To learn more about Lightning: github.com/PyTorchLightning/pytorch-lightning To learn more about Grid: www.grid.ai/ Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
Hi Josh, I have watched your ML playlist and am using space repetition to go over it again and again so that I will never forget the info you shared in that playlist. Now ML topics that come up are not as scary as before, even statistics as well. Thank You kindly Professor Josh ( I wanna call you Professor).
I can confirm that Troll 2 came out during my senior year of high school, 1990. A group of us students quickly formed the Troll 2 fan club of our high school, possibly the only one of the city and state if not the whole US or world at that time.
The poster is clear: it is Troll subscript 2. Which is a linearly independent sample from the data set of Troll. The use of superscript in the other poster is an alternative notation that should have used (2) instead of 2, but the LaTex got scrambled via a cross-entropy loss function error in the encoder training.
Hi, Josh. I really enjoy your videos. This might sound elementary but I have an issue that I would like you to help me with. I am struggling to understand the difference between a confidence and predicition interval and when to use each. Please, could you make a video to explain these two concepts clearly? Thank you.
Sir, there is one chapter which I need to learn and is there for my exam next, week can plz make a videos on that The unit consist of white noise, autocorrelation, power spectral density, linear systems n all I can mail u my pdf notes if u need to know the topics and want to make videos explaining them I missed my classes for almost more than a week so I have no idea what's going on in my university classes,
- Two different tensor views: One for mathematicians and physicists (not discussed). and the second in Neural Networks (we discuss this) - Even simple neural networks require lots of math (boring) - In practice images are 256x256 px times 3 channels = 196, 608 px. Consider a video! lol - From the view of neural networks. They store simple and complex values. Tensor may seem boring, just rename what exists. - if input is 1 value: scalar -> 0-dimensional tensor - if input is two values: array -> 1 dimensional tensor - if input is image : matrix -> 2 dimensional tensor - if input is video: multidimensional matrix or ndarray (python) -> n-dimensional tensor - Tensors are designed for hardware acceleration. They speed up math. They are sped up with GPUs or TPUs (Tensor processing units) - Tensor handle back propagation easily with automatic differentiation
To learn more about Lightning: github.com/PyTorchLightning/pytorch-lightning
To learn more about Grid: www.grid.ai/
Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
No matter how hard I tried, I couldn't understand this in my stats class. 5 minutes of watching this and it all makes sense now. Thank you, Josh!
I love it! :)
Lmao!!! I needed this break from machine learning all nighter, and this comment made my day.
@@magebomba318 BAM! :)
Finally!!!! You've explained troll 2 way clearer than a lecturer could in 7 hours!
OMG! You made me laugh very hard. Good one! :)
It is statistically impossible to not love Troll 2.
Double bam! :)
I always wanted to know more about Troll 2. Glad you can clearly explain it!
bam! :)
This man is a national treasure
BAM!
Thank you!
Love the lore!
bam! :)
Hi Josh, I have watched your ML playlist and am using space repetition to go over it again and again so that I will never forget the info you shared in that playlist. Now ML topics that come up are not as scary as before, even statistics as well. Thank You kindly Professor Josh ( I wanna call you Professor).
Awesome! Congratulations. I'm glad my videos are helpful. :)
@@statquest :D :)
Hardest concept ever digested, but you made this so clear🎉🎉Thank you so much.😂
BAM! :)
I can confirm that Troll 2 came out during my senior year of high school, 1990. A group of us students quickly formed the Troll 2 fan club of our high school, possibly the only one of the city and state if not the whole US or world at that time.
That is awesome! BAM! :)
Cool. The most important chapter of StatQuest evermade
BAM!
The poster is clear: it is Troll subscript 2. Which is a linearly independent sample from the data set of Troll. The use of superscript in the other poster is an alternative notation that should have used (2) instead of 2, but the LaTex got scrambled via a cross-entropy loss function error in the encoder training.
BAM!
I couldn't finish the movie but now that you hinted at the fights, I guess I'll have to!
bam!
