Python is good for lots of things, but it is not the best for ML. What I would say is the King (or Queen) of ML is Julia, because it is based around matrices as it's core, and it was designed with ML in mind.
This! So much this. Pythons multi-dim arrays have, as you point out, odd syntax. For doing matrix math Numpy is excellent. And machine learning is all about matrix operations in practical applications. I've been primarily programming in C# since C# was a thing and just got into Python about a year ago. It's superior for many tasks and has a much more robust library eco-system. It's the flat-head screwdriver to C#'s phillip's. Get them both in your toolbox and then use the right one for the job.
Jabrils I wanted you to know that I've been following you for a year now and I can truly say you've been my number one inspiration for learning AI. I studied CS in college but we have never talked about AI. Thank you, and keep up the good work.
I think there is a lot of potential in relying on a voluntary crowd of your viewers to help sift through training data. It's like in fan fiction, where an author mentions in an author's note, that they need a beta reader or editor. And as a result, people volunteer to do so, free of charge. You just need to think of a system to make crowdsourcing work from fans, work. And make it robust against mischief makers. I like the idea of using a GANN to identify good quotes. But you'll need some training data to jumpstart even one of those. I think what I would do, is treat the training data problem like a physical particulate filtering problem, where your trying to get clean water, but super fine filters are expensive, and clog quickly. This means, you need a series of filters to do the filtering, progressing from a metal vegetable strainer, to a course metal mesh, to sand, to a fine metal mesh, to a coffee filter, to activated charcoal. In the case of your training data, the more obviously bad a quote is, equates to a larger piece of junk in the water filtering analogy. The really big filters are either dirt cheap, or can be rinsed off, and used indefinitely, and the ultra fine filters are rescued from extraneous clogging. So in conclusion: Use a series of filters, to eliminate the most obvious bad quotes first, and work your way down to least obvious, eventually giving it to a neural network of some type to do the final filtering.
well. Recording is the easiest part aha, for obvious reasons. & the editing time is scaled, depending how much time I have is how detailed I will make the animations / editing & such. For instance, I am missing A LOT of sound effects for this video, but you probably would of never known if I just kept my mouth shut 😉 - Jabril
Jabrils I know editing takes lots of time. But I don't care much for sfx, content and the narrative deliver it 👌 btw where's your background music from?
For the 3x3 multidimensional array, it’s like that because it’s way more common to write out an array literal than a multidimensional array, so it makes more sense to initialize the array literal [3, 3]. For multidimensional arrays, you’ll want to use numpy’s ndarray anyway.
Indeed, Numpy has the option to great multi-dimensional arrays without any issue, useful when making back-prop neural networks. The thing with Python is that it's probably the most powerful language you can use, but you need to know it very well to actually use it correct, and python does use a ton of external imports to do that as it misses many "basic" functions.
Also, even if you decide to use lists, the way you did doesn't give you what you want. Because: >>> a = [[]] * 3 >>> a[0].append(1) >>> a [[1], [1], [1]] When you use *, it copies the reference to object. So all elements change at the same time.
In vanilla python we can create something like node = [[0 for _ in range(3)] for _ in range(3)] to create an 3x3 list. With numpy we can create "lists" with the dimensions we want telling his "shape", like node = numpy.zeros((3, 3)). If you multiply a list you may copy his reference and think is three different lists while they are the same. Bit late but may be useful to someone sometime.
Jabrils I absolutely love your videos. I know nothing about coding, but you still keep it simple and super interesting! Oh, and the smooth jazz is oh so relaxing. Keep it up!
Superb content as always man! & u know what i learn the most from u? 3 things. Even without any formal education in com science, u can eventually be more skilled than the grads themselves due to 1) Always embarking on these creative projects which makes u to put ur skills into practice & be innovative, most of ur videos are about projects rather than the technical skills which is what companies look out for in a person eg "yes u have all these skills but can u show me what have u done with it?" 2) Identifying and rectifying mistakes, evry video u make will always start with a mistake or obstacle u face and how u overcome them, and lastly 3) how to get past a roadblock and literally take urself to the next level, eg how the vocab and math terms were unfamiliar to u but u went ahead to search for answers to understand better instead of "screw this shit this aint for me ill just skip past this part", a problem which 90% of us face when hit with unfamiliar stuffs especially when we r beginners.
