I don’t comment often videos but this one is really good, you explained the math concept really well by doing the examples on the graph etc… Good stuff in this channel !
this video is amazing, good job, im now actually thinking about re visiting the math classes i couldn't take before in order to get better at these machine learning algorithims.
The way you explained and implemented was really efficient and easy, it was really helpful as I am just getting started on my ML journey and after implementing this I feel m a step forward. Thank You!!
Thanks man! Awesome video that dumbed it down enough for me. Could you do linear regression of fitting a sphere to points given the sphere radius (or not) like is used in terrestrial laser scanning? If you've never messed with it, you place spheres around what you are scanning, then the software uses the spheres to align all of the different scans at different locations together.
I am subscribing, definitely. You have taught in the most straight forward and explanatory way a concept that other videos made a bit too complicated. Thank you!
Damn !! I like your video so much, especially the implementation part. Most of the people would just directly use sklearn library and call it a day. But, you really have showed how the code actually runs behind the scene. 👍👍💛💛
Awesome tutorial! Could you please explain why you use gradient descent to minimize squared error instead of using the formula: divide the standard deviation of y values by the standard deviation of x values and then multiply this by the correlation between x and y?
A really insightful video to learn this concept!! I found it very helpful !! Just one suggestion @NeuralNine, please include the files (that you display in the video) in the description of the video. Everything else is amazing and praiseworthy!
hi , really you have the best videos in youtube. Your speaking is also very clear for me although my native language is not English. Thanks you very much it helps me very much , and i am in 17 years old.I want to improve and in my this way you help me.
I implemented it myself and it came out to be more accurate then sckit-learn import numpy as np from sympy import Symbol, solve, diff class LinearRegression: def __init__(self): self.w = {} def fit(self, x, y): for i in np.arange(x.shape[1] + 1): self.w[f"w{i}"] = Symbol(f"w{i}") e = 0 for i in range(len(x)): yp = 0 for j in np.arange(len(self.w)): if j == 0: yp += self.w[f"w{j}"] else: yp += self.w[f"w{j}"] * x[i][j-1] e += (yp-y[i]) ** 2 eq = [] for i in np.arange(len(self.w)): eq.append(diff(e, self.w[f"w{i}"])) w = solve(eq, list(self.w.keys())) for i in np.arange(len(self.w)): self.w[f"w{i}"] = w[self.w[f"w{i}"]] def predict(self, x): def prediction(features): yp = 0 for i in np.arange(len(self.w)): if i == 0: yp += self.w[f"w{i}"] else: yp += self.w[f"w{i}"] * features[i-1] return yp return list(map(prediction, x))
Excellent video, although I will need to scrub up on my mathematics!! I think I am one of the few that loves looking at formulas and wonders; "What in the hell is going on there?" 😆😆
Lmao, I finished summer school for algebra 1 and we just learned this and to calculate it, we used a calculator this is a amazing video the mathematics where correct and your explanation is amazing!
Why does 1/n become -2/n. As I read in towards data science that it would be 1/n and then the -2 would be after the sum symbol as 2x'i' so like why or how does 1/n become -2/n. Also great video btw, helped me unlock a few skills in the ml skill tree :)
great video NeuralNine I feel this the gradiant descent. is not it ? if It is, is there an implementation for the least square because I feel it is just so random you take some values and you choose the least one from those random values not necessarily the least value you can get
Hi sir , it's really helps me to understand linear regression and build it by myself from scratch. Thanks 👍 can you describe all the models from scratch? Like it's
I dont understand the reasoning behind [m * x * b for x in range(20, 80)] why is this the y axis. What does it represent. Why the equation? Also why arent we just reading the found values of x and y? I have understood all the calculations being done but I dont understand the visualization/graph representation part. Please let me know if you see this.
If you're counting from 0 to n, it means you have (n+1) data points, so should the dividing factor not be 1/(n+1) while calculating mean of squared errors at 4:43?
I am getting error AttributeError Traceback (most recent call last) Cell In[10], line 39 37 if i % 50 == 0: 38 print(f"Epoch: {i}") ---> 39 m, b = gradient_descent(m, b, data, L) 41 print(m, b) 43 plt.scatter(data.studytime, data.score, color = "black")
@NeuralNine How do I test the trained models to give a prediction based on a newly given value/independent variable (a value that is foreign to the training set)? Great video btw!
What's the point of printing Epochs if we see nothing on the screen in that regard lol, why is it highly mathematical if we do not even derive anything? I would redo the video with loss_function being in the print, o/w it just hangs in the code for God knows what reason.
Hello, do you have a video or notes on plotting the linear regression when attributes in the input data points are more than one. Say 10 columns of x, and 1 column of y?? Please respond. Thank you.
@@sairekhaunnam3001 Hey, if there is one attribute the we can plot it on 2D, if two attributes, then in 3D, and for three attribute, we will plot in 4D, which is not possible visually. That's why we restrict ourselves.
Please do more mathematical implementations like this please! This really helped me to understand the math behind these algorithms
😭😭
True. Nobody explains like he does.
