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Hey guys and welcome to another fun and easy Machine Learning Tutorial on Artificial Neural Networks.
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Deep learning and Neural Networks are probably one of the hottest tech topics right now. Large corporations and young startups alike are all gold-rushing this state of the art field. If you think big data is important, then you should care about deep learning. Deep Learning (DL) and Neural Network (NN) is currently driving some of the most ingenious inventions this century. Their incredible ability to learn from data and the environment makes them the first choice for machine learning scientists.
Deep Learning and Neural Network lies in the heart of products such as self-driving cars, image recognition software, recommender systems and the list goes on. Evidently, being a powerful algorithm, it is highly adaptive to various data types as well.
People think neural network is an extremely difficult topic to learn. Therefore, either some of them don’t use it, or the ones who use it, use it as a black box. Is there any point in doing something without knowing how is it done? NO! That’s why you’ve’ come to right place at Arduino Startups to Learn about Artificial Neural Networks, so sit back relax and see how deep the rabbit hole goes.
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Thank You!
This video is incredible!!!!!!!!amazing explanation.The best.Can you make a video onhow can I get started with making projects in ANN
Awesome video! thank you!
This is a great video about Neural network which I have seen ever till today, you are the best tutor on youtube you have the ability to teach the students, easy understand difficult things you know better compare to others.
This might as well have been the sound of a jackhammer operating on a steel surface for 19 minutes. I love your videos, and you are a great teacher, but this topic requires a MUCH slower analysis of each step. absolutely impossible to follow.
good video
I wish I found this series earlier in the semester! Thank you for making and posting these vids!
Can someone tell me what the "m" is for on the upper limit of the sigma summation? I usually see it as an "n" notation but here it is an "m". Does this "m" mean "the number of samples" from the data?
Thank you for this truly. This really helped me in reviewing for my final exam. You are amazing an explaining this sir
It's so awesome ! I like your explanation. Thanks, thanks, thanks !! I'm dying to know about ANN.
Thanks for such a great videos, One of the best video for ANN.
I do like your content, but u r the only channel who has soo weird thumbnails, I mean what ? Grylls is doing here???
Great video!!
yeah, all these are good and very informative, BUT, shouldn't be at least a code programme example to give us a real taste?
Excelent presentation
"Arduino Startups" messy work guys
Relu is shit
Use lrelu
You are the best bro. You explained much better than my teacher.Thank You for this lucid yet exhaustive video.
Nice compilation. Thumb up
By far the most intuitive explanation of neural networks. Love u man
The best ANN video I watched.. Thank you.
Thank so very much for the video. Indeed this video is one of the more informative videos I have watch so far. To be honest I started learning about ANN on youtube and by reading ANN methodology employed in some research articles. I see youtube as my teacher and you in particular. If you keep operating with such high impact video, it will attract you millions of subscribers. Hence, I will not hesitate to recommend your youtube channel to my respected colleagues. I'm doing my research at the moment using ANN algorithm and I seriously need your assistance. I trained my network using nntool in MATLAB until the certain MSE values is obtained. I'm not still clear about normalizing my data set and how to train the normalized data set in order to obtain the desire weights when MSE value becomes minimum. I have 124 input variables and 1 output variables. I don't know how to input and were to get all the values including the output values after the training for my data analysis. This is my major challenges at this material time. Your response will go a long way in widening my understanding of ANN especially in real practical application. Thank you
Thank you very useful video. We need more videos about ANN :D.
There is a statistic approach (pictorial representation over theoritical representation) in your teaching I LIKE IT !
Waao mind blowing explanation The Best video I've found for neural network