Python doesn't do arrays natively. It does linked lists. If you want arrays, try numpy. That has the kinds of lists you might be thinking about when using syntax like that. node = [3, 3] # stores a list with the entries [3, 3] That's not dimensional initialization, it literally initializes a list with those values. You don't really need to initialize stuff in the first place though. I think. It kinda depends on what you're trying to do? I can't really gleam it off that. Literally want a 3x3 matrix in a list? Try this: [ [ ] for _ in range(3) ] that's a 3 x n "matrix". (Really a linked list with three empty linked lists that you then can fill with stuff) If you truly want a list with some data already filled in so it's literally a 3x3 matrix, try, for instance, this: [ [0 for _ in range(3)] for _ in range(3) ] # [[0, 0, 0], # [0, 0, 0], # [0, 0, 0]] But you probably want actual arrays anyway. So go with numpy: import numpy as np node = np.empty([3 , 3]) Which, besides the wrapper, is basically the exact syntax you were actually hoping for. And if you already know that it's a float array, you probably wanna do node = np.empty([3, 3], dtype=np.float64) or something. Though in general, Python really loves list comprehensions. Better learn to get used to those. I promise that, once you are, they are a huge help. (Initially they sure are confusing though)
For practical implementation: using packages and optimized could is usually always best if possible, you are much less likely to make mistakes. For learning and long term retention: Try to program from scratch the components that doable components (splitting data set, gradient descent, matrix representation of closed form solutions) and packages for the difficult components (convex optimization).
The "Don't reinvent the wheel" discussion sounds similar to the conversation me an you had about the creation of game engine. Without a doubt I agree I would suggest everyone at least once attempt to build whatever it is they wish to work on from scratch as the level of understanding it allows I just do not feel can be accomplished any other way. For instance, I had a grasp of using 3D engines and Game engines but it was not until I decided to make a simple one myself did I gain a much better understanding of everything that takes place inside of the engine and when going back to using premade ones allows me to structure and design things much better.
I wonder if you could write word equivalencies using synonyms to expand the vocab. Also, you could create an app like that "would you rather" app and have people check whether they like the quote or not; use that data to have the computer modify itself. It's a lot of work, but they're just some ideas.
Maybe you could try re-filtering your initial data set by giving each quote a score based on how many of the keywords you're looking for are in it? Then unfavourable quotes would be likely to recieve a much lower score, rather than everything left over being on an even playing field, and you could just take the highest scoring quotes. That could end up favouring longer quotes over shorter ones, but they'd at least be of higher quality.
"First, have the necessary means to achieve your ends; wisdom, money, materials, and methods, third, adjust all your means to that end if you can do, or dream you can, begin it. Boldness has genius, power and magic in it. Ask and the door will be open for you." YOU." - Jabrils ML Computer Software | 2018
To avoid word salad, you could parse the statements to make sure they all have a subject section, predicate section, and verb section. Wouldn't fix the data set, but the sentences it generated would make some sense. Even something like "You can be pineapple." makes a little sense.
In python lists you have to define dimensions with brackets, and you don’t have to define type, that’s why python lists are super slow, use numpy to create arrays which are much faster, search up a numpy tutorial and get the syntax down, then it’s pretty easy as you already have experience with arrays.