I don’t comment often videos but this one is really good, you explained the math concept really well by doing the examples on the graph etc… Good stuff in this channel !
this video is amazing, good job, im now actually thinking about re visiting the math classes i couldn't take before in order to get better at these machine learning algorithims.
I do like all your videos that contain the keywords "mathematical theory" and "from scratch" :). Please do more similar videos. Thank you kindly
same, stuff from scratch is the best stuff for learning
@@samas69420 yeah, and it's really fun
I am always so proud when I am able to code something from scratch
That was just wow! The way you explained it was amazing.
Thank you!
I was searching everywhere and final found what I need. Your video really clears up the fundamentals of creating Linear Regression Model. Thank you
I recently stumbled upon this while looking for similar approach via python and I subscribed you now.
Thank you very much for imparting knowledge.
dude Slammer video!! love the fact that you made the math interesting and super easy to understand
The way you explained and implemented was really efficient and easy, it was really helpful as I am just getting started on my ML journey and after implementing this I feel m a step forward. Thank You!!
Thanks man! Awesome video that dumbed it down enough for me. Could you do linear regression of fitting a sphere to points given the sphere radius (or not) like is used in terrestrial laser scanning? If you've never messed with it, you place spheres around what you are scanning, then the software uses the spheres to align all of the different scans at different locations together.
I am subscribing, definitely. You have taught in the most straight forward and explanatory way a concept that other videos made a bit too complicated. Thank you!
AMAZING video!!! I love these videos which teach theory too! Thank youuu!!!
Thanks very much for this!! I am a data analysis student and close to giving up but still hanging on!
dont give up ,u can do it ❤
@@kelvinthomas-pr8sd Thank you! I did then but it still apllies now. I now work but have 2 classes I haven't passed yet. Never give up!
Damn !! I like your video so much, especially the implementation part. Most of the people would just directly use sklearn library and call it a day. But, you really have showed how the code actually runs behind the scene. 👍👍💛💛
Greatly explained in simple words, looking forward to learn more methods from you.
Thank you so much. Im working through problem set for a Neural Analysis class and this really helps. Great video.
Awesome tutorial! Could you please explain why you use gradient descent to minimize squared error instead of using the formula: divide the standard deviation of y values by the standard deviation of x values and then multiply this by the correlation between x and y?
Easy to follow and goes straight to the point, thank you!
A really insightful video to learn this concept!! I found it very helpful !!
Just one suggestion @NeuralNine, please include the files (that you display in the video) in the description of the video.
Everything else is amazing and praiseworthy!
hi , really you have the best videos in youtube. Your speaking is also very clear for me although my native language is not English. Thanks you very much it helps me very much , and i am in 17 years old.I want to improve and in my this way you help me.
I really appreciate the time and the effort to simplify such a thing, Thanks a bunch 🌹🌹🌹🌹
I implemented it myself and it came out to be more accurate then sckit-learn
import numpy as np
from sympy import Symbol, solve, diff
class LinearRegression:
def __init__(self):
self.w = {}
def fit(self, x, y):
for i in np.arange(x.shape[1] + 1):
self.w[f"w{i}"] = Symbol(f"w{i}")
e = 0
for i in range(len(x)):
yp = 0
for j in np.arange(len(self.w)):
if j == 0:
yp += self.w[f"w{j}"]
else:
yp += self.w[f"w{j}"] * x[i][j-1]
e += (yp-y[i]) ** 2
eq = []
for i in np.arange(len(self.w)):
eq.append(diff(e, self.w[f"w{i}"]))
w = solve(eq, list(self.w.keys()))
for i in np.arange(len(self.w)):
self.w[f"w{i}"] = w[self.w[f"w{i}"]]
def predict(self, x):
def prediction(features):
yp = 0
for i in np.arange(len(self.w)):
if i == 0:
yp += self.w[f"w{i}"]
else:
yp += self.w[f"w{i}"] * features[i-1]
return yp
return list(map(prediction, x))
Excellent explanation of the implementation of linear regression, thanks!!
Amazing video, looking forward for more implementations from scratch!
Amazing simple explanation of linear regression. Please also cover the other techniques in same way
It would have been nice to compare it to the analytical solution of least squares regression being (Xᵀ•X)⁻¹•(Xᵀ•Y) just to show they're identical
quicker and theoretically more correct for solving this problem!
@@andreagalloni92 could you make a video and share it with everyone please? (theory and python code)
@@JohnMacPherson-hr4yz mmm... Maybe I can :)
I'll try to do it
Yes, please do more videos like this one. Even Einstein gives it 2 thumbs up! :)
Next for logistic regression please!
Waiting for series of such videos 🤩
Bruder, danke dir. Es wollte nicht in meinen Kopf. Dank dir habe ich das verstanden.
This is a great video! Thank you so much!
on 10:00 we need to take derivative of each weight (M) like this, first for M1 then for M2 then M3.... to Mn
Thanks a bunch professor taught this in class I was like a deer with headlights coming, but after you video I have understanding.
your explanations are really clear and precise, thank you very much♥♥♥
You got a new subscriber... Best in detail explanation ever!!!
