@awaisghani2557
This guy is a true legend. Hats off to you dear sir!!
@chanaka7518
Thank you. The way you teach is very easy to understand
@lizardovalencia
WOW, very well explained. Thanks!
@ДенисПирогов-б1з
Awesome tutorial, it's great to see that you are using sklearn for data partitioning, thanks for the tutorial! Keep up the good work👍
@xflory26x
Really love your series - I would really appreciate it if you can go through what the backpropation functions do: optimizer.zero_grad() loss.backward() optimizer.step()
@mariaobmamede
Huge help! Thank you
@truelove-tr7mg
This saved my life. Thank you!
@PhaniKompella-qs9xi
Thanks for the video!!!really helpful to lot of learners
@the_sav4ik
if you have issues with .replace, use
label_map = {'Setosa': 0.0, 'Versicolor': 1.0, 'Virginica': 2.0}
my_data['variety'] = my_data['variety'].map(label_map).astype(float)
@gbolagadeolajide8595
great video! thanks!
@kiitanayandosu-ug7pe
THANK YOUUU
@leigong8590
Thanks for sharing this. You are really patient, but it seems like you did not set up the model in the training mode before your training loop.
@sleepycloud_y
very wonderful video, but i wonder why it goes wrong when i convert the df to numpy arrays. it always warns that 'numpy.ndarray' object has no attribute 'values'
@kombatbakpen6820
Thank you so much. However, I have a problem with my Colab; I am not able to visualize the epochs like you did around the timestamp 19 minutes and 30 seconds. How do I set up my Colab like that?
@shahrizal0715
Amazing learning with your videos and then i got deep dive learning.. anjaay
@JackByrne-z8r
Hi John. Great video and really clear explanation. Just one question, is there a better way of classifying the flower types. It seems like doing it numerically with all types on the same scale is a bit strange and could lead to some confusion for less accurate models. For example, the machine might give a score of 1.1 because it is unsure whether a flower is Setosa (0.0) or Virginica (2.0). However, a score of 1.1 would lead us to conclude that it is most likely Versicolor. Is there a way to avoid this? I hope I'm being clear.
@haider5941
Thank you for this great video. But I faced a problem that I got error of "Module [Model] is missing the required "forward" function" When I ran the model training cell. Note: I checked every single line within the cell
@sinclairarmour1905
I've been typing your code into my PC to try but it's a slow process. Can I get a text file with your code anywhere?
@_derekbuchi
so how do we add a model like into like say yolo architecture
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