to a field in artificial intelligence and machine learning where deep learning and generative adversarial networks (GANs) are employed to generate new, synthetic visuals that closely resemble real-world images. Utilized primarily in marketing, DCGANs can develop images, enhance video quality, and create virtual environments. They use two neural […]
Deep Boltzmann Machines (DBMs)
Definition Deep Boltzmann Machines (DBMs) are a type of Artificial Intelligence algorithm used in unsupervised learning. They utilize a probabilistic graphical model to discover complex, non-linear representations of input data. In marketing terms, DBMs can be used to analyze customer behavior or predict trends by studying patterns in large datasets. Key takeaway Deep Boltzmann Machines […]
Deep Belief Networks (DBNs)
Definition Deep Belief Networks (DBNs) in marketing refer to a type of artificial intelligence, specifically a generative graphical model, often used for tasks like feature recognition or generation. DBNs utilize multiple layers of hidden variables, learning probabilities and correlations among them. This allows DBNs to model complex and abstract patterns in data, enhancing personalization and […]
Deep Q-Networks (DQN)
Definition Deep Q-Networks (DQN) is an AI algorithm used in marketing, combining deep learning and reinforcement learning principles to predict the best possible actions. DQN uses neural networks to approximate Q-values, which are measures of the total reward expected after taking a particular action. In marketing, DQN can be used to optimize campaign strategies by […]
Dropout
Definition In the context of AI and marketing, Dropout refers to a technique used during the training phase of machine learning models to prevent overfitting. It involves randomly deactivating or “dropping out” some neurons in a layer during a training step, which helps to make the model more generalized. Thus, the model is less likely […]
Deep Learning Automation
Definition Deep Learning Automation in marketing refers to the use of artificial intelligence (AI) systems that automatically learn and improve from experience without being explicitly programmed. These systems can automatically analyze and implement data, identify trends, and make decisions, vastly enhancing marketing efficiency. Key applications include personalized marketing, predicting customer behavior, and optimizing customer engagement […]
Dark Knowledge
Definition Dark Knowledge is a term in AI that refers to the intricate, nuanced lessons learned by a neural network during its training phase. It captures the complex decision boundaries learned by the network and these learnings are often not directly observable or understandable. In the context of marketing, it can be used to refine […]
Deep Unsupervised Learning
Definition Deep Unsupervised Learning in AI marketing refers to a type of machine learning where an algorithm learns to identify patterns and structures from unlabelled input data or experiences. The AI system self-learns to group, categorize, or cluster the input data, without any prior training or intervention. In marketing, it can be used to uncover […]