Combine theory with practice，and show the calculate procedure，really good👍
love the videos, finally
dude did my 6 hours lecture + lab class all in just 24 minutes, bruh. Ez Clap hahah, thanks a lot.
Brother! I simply love you after I came across this video!
Excellent video, although I will need to scrub up on my mathematics!! I think I am one of the few that loves looking at formulas and wonders; "What in the hell is going on there?" 😆😆
got a good idea about linear regression thnx bud!!
the math part as awesome! thanks a lot very clear and simple
Thank you so much man! Great vid...Keep up the good work!
I love this guy
well done
That was so much easier to understand. thank you.
Thank youuu :) Loved the way you explained it.
Lmao, I finished summer school for algebra 1 and we just learned this and to calculate it, we used a calculator this is a amazing video the mathematics where correct and your explanation is amazing!
awesome video and dark mode paint rocksss
yoo out all explanations i saw u did the best work this is what i wanted best work man subbed
Excellent explanation. Thanks
You’re a life saver thank you soooo much❤❤
Why does 1/n become -2/n. As I read in towards data science that it would be 1/n and then the -2 would be after the sum symbol as 2x'i' so like why or how does 1/n become -2/n. Also great video btw, helped me unlock a few skills in the ml skill tree :)
This is amazing!
How can I store the current m and b values for every iteration?
Awesome representation!!!
Nicely done!
this is the real algorithm. great :)
amazing crystal clear
Thanks a lot for great explanation!
great video NeuralNine
I feel this the gradiant descent. is not it ?
if It is, is there an implementation for the least square
because I feel it is just so random you take some values and you choose the least one from those random values not necessarily the least value you can get
Great video, very informative
Hi sir , it's really helps me to understand linear regression and build it by myself from scratch. Thanks 👍 can you describe all the models from scratch? Like it's
wow, that is exactly what is called simplicity
I dont understand the reasoning behind [m * x * b for x in range(20, 80)] why is this the y axis. What does it represent. Why the equation? Also why arent we just reading the found values of x and y? I have understood all the calculations being done but I dont understand the visualization/graph representation part. Please let me know if you see this.
He did it all wrong just ignore it imo
here to learn something that I already know but the video is too entertaining to click off
Great video!
loved it bro u r just amazing
If you're counting from 0 to n, it means you have (n+1) data points, so should the dividing factor not be 1/(n+1) while calculating mean of squared errors at 4:43?
where can we find the data you uploaded. I would be happy to try the code. thanks for making these videos.
Hi, I really like your videos. One question, what is h?
Thank you!
Nice video! Could you please provide the dataset that you used
yup
This was really helpful
Why the gradient descent method? The pseudo inverse solves this immediately
Make a video about it and link me, plz
Great explanation
Upload more model implementation plz
why did we do a partial derivative?
that's pretty good lecture.. 😍
Lstm ,rnn , logistics and more we are expecting more from you
Amazing video!!!!
LSTM from scratch! 🙏😍
Nice video as usual :).But I don't know calculus 😥
I am getting error
AttributeError Traceback (most recent call last)
Cell In[10], line 39
37 if i % 50 == 0:
38 print(f"Epoch: {i}")
---> 39 m, b = gradient_descent(m, b, data, L)
41 print(m, b)
43 plt.scatter(data.studytime, data.score, color = "black")
The flow should terminate on global minima right ?? Is this program complete ??
@NeuralNine How do I test the trained models to give a prediction based on a newly given value/independent variable (a value that is foreign to the training set)?
Great video btw!
Yes! This is a good question I hope he answers. If he already has can someone link me please? :)
Thanks man
perfect job
Which software you use for the video ?
You sound like tech with Tim😂😂😂
Where can we get the csv file he worked on?
Amazing explaining, thanks a lot!
Can you upload the csv file and send me the link, please ?
the defined lossfunction is not called anywhere ,why
You should not to "think" that it is a best line, you should verify it!
Thank you very much
amazing content
please provide the dataset which you use bcz it will us to follow along with you
Do Multiple Linear Regression!!!!!
What's the point of printing Epochs if we see nothing on the screen in that regard lol, why is it highly mathematical if we do not even derive anything? I would redo the video with loss_function being in the print, o/w it just hangs in the code for God knows what reason.
Hello, do you have a video or notes on plotting the linear regression when attributes in the input data points are more than one. Say 10 columns of x, and 1 column of y??
Please respond.
Thank you.
I too need explanation on this. It will be really helpful
@@sairekhaunnam3001 Hey, if there is one attribute the we can plot it on 2D, if two attributes, then in 3D, and for three attribute, we will plot in 4D, which is not possible visually. That's why we restrict ourselves.
Please do more such videos
bro how does the model created by these mathematical output and how does the predict function works
;
Can Anyone tell me some practical usecase or some example where we can implement this? It should be great if anyone can give me full scenario