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Thursday, July 26 • 10:30am - 11:45am
Machine Learning for Startups (Room #C1722) ADD ME

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Limited Capacity seats available

Machine learning has become a popular buzzword in the tech community. An application of artificial intelligence technology, machine learning analyzes data in order to learn and in cases identify specific behaviors, patterns, and trends and then predict or replicate them in the future. Its uses range from healthcare and cybersecurity to travel and shopping, and today in combination with IoT is becoming a valuable tool in many Smart City initiatives.

Yet there is a misconception that to leverage machine learning you need to be a mathematical genius or have a degree in Artificial Intelligence. In reality, most machine-learning applications use well-understood, well-tested, off-the-shelf algorithms. For many developers, especially those at early stage startups, the real challenge lies in training the data. Overcoming this challenge takes insight, strategy, good product development, and good high-quality, labeled training data.

During this session you will get a hands on look at how simple it can be, learn the tricks, see some of the tools, and ask many questions to understand if you really need machine learning or not. El aprendizaje automático mecanizado se ha convertido en una palabra de moda popular en la comunidad tecnológica. Una aplicación de tecnología de inteligencia artificial, el aprendizaje automático analiza los datos para aprender y, en algunos casos, identifica conductas, patrones y tendencias específicas y luego los predice o replica en el futuro. Sus usos van desde la industria de la salud y seguridad cibernética hasta viajes y compras, y hoy en combinación con IoT se está convirtiendo en una herramienta valiosa en muchas iniciativas de desarrollo de Ciudades Inteligentes. Sin embargo, existe la idea errónea de que para aprovechar el aprendizaje automático debe ser un genio matemático o tener un título en Inteligencia Artificial. En realidad, la mayoría de las aplicaciones de aprendizaje automático utilizan algoritmos bien conocidos, bien probados y listos para usar. Para muchos diseñadores informaticos, especialmente aquellos en nuevas empresas en etapa inicial, el verdadero desafío radica en el entrenamiento de los datos. Superar este desafío requiere conocimiento, estrategia, buen desarrollo del producto y buenos datos de capacitación de alta calidad. Durante esta sesión verá usted lo simple que puede ser aprender los trucos, ver algunas de las herramientas, asi como hacer muchas preguntas para comprender si realmente necesita el aprendizaje automático o no.

Moderators
avatar for Dixon Dick

Dixon Dick

Founder, CEO, Archethought
I lead a smart city startup, capturing and sharing data to enhance community and environmental dialogs. As a 35 year veteran of the technology industry, my experience includes a broad variety of development, leadership and creative roles, enabling me to bring a wide range of perspectives... Read More →

Speakers
avatar for Dan Connors

Dan Connors

Chief Technology Officer, Boulder AI
Boulder AI’s CTO, Dan Connors, PhD, is the co-founder of Boulder AI, Head of the Computer Engineering Program in the Department of Electrical Engineering at the University of Colorado in Denver and founder of Avout Education Development Technologies. He has received a number teaching... Read More →

Sponsors
avatar for Xilinx

Xilinx

Xilinx
Building the Adaptable Intelligent World - Xilinx develops highly flexible and adaptive processing platforms that enable rapid innovation – from the endpoint to the edge to the cloud. Xilinx is the inventor of the FPGA, programmable SoCs and the ACAP, designed to deliver the most... Read More →



Attendees (37)