4:52 That absolutely makes sense. A multi-dimensional array is an array of arrays of arrays of arrays ... For example, a simple array is a one-dimensional array, an array of arrays is a two-dimensional array. One-dimensional array with values: arr = [1, 2, 3, 4] It's an array of integers. So arr[0] will give you an integer. Two-dimensional array with values: arr = [a, b] where a and b are arrays themselves: a = [1, 2] b = [3, 4] So you could also write arr = [[1, 2], [3, 4]] It's an array of integer arrays. So arr[0] will give you an array of integer arrays and arr[0][0] will give you an integer. And all this is not really special to python. When you see something like arr = [1]*3, then "*3" means you want 3 of those elements you specified in the last array. So [1]*3 will give you an array with 3 ones in it [1, 1, 1] and [[]]*3 will give you an array with 3 empty arrays in it [[], [], []] and [[1]*3]*3 will give you an array with 3 arrays in it which all have 3 ones in it [[1, 1, 1], [1, 1, 1], [1, 1, 1]] So what means [[[]]]*3? [something]*3 means you want an array with 3 somethings. So you already know you want three elements on the topmost/first dimension. "something" is [[]], which is an array with one empty array in it. So you have an array in an array in an array. That's what makes it three-dimensional. But there is just one array on the middle/second dimension, so at some point, new arrays have to be added in that dimension since an array with just one element in it doesn't make much sense. And the lowest/third dimension has neither any values or arrays in it, so they also have to be added later since an x-dimensional array without any values on the x-th dimension also doesn't make any sense. Let's say you want a three-dimensional array with the size 2x3x4 which is initialized with zeros. The lowest dimension would be [0]*2. It could also be *3 or *4, it only depends on where you need which size. Then you put that into the middle dimension [[0]*2]*3 And that goes into the topmost dimension [[[0]*2]*3]*4 Which gives you [[[0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0]]]
I have never been more inspired in my whole life: "Animals be goding 100 gasting and hall sever of bigmen righ of the door power is the decision to act, the rest is merely tenacity." -Jabrils
You do not need to use other libraries to do things like creating a matrix, " [[0 for i in range(3)] for j in range(3)] " will produce the same output. But if you are going to do machine learning in python, then you at least want some tools to do matrix operations, like calculation a dot product or transpose a matrix, which numpy can do (if you want an ML challenge and not just a programming challenge).
quick tip: you should give Teamviewer a try. It allows you to control one computer from another (or a phone), is totally free, works extremely well, and even has a file transfer tool built in! I recently started using it, and I don't know how I went without it!
How's fransisco? I've been there a year ago and saw a lot of crazy homeless(or not homeless) people it was exciting and funny xD be sure to check out the golden gate bridge it's really cool
3:39 Watch Siraj Raval videos, but with caution, you will not necessarily learn about Machine Learning, so it is better to use his videos as reference. Of course, this is a personal opinion, if you do learn by watching his videos, please continue to do so.
inspirobot.me/ says: 1. After the cleansing, comes the mistake. 2. You can prevent, that your dad gets torn apart by piranhas. 3. There are ordinary screens, and there are human screens.
Just a quick note on your python matrix complaints. Don't think of it as initializing an array using dimensions, think of it as creating lists inside lists. And, those lists are objects rather than fancy compiler stuff, so setting a variable to that object is just that object. Remember, in python, lists (and most data structures) can contain anything and those types can be mixed and matched willy nilly since the list just treats them all as generic objects. It's much more flexible than a compiled language because of this, but it is also a much different mindset than strictly defining everything and it's really easy to shoot yourself in the foot in very confusing ways. If you want good matrix operations or cleaner initialization, you're going to want a matrix library instead of using lists.
Cmon dude, [3,3] is just a list with two threes in it. As everybody here said, Numpy is what you should be using for this stuff. You should also really check out Pandas, it's a super useful library based on Numpy that will make opening/cleaning/standardizing databases for ML much faster and easier.
Did you use any semantic analysis to break sentences down by parts of speech before inputting it as training data? This is much more effective than character-level analysis. Google has their Natural Language service which is very good but there are also free options. If you don't already know about these I'd recommend looking into GloVe vectors, Systemic Functional Grammar and the Stanford Parser
You could have used a FastText classifier trained on the smaller data set of examples, to filter the larger data set. Would probably perform much better than the filters you used. Might need to grab a bunch of negative examples too, but that wouldn't be too hard.
