How AI Learns (Backpropagation 101)

Explore the fundamental process of backpropagation in artificial intelligence (AI). This video show how neural networks learn and improve by adapting to data during each training phase. Backpropagation is crucial in calculating errors and updating the network's weights to enhance decision-making within the AI system. This tutorial breaks down the core mechanics of neural network training, making it easier to understand for individuals interested in AI, machine learning, and network training. By understanding backpropagation, viewers can better grasp how neural networks evolve to process information more accurately. Keywords: rosenblatt, AI, Artificial Intelligence, Neural Networks, Backpropagation, Machine Learning, Network Training, Data Adaptation, Error Calculation, Performance Tuning, Decision Making.

[markheaney]

This is easily the best channel on YouTube.

[TimBorny]

As always, worth the wait. You are a genius at distillation and visualization.

[robertbohrer]

This is the best explanation of neural networks I've seen by far, and I've seen most of them.

[austinvw]

WOW!! This is the only video that I've watched that made me finally get it. The inclusion of the physical dimmer switch and weights in the neural net made me finally start to grasp this concept. Thank You! 👏

[baechlio]

Yay!!! My favourite channel finally uploads again.. to be honest the quality of your videos makes the wait worth it

[TimBorny]

Seriously impressive. As someone currently applying to masters degrees in science communication, you are an inspiration. While a personal inquiry within a public forum is generally not advisable, I'm compelled to wonder if you'd be willing to be available for a brief conversation.

[AstonVantage]

Have been watching quite a few videos to try to get an intuitive understanding of the inner workings of neural network. Some are too simplistic while others go way deep into the math. This video has just the right level of detail and accompany with excellent illustrations and explanation. It is by far the best I have come across.
Glad I found this channel, look forward to the other videos.

[ArtOfTheProblem]

STAY TUNED: Next video will be on "History of RL | How AI Learned to Feel"
WATCH AI series:

[raresmircea]

This, along with everything else on this channel, is fantastic material for schools. I hope it gets noticed by teachers

[KhaliliStudios]

I’m always very impressed at the novel approach to teaching these subjects - another hit, Brit!

[rj]

After reading all day about tensorflow you just inspired me to go deeper on this subject. Thank you

[iMamoMC]

This video was great! What an awesome introduction to deep learning ^^

[TheFirstObserver]

This is a well-done, visual representation of artificial neural networks and how they compare to biological ones. I will say, the only item I might add is that the real reason the "gradual" activation functions mentioned in the latter half of the video are so useful is because they are differentiable. The functions being differentiable are what truly allowed backpropagation to shine, as the chain rule is what allowed the error of a neuron to be determined by the error of the neurons following it, rather than calculating a neuron's error from the output directly each time.

[ccc]

Your videos are great at making someone more curious about a subject. They have the right balance of simplification and complexity.

[zyugyzarc]

now that's a brilliant explanation of neural networks. better than anything Ive ever seen.

[Aksahnsh]

I just don't understand, why this channel is not popular.

[srabansinha]

As a Medical Student studying Neural anatomy and Physiology, this is a whole new perspective to me !!! Keep teaching us More !!You are the Best teacher :)

[yomanos]

Brilliant video, as always. The part on the explanation of a deep neural network was really well explained.

[mehdia]

Beautiful work coming from a beautiful biological neural network about the beauty of artificial neural networks.

[ilovett]

This could be a Netflix series. Bravo.