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What is learning

Machine Learning

IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his research (PDF, 481 KB) (link resides outside IBM) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat almost seems trivial, but it’s considered a major milestone within the field of artificial intelligence. Over the next couple of decades, the technological developments around storage and processing power will enable some innovative products that we know and love today, such as Netflix’s recommendation engine or self-driving cars.

Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them.

Machine Learning vs. Deep Learning vs. Neural Networks

Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, deep learning is actually a sub-field of machine learning, and neural networks is a sub-field of deep learning.

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as “scalable machine learning” as Lex Fridman notes in this MIT lecture (01:08:05) (link resides outside IBM). Classical, or “non-deep”, machine learning is more dependent on human intervention to learn. Human experts determine the set of features to understand the differences between data inputs, usually requiring more structured data to learn.

“Deep” machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the set of features which distinguish different categories of data from one another. Unlike machine learning, it doesn’t require human intervention to process data, allowing us to scale machine learning in more interesting ways. Deep learning and neural networks are primarily credited with accelerating progress in areas, such as computer vision, natural language processing, and speech recognition.

Neural networks, or artificial neural networks (ANNs), are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. The “deep” in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm or a deep neural network. A neural network that only has two or three layers is just a basic neural network.

Demuestra tus aptitudes

Fotografía ingeniosa: Trucos

Fundamentos de la fotografía: Composición 3

Fundamentos de la fotografía: Lentes

Fundamentos de la fotografía: Composición 2

Fundamentos de la fotografía: Flash

Fundamentos de la fotografía: Composición 1

Fundamentos esenciales de la fotografía

Cómo escoger tus accesorios de fotografía

Photoshop CC: Mejoras y modificaciones mediante plugins

Presentación de Liderazgo con inteligencia emocional

Dashboard o cuadro de mando. Definición y contexto

La racionalidad limitada en la toma de decisiones

Valor frente a volumen

Qué es un cuadro de mando o Dashboard

Algunos antecedentes son más significativos que otros

Presentación de Desarrolla tu elevator pitch

Concepto de KPI. Key Performance Indicator

Behavior economics o economía del comportamiento

Menos cosas mejor ejecutadas

El mapa o composición de un dashboard

Contenido extra: el legado que dejamos

Lenguaje no verbal para líderes

Mejora tu competencia en conflictos

Fundamentos de la negociación

Cómo tener conversaciones difíciles

La escucha activa

Cómo trabajar con gente difícil

Persuadir a los demás

Cómo escribir con eficacia

Cómo generar confianza

Comunicación con confianza

Cómo dar feedback a tu personal

Cómo elaborar un plan de comunicación

Excel: Fórmulas y funciones avanzadas (Office 365/Microsoft 365)

Lenguaje no verbal para líderes

Excel: Crear un dashboard intuitivo e impactante desde cero

Mejora tu competencia en conflictos

Excel para principiantes: Tablas dinámicas (365/2019)

Excel para principiantes: Cuadros de mando simples

Excel 2013 práctico: 100 trucos de productividad

Cómo gestionar el enfado en la clientela

Excel 2016 práctico: 25 aplicaciones básicas con tablas dinámicas

Excel: Fórmulas y funciones avanzadas (Office 365/Microsoft 365)

Excel: Crear un dashboard intuitivo e impactante desde cero

Excel para principiantes: Tablas dinámicas (365/2019)

Excel para principiantes: Cuadros de mando simples

Excel 2013 práctico: 100 trucos de productividad

Excel 2016 práctico: 25 aplicaciones básicas con tablas dinámicas

Excel: Tablas dinámicas (Office 365/Microsoft 365)

Excel para principiantes: Cuadros de mando con fuente de datos externa (365/2019)

Excel 2016: Obtener y transformar (Power Query)

Excel: Técnicas de formato avanzadas (Office/Microsoft 365)

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Excel 2013: Tablas dinámicas

Deep learning applications

Real-world deep learning applications are a part of our daily lives, but in most cases, they are so well-integrated into products and services that users are unaware of the complex data processing that is taking place in the background. Some of these examples include the following:

Law enforcement

Deep learning algorithms can analyze and learn from transactional data to identify dangerous patterns that indicate possible fraudulent or criminal activity. Speech recognition, computer vision, and other deep learning applications can improve the efficiency and effectiveness of investigative analysis by extracting patterns and evidence from sound and video recordings, images, and documents, which helps law enforcement analyze large amounts of data more quickly and accurately.