BROOOOOO, I just started learning python too and last night was having the SAME issue that you were ranting about I'm so glad im not the only one (im coming from C++)
Omg C# is MasterRace! Python is the bane of my existence, and I just hate its syntax. Also, maybe you could accomplish something by creating a bunch of word lists under different categories (e.g nouns, adjectives, verbs) and then create templates combining these categories and have them randomly pick from each category's respective list to create quotes!
![The moment we stopped understanding AI [AlexNet]](http://i.ytimg.com/vi/UZDiGooFs54/mqdefault.jpg)
"Consider this: y" - hahahah thats so good
"Industry programmers, is this your king!?" I'm dying lol
Python is good for lots of things, but it is not the best for ML. What I would say is the King (or Queen) of ML is Julia, because it is based around matrices as it's core, and it was designed with ML in mind.
SAS is the best!
Just use a for loop to make a nested list
I realize I am pretty off topic but does anyone know a good site to stream newly released series online ?
@Malakai Ariel Flixportal =)
For your node matrixes... just import numpy and use something like np.zeros([3,3]) to create the 3x3 matrix shape
This! So much this. Pythons multi-dim arrays have, as you point out, odd syntax. For doing matrix math Numpy is excellent. And machine learning is all about matrix operations in practical applications. I've been primarily programming in C# since C# was a thing and just got into Python about a year ago. It's superior for many tasks and has a much more robust library eco-system. It's the flat-head screwdriver to C#'s phillip's. Get them both in your toolbox and then use the right one for the job.
Jabrils I wanted you to know that I've been following you for a year now and I can truly say you've been my number one inspiration for learning AI. I studied CS in college but we have never talked about AI. Thank you, and keep up the good work.
That's unfortunate Maor, but we are all life-long students! I wish you nothing but the best on your AI adventure 😁
- Jabril
This was really cool. Please make a part 2!
wow, those quotes were really inspirational.
"ask and the door will be opened for you".
I think there is a lot of potential in relying on a voluntary crowd of your viewers to help sift through training data. It's like in fan fiction, where an author mentions in an author's note, that they need a beta reader or editor. And as a result, people volunteer to do so, free of charge. You just need to think of a system to make crowdsourcing work from fans, work. And make it robust against mischief makers.
I like the idea of using a GANN to identify good quotes. But you'll need some training data to jumpstart even one of those.
I think what I would do, is treat the training data problem like a physical particulate filtering problem, where your trying to get clean water, but super fine filters are expensive, and clog quickly. This means, you need a series of filters to do the filtering, progressing from a metal vegetable strainer, to a course metal mesh, to sand, to a fine metal mesh, to a coffee filter, to activated charcoal. In the case of your training data, the more obviously bad a quote is, equates to a larger piece of junk in the water filtering analogy. The really big filters are either dirt cheap, or can be rinsed off, and used indefinitely, and the ultra fine filters are rescued from extraneous clogging. So in conclusion: Use a series of filters, to eliminate the most obvious bad quotes first, and work your way down to least obvious, eventually giving it to a neural network of some type to do the final filtering.
Me: "I just wish there were more inspirational quotes"
Jabrils: "Hold my ice cream"
i just love your narrative style!
How long does it take you to record and edit the videos?
well. Recording is the easiest part aha, for obvious reasons. & the editing time is scaled, depending how much time I have is how detailed I will make the animations / editing & such. For instance, I am missing A LOT of sound effects for this video, but you probably would of never known if I just kept my mouth shut 😉
- Jabril
Jabrils I know editing takes lots of time. But I don't care much for sfx, content and the narrative deliver it 👌
btw where's your background music from?
