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1
Intro
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About me
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Visual perception example
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Neuron synapse
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Hubel and Torsten cat experiment
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Hebbian theory
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McCulloch Pitts neuron
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Activate function
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Hopfield networks
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Boltzman machines
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Restricted Boltaman machines
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Deep belief networks
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Loss function
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Stochastic gradient descent
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Backpropagation
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Internal representation of the world
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Classification
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Regression
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Robotics
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Recommender systems
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Clustering
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Network analysis
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Image recognition
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Self driving cars
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Projects
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Questions?
Description:
Explore the fundamentals of data science in this EuroPython 2017 conference talk. Delve into the influence of artificial intelligence and the quest to replicate the human mind. Discover how artificial intelligence draws inspiration from our understanding of the human brain, including visual perception, neuron synapses, and the Hebel and Torsten cat experiment. Learn about key concepts such as Hebbian theory, McCulloch Pitts neurons, and various types of neural networks. Understand essential machine learning principles like loss functions, stochastic gradient descent, and backpropagation. Examine practical applications of data science in classification, regression, robotics, recommender systems, clustering, network analysis, image recognition, and self-driving cars. Conclude with a hands-on demonstration of building a "hello world" machine learning model using Python, providing a solid foundation for those new to the field of data science.

A Gentle Introduction to Data Science

EuroPython Conference
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