Brief history of Deep learning - Neuron Doctrine - AI Winter


Summary

In the video, the evolution of deep learning over the past six years is explored, tracing back to the developments in understanding the nervous system dating back to 1871. The limitations of the perceptron model in complex tasks like language translation are discussed, leading to the discovery of back propagation in 1986. This advancement played a crucial role in the development of deep neural networks, marking a significant breakthrough in the field of artificial intelligence.


Introduction to Deep Learning

A brief overview of the history of deep learning, starting around six years ago, highlighting the growth and development in the field.

Early Understanding of Nervous System

Discussion on the history of the nervous system understanding, dating back to 1871, including the development of staining techniques and the concept of neurons.

Perceptron Model and Limitations

Explanation of the perceptron model proposed in the 1950s and the limitations faced, particularly in solving complex decision-making tasks like language translation and understanding.

Back Propagation and Neural Network Advancements

Explanation of the discovery of back propagation around 1986 and its significance in advancing neural networks, leading to breakthroughs in deep neural networks.

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