Thank man, & the score is from all over the place haha. Been building me library over the last some-10 years xD
- Jabril
Jabrils thanks for answering my questions! looking forward to part 2.
maybe we could do a collab sometime 😂
id be interested. Shoot a pitch to my email & let's see what's possible
- Jabril
You get better and better in Ai field
We are waiting for part 2, I love your channel
For the 3x3 multidimensional array, it’s like that because it’s way more common to write out an array literal than a multidimensional array, so it makes more sense to initialize the array literal [3, 3]. For multidimensional arrays, you’ll want to use numpy’s ndarray anyway.
Man, love your videos! Perfect balance between techie stuff, entertainment and charming presentation style!
That was not an array, it was a list! you wanna use arrays, import numpy
numpy huh? I shall look into it! 👍😀
- Jabril
Indeed, Numpy has the option to great multi-dimensional arrays without any issue, useful when making back-prop neural networks.
The thing with Python is that it's probably the most powerful language you can use, but you need to know it very well to actually use it correct, and python does use a ton of external imports to do that as it misses many "basic" functions.
This is exactly what I was going to comment ^_^
Was about to say the same haha. Numpy is king when it comes to matrix math in Python.
Also, even if you decide to use lists, the way you did doesn't give you what you want.
Because:
>>> a = [[]] * 3
>>> a[0].append(1)
>>> a
[[1], [1], [1]]
When you use *, it copies the reference to object. So all elements change at the same time.
In vanilla python we can create something like node = [[0 for _ in range(3)] for _ in range(3)] to create an 3x3 list.
With numpy we can create "lists" with the dimensions we want telling his "shape", like node = numpy.zeros((3, 3)).
If you multiply a list you may copy his reference and think is three different lists while they are the same.
Bit late but may be useful to someone sometime.
Man your videos are my favorite! Can't wait to see what else you come up with
Jabrils I absolutely love your videos. I know nothing about coding, but you still keep it simple and super interesting! Oh, and the smooth jazz is oh so relaxing. Keep it up!
I love this video so much! As a software developer starting my own channel on TH-cam, I really look up to your content as the gold standard
C# master race ftw! B)
my boy. 😏
- Jabril
Jabrils HAHA I was so happy when you said that!!!
Excel master race
@@andrepedersen1147 notepad master race
Butterflies!
Superb content as always man! & u know what i learn the most from u? 3 things. Even without any formal education in com science, u can eventually be more skilled than the grads themselves due to 1) Always embarking on these creative projects which makes u to put ur skills into practice & be innovative, most of ur videos are about projects rather than the technical skills which is what companies look out for in a person eg "yes u have all these skills but can u show me what have u done with it?" 2) Identifying and rectifying mistakes, evry video u make will always start with a mistake or obstacle u face and how u overcome them, and lastly 3) how to get past a roadblock and literally take urself to the next level, eg how the vocab and math terms were unfamiliar to u but u went ahead to search for answers to understand better instead of "screw this shit this aint for me ill just skip past this part", a problem which 90% of us face when hit with unfamiliar stuffs especially when we r beginners.
that being said, this channel deserve way more subs views and evrything man!!
Thank you for coming into my feed. I was looking at getting into coding and after a few of your videos i have a direction to go
Hey Jabrils! as always, love the content! Thank you man!
Python doesn't do arrays natively. It does linked lists. If you want arrays, try numpy. That has the kinds of lists you might be thinking about when using syntax like that.
node = [3, 3] # stores a list with the entries [3, 3]
That's not dimensional initialization, it literally initializes a list with those values.
You don't really need to initialize stuff in the first place though. I think. It kinda depends on what you're trying to do? I can't really gleam it off that. Literally want a 3x3 matrix in a list? Try this:
[ [ ] for _ in range(3) ]
that's a 3 x n "matrix". (Really a linked list with three empty linked lists that you then can fill with stuff)
If you truly want a list with some data already filled in so it's literally a 3x3 matrix, try, for instance, this:
[ [0 for _ in range(3)] for _ in range(3) ]
# [[0, 0, 0],
# [0, 0, 0],
# [0, 0, 0]]
But you probably want actual arrays anyway. So go with numpy:
import numpy as np
node = np.empty([3 , 3])
Which, besides the wrapper, is basically the exact syntax you were actually hoping for. And if you already know that it's a float array, you probably wanna do
node = np.empty([3, 3], dtype=np.float64)
or something.
