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Glossary Index

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Glossary Index

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Glossary term

Deep learning

Quick definition

Deep learning is a subset of machine learning that uses algorithms and unstructured data to cluster, classify, and make predictions for future data.

Key takeaways


●      Artificial neural networks inspired by the human brain form the backbone of everything related to deep learning.

●      Deep learning is integrated into many of the products and services people use every day, like smart assistants, chat boxes, and financial fraud detection.

●      Deep learning can improve the customer experience in all phases of the customer journey.

Prithvi Bhutani is a strategic product leader with over 9 years of experience in startup and enterprise SAAS product platforms. As a principal product manager at Adobe, she manages the design, testing, and launch of machine learning technologies for Adobe Analytics.

Q: What is deep learning?

A: Deep learning is a subset of machine learning — a facet of artificial intelligence (AI) — that works with unstructured data like text and images. Unlike regular machine learning, which requires a human expert to establish hierarchies and other parameters, deep learning algorithms can autonomously detect what features in a data set are most important. This allows deep learning systems to perform tasks like clustering data and making predictions, capabilities that mimic how the human brain learns. Deep learning powers many of the artificial intelligence (AI) applications we see in products and services we use every day.

Q: What is a neural network?

A: Artificial neural networks form the backbone of everything related to deep learning. Neural networks — inspired by the human brain — can identify patterns and imitate how human brains learn and classify information. The ultimate goal is to train deep neural networks to do so faster and better than humans.

Neural networks have two main functions:

●      Clustering – detecting similarities in a group of data.

●      Classification – drawing correlations between data.

Every neural network consists of a few different types of layers — an input layer, hidden layers, and an output layer. Any network with more than three layers is considered “deep.” Every layer has something called a node, which builds upon previous layers to refine and optimize processes and produce specific actions when stimulated.

Q: What are the most common types of deep learning models?

A: The different types of deep learning models are essentially just different kinds of neural networks:

●      Feedforward neural network. This network is often referred to as a regular neural network, as it was the first type of neural network to emerge. In this model, information moves in a singular direction — forward. There is no circular nature like in other models, where data loops over and over. The data travels from the input layer, through the hidden layers, and to the output layer. Because they are relatively simple yet powerful, feedforward neural networks are very beginner-friendly. They also have a wide range of applications including speech recognition, image recognition, and data analytics tasks like regression and predictive modeling.

●      Convolutional neural network. You may hear this type of network referred to as a CNN or ConvNet. A convolutional network is similar to a feedforward network — except it’s not linear, but circular. Data moves through the model in a constant loop. This gives a CNN a variety of applications, including classifying images.

●      Recurrent neural network. This network is recurrent because it has something called a memory state, which means it can store the output of a previous algorithm run and use it to influence a current input and output. This capability is useful in Natural Language Processing (NLP) models.

Q: What are some examples of deep learning in use?

A: Deep learning is so integrated into many of the products and services people use every day that most people are unaware that they are using it. Many of the chatbots on websites and mobile apps use deep learning and NLP processing to understand language and respond appropriately, instead of simply looking for keywords to direct users to certain topics.

Any virtual assistant — from Apple’s Siri, to Amazon’s Alexa, to Google Assistant — uses some form of deep learning.

There are more emerging deep learning applications in financial services as predictive algorithms inform stock trades, business assessments, loan approvals, and fraud detection.

Even law enforcement agencies use deep learning techniques to assist with image recognition, voice analysis, and evidence searches. Image analysis, image classification, and auto caption generation found in tools like Microsoft PowerPoint are also examples of deep learning in action.

Q: Why would you use deep learning instead of machine learning?

A: Deep learning and traditional machine learning work with different types of data, and the algorithms “learn” differently. Depending on your intended goal, one type of learning might work better than another.

Machine learning algorithms use structured, labeled data to make predictions. First, you define specific features in the model’s input data, and then the model organizes the data into labels. A machine learning algorithm can use unstructured data, but only if you pre-process and organize it before running the algorithm. This preprocessing does require a human being to step in and establish a hierarchy, classification, or any distinguishing features.

On the other hand, deep learning eliminates the need for data pre-processing. Instead, the algorithm ingests and processes the unstructured data and then recognizes and determines which of the features are important. Deep learning thrives with large data volumes and highly unstructured data.

Q: Explain anomaly detection in deep learning.

A: Anomalies are outliers, or rare, unexpected deviants in data points. Anomaly detection means finding patterns in data that don’t fit within the norm. Using deep learning for anomaly detection has many advantages. You can work with multivariate data and high dimensional data, and you can model complex relationships.

Q: What are some deep learning platforms or tools?

A: Some of the most common deep learning platforms include:

●      TensorFlow. A free, open-source software library for machine learning and training models.

●      Microsoft Cognitive Toolkit. A free, easy-to-use tool for training deep learning algorithms. It has a commercial grid that allows high volumes of data.

● An open-source, scalable tool popular for predictive analytics.

●      Torch.AI. A tool that supports machine learning frameworks by putting the graphics processing unit (GPU) first and using an easy-to-learn scripting language.



Q: What are the benefits of deep learning?

A: Deep learning can improve the customer experience in all phases of the customer journey by clustering algorithms to identify and hyper-target ideal customers. Deep learning can also help marketers segment customers by aligning traits to specific goals based on what the model learns from the data. Sales teams can use NLP models to analyze and transcribe sales conversations and even indicate the conversation’s key insights. Deep learning automates data entry and uses insights to provide personalized recommendations

Q: How will deep learning evolve in the future?

A: In the coming years, deep learning will continue to be an important part of the conversation about privacy. The key will be helping organizations maintain user privacy, while still taking advantage of the competitive edge that deep learning brings by solving problems more quickly and easily.

As the volume of an organization’s data continues to grow, deep learning will be useful for addressing big data problems, extracting complex patterns, and completing essential tasks like data tagging, indexing, and retrieval.

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