Скачать или смотреть AI building blocks : Artificial Neurons, Perceptrons and Neural Net Layers

Last week, we introduced the concept of Generative AI and provided an overview of its various applications. In this video, we delve into the components that make Gen AI versatile and useful. We discuss the artificial neuron, a fundamental component that enables a wide range of AI applications. You will learn how these simple components are organized to handle more complex learning tasks. The video concludes with two visualizations that illustrate this incredible technology..

ABOUT
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Gunnar Kleemann is a lifelong educator, teaching data science for government organizations, Fortune 500 companies and as a Faculty at the UC Berkeley Masters in Data Science program (MIDS). Based In Austin Texas, he is the CEO of Austin Capital Data (HTTP://austincapitaldata.com) and a partner with Berkeley Data Science Group. Gunnar has enjoyed conducting research on tech trends and physiological genomics. He is published in a number of top tier journals and presents his work domestically and internationally.


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REFERENCES
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D. Dmitriov (2016) MNIST animation
Demo:    • Neural Network 3D Simulation  

Lefkowitz (2019) Cornell Chronicle
https://news.cornell.edu/stories/2019/09/p...

Okdalto 2019 VisualizeMNIST_web:
Code repository: https://okdalto.github.io/VisualizeMNIST_web
demo :    • Neural network visualization  

Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408. https://doi.org/10.1037/h0042519.

Mccollogh and Pitts model schematic (image)
Ramasubramanian, K., Singh, A. (2019). Machine Learning Theory and Practice. In: Machine Learning Using R. Apress, Berkeley, CA. . (pg 429) https://doi.org/10.1007/978-1-4842-4215-5_6 LINK

Yann, The MNIST Database of handwritten digits. Yann LeCun, Courant Institute, NYU Corinna Cortes, Google Labs, New York Christopher J.C. Burges, Microsoft Research, Redmond. (https://yann.lecun.com/exdb/mnist/)