Though in general, Python really loves list comprehensions. Better learn to get used to those. I promise that, once you are, they are a huge help. (Initially they sure are confusing though)
For practical implementation: using packages and optimized could is usually always best if possible, you are much less likely to make mistakes.
For learning and long term retention: Try to program from scratch the components that doable components (splitting data set, gradient descent, matrix representation of closed form solutions) and packages for the difficult components (convex optimization).
Great work!!! Like the technical content that are you putting on! Thanks
"Animals be more dissapointed by the things that you didn't do than the ones you did do" sounds like a normal quote
Love your channel keep up the great work brother wishing you success and happiness
The "Don't reinvent the wheel" discussion sounds similar to the conversation me an you had about the creation of game engine. Without a doubt I agree I would suggest everyone at least once attempt to build whatever it is they wish to work on from scratch as the level of understanding it allows I just do not feel can be accomplished any other way.
For instance, I had a grasp of using 3D engines and Game engines but it was not until I decided to make a simple one myself did I gain a much better understanding of everything that takes place inside of the engine and when going back to using premade ones allows me to structure and design things much better.
I wonder if you could write word equivalencies using synonyms to expand the vocab. Also, you could create an app like that "would you rather" app and have people check whether they like the quote or not; use that data to have the computer modify itself. It's a lot of work, but they're just some ideas.
I know its not the point, but there are some great chrome extensions with inspirational quotes in the new tab page which I love.
8:02 "Consider this: Y"
Maybe you could try re-filtering your initial data set by giving each quote a score based on how many of the keywords you're looking for are in it?
Then unfavourable quotes would be likely to recieve a much lower score, rather than everything left over being on an even playing field, and you could just take the highest scoring quotes.
That could end up favouring longer quotes over shorter ones, but they'd at least be of higher quality.
Yay another video, I'm way excited
C# Master Race all the way bro. My favorite language I've learned so far.
"Consider this: Y"
Mind... fucking... blown...
"I'm working it" sounds like a good one for you man.
13:44 “we’re going to enslave our student” *right after talking about the teacher and student 😂🤣I was not expecting that
I live for Jabril's foods and talking without moving his mouth
"First, have the necessary means to achieve your ends; wisdom, money, materials, and methods, third, adjust all your means to that end if you can do, or dream you can, begin it. Boldness has genius, power and magic in it. Ask and the door will be open for you."
YOU."
- Jabrils ML Computer Software | 2018
the program has spoken "animals gen it is not enough; we must do." 12:08
For your matrices, you can use list comprehensions.
Make an A.I that bullies me everytime I feel good about myself.
Gotta stay humble
Awesome as always!!
The thumbnail is AWESOME!
To avoid word salad, you could parse the statements to make sure they all have a subject section, predicate section, and verb section. Wouldn't fix the data set, but the sentences it generated would make some sense.
Even something like "You can be pineapple." makes a little sense.
I love these chill layed back videos👌🏽👌🏽👌🏽
In python lists you have to define dimensions with brackets, and you don’t have to define type, that’s why python lists are super slow, use numpy to create arrays which are much faster, search up a numpy tutorial and get the syntax down, then it’s pretty easy as you already have experience with arrays.
import numpy as np
node = np.zeros( (3,3) )
4:52 That absolutely makes sense.
A multi-dimensional array is an array of arrays of arrays of arrays ... For example, a simple array is a one-dimensional array, an array of arrays is a two-dimensional array.
One-dimensional array with values: arr = [1, 2, 3, 4]
It's an array of integers. So arr[0] will give you an integer.
Two-dimensional array with values: arr = [a, b]
where a and b are arrays themselves:
a = [1, 2]
b = [3, 4]
So you could also write arr = [[1, 2], [3, 4]]
It's an array of integer arrays. So arr[0] will give you an array of integer arrays and arr[0][0] will give you an integer.
