A Neural Network Primer

[Tier 1, Lecture 04c] This video provides a primer on neural networks for machine learning and artificial intelligence. Neural networks are biologically inspired and provide the backbone of many modern ML/AI frameworks.

This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company

%%% CHAPTERS %%%
0:00 Overview
2:15 What is a Neural Network?
5:17 The Perceptron (History of Neural Networks)
6:39 Deep Learning
8:50 A Diversity of Architectures: the Neural Network Zoo
11:30 CNN: Convolutional Neural Networks
13:11 RNN: Recurrent Neural Networks
14:01 Autoencoder Networks
16:20 Outro

[martincardenas]

The lectures of Professor Brunton are outstanding from all points of view: fachlich, pädagogisch, organisatorisch and, why not, sprachlich (my first language is not English).
For me, as a 78 old control engineer, your lectures are really a pleasure...
Thank You very much for your knowledge, time and energy

[mustaphasadok]

Thank you professor,

Best recap for beginners

[VinnieMTG]

Finally a good channel for learning ai! YouTube is filled with opportunists and I'm glad to find this channel thank you so much

[hasinabrar]

Very good and informative video as always. I Would really love to see more videos on this and if possible after this a series on CFD and/or FEA.

[sung]

Thank u steve for continuing to make wonderful and relevant content

[deltax]

such a great analogy with the periodic table to our current list of models and what kinds of problems they are good for solving. Look forward to the day that we have a nice lookup table, or even better, a NN that looks at our dataset and the problem at hand and gives us a list of potential models and how probable that they are the "best" model to choose for this problem.

[MrWater]

Steve thank you very much I follow all of your videos and books, big fan of you! I really enjoy how you explain, I’ve learned a lot.

[GeoffryGifari]

If lets say we succeeded in pinning the behavior of neural networks rigorously, what do you think the "physical laws" of neural networks would look like? how can we write them down?

[tshepisosoetsane]

I like this channel it goes to science and engineering principles and makes sense

[DaniMilak]

Lol, I just typed 'convolutional neural network' into YouTube, and then, 3 seconds later, I received the notification about this video :D

[netuno]

But anyway thank you for your great class about NN. I have learned a lot after I configured the velocity to 0, 75 and paused the video sometimes to think about what you have just explained.

[jerewang]

Excellent summary and explanation 👏🏻 Keep up the great work!

[reyes]

So ready to dive into this series. Using the biological system analogy, what makes a learning model ‘smart’?Thank you Steve.

[rsbenari]

Crystal. And needed. Suggests what the math might look like -- enough so to want to go on to the next installment. Thanks so much.

[alial-ghanimi]

Impressive explanation for such a hot topic

[orcu]

Great one! I would also be interested in the thought of RNNs for CTR estimations for seasonality considerations.

[vitorbortolin]

I am eager to learn more about deep autoenconder !

[GeoffryGifari]

For people who build neural networks, where do they get the data from? are there special repositories that provide datasets?

[yashjaiman]

Really good
Got the jist of Neural Nets

[jadenC]

Thanks for the excellent explanation. Can you share the information about your book that you mentioned in the video?