Financial services

Financial institutions regularly use predictive analytics to drive algorithmic trading of stocks, assess business risks for loan approvals, detect fraud, and help manage credit and investment portfolios for clients.

Customer service

Many organizations incorporate deep learning technology into their customer service processes. Chatbots—used in a variety of applications, services, and customer service portals—are a straightforward form of AI. Traditional chatbots use natural language and even visual recognition, commonly found in call center-like menus. However, more sophisticated chatbot solutions attempt to determine, through learning, if there are multiple responses to ambiguous questions. Based on the responses it receives, the chatbot then tries to answer these questions directly or route the conversation to a human user.

Virtual assistants like Apple’s Siri, Amazon Alexa, or Google Assistant extends the idea of a chatbot by enabling speech recognition functionality. This creates a new method to engage users in a personalized way.


The healthcare industry has benefited greatly from deep learning capabilities ever since the digitization of hospital records and images. Image recognition applications can support medical imaging specialists and radiologists, helping them analyze and assess more images in less time.

Deep learning hardware requirements

Deep learning requires a tremendous amount of computing power. High performance graphical processing units (GPUs) are ideal because they can handle a large volume of calculations in multiple cores with copious memory available. However, managing multiple GPUs on-premises can create a large demand on internal resources and be incredibly costly to scale.

For decades now, IBM has been a pioneer in the development of AI technologies and deep learning, highlighted by the development of IBM Watson, IBM’s AI chatbot. One of the earliest accomplishments in deep learning technology, Watson is now a trusted solution for enterprises looking to apply advanced natural language processing and machine learning techniques to their systems using a proven tiered approach to AI adoption and implementation.

Watson uses the Apache Unstructured Information Management Architecture (UIMA) framework and IBM’s DeepQA software to make powerful deep learning capabilities available to applications. Utilizing tools like IBM Watson Studio, your enterprise can harness your big data and bring your data science projects into production while deploying and running your models on any cloud.

Further reading

Gopnik, A. (2016). The Carpenter and the Gardener. What the New Science of Child Development Tells Us about the Relationship Between Parents and Children. London: Boadley Head. This is an excellent critique of the contemporary concern with ‘parenting’ and provides an accessible overview of recent research into the ways children learn from each other, and adults.

Lieberman, M. D. (2013). Social. Why Our Brains Are Wired to Connect. Oxford: Oxford University Press. A good introduction to the development of thinking around the social brain. It includes some discussion of the relevance for educators.

Merriam, S. B., Caffarella, R. S., & Baumgartner, L. M. (2012). Learning in adulthood: a comprehensive guide. 3e. San Francisco: Jossey-Bass. Pretty much the standard text for those concerned with adult education and lifelong learning. It is, as it states in the title, a comprehensive guide.


Cinnamond, J. H. and Zimpher, N. L. (1990). ‘Reflectivity as a function of community’ in R. T. Clift, W. R. Houston and M. C. Pugach (eds.) Encouraging Reflective Practice in Education. An analysis of issues and programs. New York: Teachers College Press.

Cohen, J. D., McClure, S. M., and Yu, A. J. (2007). “Should I Stay or Should I Go? How the Human Brain Manages the Trade-off Between Exploitation and Exploration.” Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1481933–42. doi: 10.1098/rstb.2007.2098.

Kelly, L. (2002). What is learning … and why do museums need to do something about it? A paper presented at the Why Learning? Seminar, Australian Museum/University of Technology Sydney, 22 November. [ Retrieved June 7, 2018].

Miettinen, R. (2000). The concept of experiential learning and John Dewey’s theory of reflective thought and action, International Journal of Lifelong Education, 19:1, 54-72, DOI: 10.1080/026013700293458. [ Retrieved: June8, 2018].

Zull, J. E. (2006). Key aspects of how the brain learns. In S. Johnson & K. Taylor (Eds.), The neuroscience of adult learning (pp. 3–10). New Directions for Adult and Continuing Education, No. 110, San Francisco: Jossey-Bass.

Explorations in Learning & Instruction: The Theory Into Practice Database – TIP is a tool intended to make learning and instructional theory more accessible to educators. The database contains brief summaries of 50 major theories of learning and instruction. These theories can also be accessed by learning domains and concepts.


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