And all this is not really special to python.
When you see something like arr = [1]*3, then "*3" means you want 3 of those elements you specified in the last array.
So [1]*3 will give you an array with 3 ones in it [1, 1, 1]
and [[]]*3 will give you an array with 3 empty arrays in it [[], [], []]
and [[1]*3]*3 will give you an array with 3 arrays in it which all have 3 ones in it [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
So what means [[[]]]*3?
[something]*3 means you want an array with 3 somethings. So you already know you want three elements on the topmost/first dimension.
"something" is [[]], which is an array with one empty array in it. So you have an array in an array in an array. That's what makes it three-dimensional. But there is just one array on the middle/second dimension, so at some point, new arrays have to be added in that dimension since an array with just one element in it doesn't make much sense. And the lowest/third dimension has neither any values or arrays in it, so they also have to be added later since an x-dimensional array without any values on the x-th dimension also doesn't make any sense.
Let's say you want a three-dimensional array with the size 2x3x4 which is initialized with zeros.
The lowest dimension would be [0]*2. It could also be *3 or *4, it only depends on where you need which size.
Then you put that into the middle dimension [[0]*2]*3
And that goes into the topmost dimension [[[0]*2]*3]*4
Which gives you [[[0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0]]]
I definitely read this
Awesome Jabrils:)👏👏👏✔
Consider this: Y
It learned existentialism
I’m still wondering why ur always eating food in each cut
Yano_ well he's not a machine himself. He has to eat at some point :P
That's his thing. he loves candy, ice cream, and snacks.
food tastes good.
That's understandable, but why is he aways eating candy and *not getting fat*‽
Nah, he's a machine designed to write other machines and eat candy. Come on, it's so obvious.
his acting is so cool. you forget what he is talking about. and forget everything he said.
I have never been more inspired in my whole life: "Animals be goding 100 gasting and hall sever of bigmen righ of the door power is the decision to act, the rest is merely tenacity." -Jabrils
Part2 Part2 Part2 Part2 Part2 pleaaaaase!
We use numpy for that
>>> import numpy as np
>>> np.zeros((3,3))
array([[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.]])
You do not need to use other libraries to do things like creating a matrix, " [[0 for i in range(3)] for j in range(3)] " will produce the same output. But if you are going to do machine learning in python, then you at least want some tools to do matrix operations, like calculation a dot product or transpose a matrix, which numpy can do (if you want an ML challenge and not just a programming challenge).
That 3x3 array thing has me gigglin'!!!
quick tip: you should give Teamviewer a try. It allows you to control one computer from another (or a phone), is totally free, works extremely well, and even has a file transfer tool built in! I recently started using it, and I don't know how I went without it!
IN THE CHAT FOR THE NEXT HOUR! YO!
Jabrils cool
Ey
How's fransisco? I've been there a year ago and saw a lot of crazy homeless(or not homeless) people it was exciting and funny xD
be sure to check out the golden gate bridge it's really cool
Jabrils looking forward for part 2
Jabrils sounds like a candy brand for maybe gummy dreadlocks 😂 I'd be so down to buy a pack
I WANT PART 2 FROM EVERYTHING YOU DO. Yes these are caps letter.
NumPy is fundamental for machine learning with python. Use it to deal with multi dimensional arrays (matrices) operations
3:39 Watch Siraj Raval videos, but with caution, you will not necessarily learn about Machine Learning, so it is better to use his videos as reference.
Of course, this is a personal opinion, if you do learn by watching his videos, please continue to do so.
You should use numpy then working with matrices/machine learning. It is implemented in c/c++, so it is faster than using list :)
Might as well start Vlog can't wait to watch themmmmm 🤩
Dammit Jabrils your profile pic is adorable you da best!!!
inspirobot.me/ says:
1. After the cleansing, comes the mistake.
2. You can prevent, that your dad gets torn apart by piranhas.
3. There are ordinary screens, and there are human screens.
I literally live for the sound of you saying, "Yo guys!"