This made my day !!
One more 90s movie to watch with my 10-year old + math .. heaven
BAM! :)
I'll have you know I am very familiar with BAM!
ha! BAM! :)
Ahahahahah you even explained this very well! I can't believe now everything makes sense :D Enjoying learning on your channel, thank you
BAM!!!
The concepts are a bit too complex for me in this one. I think I'll need to rewatch Troll, Clearly Explained!!! a few more times first.
That won't help you as Troll 2 is not a sequel to Troll
HA!
Excellent point!
Pure Awesomeness!! I can’t wait to see the film…..
Ha! BAM!
Bam!!!
It’s never a good day to be trolled too
Ha!
Interested the year is contested - I had a friend in high school who showed a group of us this movie on his dvd copy (seemed pretty official)
Nice
New to your channel, and intrigued! Must say, I didn't see a link to a quest about BAM, so am still a bit mystified about that one...
Fixed!!! ua-cam.com/video/i4iUvjsGCMc/v-deo.html
Thank you, this was hilarious.
bam! :)
Hi, Josh. I really enjoy your videos. This might sound elementary but I have an issue that I would like you to help me with. I am struggling to understand the difference between a confidence and predicition interval and when to use each. Please, could you make a video to explain these two concepts clearly? Thank you.
I'll keep that in mind. However, in the meantime, check out: statisticsbyjim.com/hypothesis-testing/confidence-prediction-tolerance-intervals/
@@statquest Thanks a lot.
An here I was thinking this was a new fancy NLP algorithm or something like that 🤣
That would be cool. Maybe one day I'll create an algorithm in Troll 2's honor.
Hey, so I am not familiarised with the topic of troll 1, so should I watch this statquest or do a bit of research about troll 1 before?
See 1:53
Sir, there is one chapter which I need to learn and is there for my exam next, week can plz make a videos on that
The unit consist of white noise, autocorrelation, power spectral density, linear systems n all I can mail u my pdf notes if u need to know the topics and want to make videos explaining them
I missed my classes for almost more than a week so I have no idea what's going on in my university classes,
Unfortunately I can't make those videos right now since I'm on vacation (the first one I've taken in forever)... :(
@@statquest ok no prob sir, these exams which have are internal exams, my final exams starts from 10 july
I was hopeful that you watched Dracula 3000
I'll have to look into it.
Made my day!
Thank you!
BAM!!
Love it
Thanks!
- Two different tensor views: One for mathematicians and physicists (not discussed). and the second in Neural Networks (we discuss this)
- Even simple neural networks require lots of math (boring)
- In practice images are 256x256 px times 3 channels = 196, 608 px. Consider a video! lol
- From the view of neural networks. They store simple and complex values. Tensor may seem boring, just rename what exists.
- if input is 1 value: scalar -> 0-dimensional tensor
- if input is two values: array -> 1 dimensional tensor
- if input is image : matrix -> 2 dimensional tensor
- if input is video: multidimensional matrix or ndarray (python) -> n-dimensional tensor
- Tensors are designed for hardware acceleration. They speed up math. They are sped up with GPUs or TPUs (Tensor processing units)
- Tensor handle back propagation easily with automatic differentiation
A strange video to store your notes, but no worries! I'll leave it here. :)
Man... That is a ln ultimate prima aprilis! Trolling people into watching Troll 2... 😂
Ha! :)
they're eating her ..... and then they're going to eat me...... Oohhhhh myyy gooooooooooooooooooooooooooooooooooooood
YES! :)
When is the year of Troll Cubed?
Great question! :)
Extra credit if you've watched it with Rifftrax
OMG!!! Is that possible?
@@statquest It looks like I can only find highlights clips on youtube. The Rifftrax website doesn't have the recorded audio commentary anymore :(
@@synchro-dentally1965 Total bummer. :(
BAM
:)
Oh my god...
YES!
Too many cooks
ha
Absolutley is the worst movie ever made
So true!