This video is brilliant. It's so difficult to find someone who explains these concepts from first principles. Other videos use abstraction and rush through, leaving learners with a "black box" in their knowledge and are told "Don't worry about it, you just need to know how to get the answer on the test or the job done." However you explained it in a way that is so much deeper.
A++ love this series. Love to see more on the hardware side, never had time myself and its so inspiring to see!
Very good explanation! Keep going! We need to educate more people to start working with neural networks!
Another great video. Well explained and to the point. Looking forward to the practical applications
finally caught up to the most recent video. This has been a pretty exciting series.
This is great stuff! Please make more!
Hey mr.wabiszewskii sir Being a newbie and yet in my teenage being fascinated by all these things, i would request you to make a video regarding roadmap to get in the field of neural networks and AI It would be very kind of u if you do so and keep the amazing videos coming
Thank you for this video, this is an incredible high-quality explanation for beginners.
This has been fantastic! I'm looking forward to move videos. Subscribed!
HI MR. WABISZEWSKIIIII. i miss your class 😔
sir could you pls upload more videos becoz your videos are interesting
Can you give us a design of your artificial synapse on lt spice please?
A new video? I am still stuck with the 4bit Breadboard Computer!
6:11 I don't understand why we have to randomize the weights and biases, why not just make them all the same (I can only read the subtitles, I can't understand English)
I'm broke but I want to support you. Can I make your channel some badass theme music?
Sir please give us your github link so that we can learn things from you
Seems you could use an op amp-biased in a negative range, which would give you 3 states. (-5 0 +5) . I'm out of my league here.
@shamirgrant