Great video, you got me sucked into Machine Learning!
Karolade wait until the white hole spits you out into a colorful nebula.
Just a quick note on your python matrix complaints. Don't think of it as initializing an array using dimensions, think of it as creating lists inside lists. And, those lists are objects rather than fancy compiler stuff, so setting a variable to that object is just that object.
Remember, in python, lists (and most data structures) can contain anything and those types can be mixed and matched willy nilly since the list just treats them all as generic objects. It's much more flexible than a compiled language because of this, but it is also a much different mindset than strictly defining everything and it's really easy to shoot yourself in the foot in very confusing ways.
If you want good matrix operations or cleaner initialization, you're going to want a matrix library instead of using lists.
2:40 this one is actually good! "age is an issue of mind over matter. If you don't mind it doesn't matter"
Quite funny too
uh oh, computer's going to jail
watch out for overfitting ;) that happened on my numpy LSTM from scratch when I used a small dataset
lol that black panther quote xD
Cmon dude, [3,3] is just a list with two threes in it. As everybody here said, Numpy is what you should be using for this stuff. You should also really check out Pandas, it's a super useful library based on Numpy that will make opening/cleaning/standardizing databases for ML much faster and easier.
You’re an inspiration dude 🤟🏼
Plsss make videos consistently buzz I wait everyday for videos!!!!!
‘C# master race’ most inspirational quote
Did you use any semantic analysis to break sentences down by parts of speech before inputting it as training data? This is much more effective than character-level analysis. Google has their Natural Language service which is very good but there are also free options. If you don't already know about these I'd recommend looking into GloVe vectors, Systemic Functional Grammar and the Stanford Parser
When you just have 75,000 random quotes on your computer
You're the fricken best. I love you.
"I'M WORKING IT."
YASSS WERK
An upload?! Christmas isn't here yet! Did time speed up?!
Woa that last animation got dark fast.
5:22 I thought u were gonna say C++ , but C# is kool too
Why don't you use Numpy for arrays in python
because I still newbs 😢
- Jabril
Jabrils consider it!! It's very simple and written in c++ so through vectorization you can speed up your python code like crazy
You could have used a FastText classifier trained on the smaller data set of examples, to filter the larger data set. Would probably perform much better than the filters you used.
Might need to grab a bunch of negative examples too, but that wouldn't be too hard.
Cool video. I want to make videos like you but in algo finance world. Creating trading robots. Good Work Jabril!
You should check out Inspirobot. It's basically this exact project, but in a more... let's say "finished" form. :P
Use numpy for matrices and not default lists
you might be able to check for two or more of the positive/negative words in the sentences.
you should use Pandas/Numpy for those array operations.
About the python aray, node = [3, 3], you're saying the node is equal to the array [3, 3]. If you want to create matrix, I recommend using numpy
Would also love a part 2 and some GAN-Action!
INDUSTRY PROGRAMMERS, IS THIS YOUR KING?
I had no idea that Google Duplex was a thing. That's pretty dope.
maybe if you hypertrain it, you get it to tell you The One Inspirational saying to rule them all. Ex.: "All you need is what you got"
BROOOOOO, I just started learning python too and last night was having the SAME issue that you were ranting about I'm so glad im not the only one (im coming from C++)
*Did someone say C#*
This is a pretty good one lmao "Consider this: Y"
That quote about the history channel is inspirational. Go make history, don’t watch history make you.🤔
So did you use c# to write that and the run forrest program? im new and am actually taking c# courses, im thrilled to learn you use it too!
you should do a turing test with inspirational quotes
Omg C# is MasterRace! Python is the bane of my existence, and I just hate its syntax.
Also, maybe you could accomplish something by creating a bunch of word lists under different categories (e.g nouns, adjectives, verbs) and then create templates combining these categories and have them randomly pick from each category's respective list to create quotes!