+Animationix - HORROR ANIMATED STORIES that's great to hear! It's really tough trying to straddle a balanced line with this series so thanks for sharing that. - Jabril
The dog example was positive punishment not negative reinforcement by the way. Basically reinforcement encourages a behavior, punishment discourages a behavior. Positive gives something, like giving a treating or scolding, whereas negative removes something, like eating to remove hunger or taking away a kid's toys.
4:32 Looks simple enough, Positive/Negative Reinforcement is the act of doing what you described to make the dog continue a behavior, or improve apon it, where as Positive/Negative Punishment is the act of doing what you described to make the dog _stop_ a behavior. Quality content btw! Subbed!
If it helps anyone (given how late I am here) the point of that psych chart is that positive vs negative isn't about good or bad things but rather about giving vs taking away. In the example where you give the dog a treat, that's obviously positive reinforcement because you're giving the dog a good thing. But when you punish the dog by yelling at it for peeing on the carpet, you are also "giving" it a stimulus, it's just now a bad stimulation instead of good. Negative reinforcement is like punishing a kid by taking away their xbox. You are removing a thing they like, instead of adding something they don't like to the equation.
Negative punishment :). Negative/positive refers to adding or taking away something and reinforcement/punishment refers to whether the target likes it. In your case it's taking away the house the dog likes... Or adding yelling at the dog... Really both :). I learned a lot. Thanks for making the video
Loving the videos! Just a note with regards to the use of positive/negative reinforcement. The positive/negative part refers to a stimulus being added or removed, whereas the reinforcement/punishment refers to whether the outcome is an increased or decreased likelihood of the behaviour being repeated in the future. Feeding a dog when it does something you like is positive reinforcement, because you are adding something (the treat/taste) and you are doing it to reinforce the behaviour so it will happen more often. Hitting a dog when it's doing something you don't want is positive punishment, because you are adding something (pain) and you are doing it to diminish a behaviour so it will happen less often. Putting a dog in a timeout when it pees on the carpet is negative punishment, because you are taking away something (being free to roam) and you are doing it to diminish a behaviour so it will happen less often. If you're wanting to teach your dog to bark on cue, pinching it on the ear in order for it to yelp and letting go when it does is negative reinforcement, because you are taking away something (pain) and you are doing it to reinforce the behaviour so it will happen more often. So when you're doing reinforcement training, you're always doing positive reinforcement, but then you're doing positive punishment (or no punishment at all if you only reward good emergent behaviour and ignore bad behaviour).
I want to thank you for creating these videos and google for bringing them into my awareness. I'm a psych/linguistics major who knows a thing or two about programming and genetics and your little project here has given me an idea for a project of my own. Thank you for sharing this.
I'm a visual learner as well. I listen to the theory channels and get very little as they do not show examples for me to visualize each step. "A picture is worth a thousand words."
After going through the process of learning how to do all of this, what would you say are the absolute minimum prerequisites for getting into machine learning?
this reminds me of WWII German Enigma cipher machine. Poles and Brits built boxes to help predict the unknown "black box" part and use them to help guess the next day's radio traffic.
This guy is so interesting and everyone can clearly see the effort and time he puts into making these videos... Even if he is showing us what he does.. He is practically teaching us a lot of valuable info as well... Definitely deserves more subs!! Great human being (never met him, but you can make a judgment based on someones video)
@Jabrils dude, I learned neural networks the hard way too, but your videos VERY WELL explain every key aspect of neural networks. And you are right, that hierarchy chart you made is absolutely 👌🏼 beautiful. Great work man. Stay on! It's content like this that grows the community in knowledge and understanding, and in turn, in curiosity and interest in these developing fields.
I actually had a 3 credit subject about machine learning in college but only got explained gradient descent so your explanation about genetic algorithms was very helpful
Negative reinforcement is REMOVAL of a negative stimulus. Imagine I'm constantly spraying your face with cold water (Negative stimulus), I keep doing it until you touch your knees with your hands(action I want done). You realize that touching your knees stops me from spraying on your face. You constantly touch your knees. You doing the action makes me stop giving you a negative - negative reinforcement. Punishment(What you are talking about) is giving somebody a negative when they do something wrong. Like, giving Forrest a negative point when he goes right instead of left. I'm bored so I typed this out. Apparently, this is one of the most confused pairs in psychology. Great video btw.
Awesome to see that there is a channel which can be categorized under machine learning AND entertainment - you gamify education. In my opinion, you definitely should be a guest lecturer in some university which teaches machine learning!
Dude! I've seen a lot of ML/AI instructors (including training in person with Andrew Glassner) and I have to tell you: you're an awesome guide! Please keep nourishing that crazy fun gift that you have of syntax and simplification. I just wanted to take a pause and say: I appreciate you brother. This.. is.. sweet as!
First video I watched of your channel and I must say that it's awesome. Subscribed. I really appreciate that you pointed out to other resources and channels. Keep up all the good work!
I leave myself on unsupervised learning mode while watching your video and after finishing it I do supervised learning and test my self with test cases (other youtube channels)
Jabrils, very impressive explanation of neural networks and genetic algorithm processing! You can teach PhD Computer Science now! I've never even read anything as clear on these topics as your witty narrative!
I know this is an old video but I thought I'd shed some light on negative reinforcement and punishment. Positive reinforcement - eg. Giving a treat when doing something correctly Negative reinforcement - eg. Constantly annoying someone until they correct their action, IE your car beeping at you for not having a seat belt on. Punishment - eg. Actually performing a negative stimulus when doing something incorrectly. The difference between negative and punishment is you are actually rewarding them for making a correct choice. The reward is just you stopping something bad. Unlike punishment which focuses on targeting bad behavior. My wife taught a class on this for child upbringing but people got confused and thought the word "negative" just meant what you shouldn't do as a parent. But positive/negative has nothing to do with the morality of the reinforcement. Just the type. There are immoral ways to do any of the three training methods. For example, giving your child cocaine when they do something right is technically positive reinforcement, but obviously morally wrong. I didn't learn about specifically negative and positive punishment but imagine it is similar yet opposite.
I'm getting into python now. Iike how you are newer like me, I normaly cant learn from the coders who try and teach and end up having to read. With your vids your explaining it slowly enough to understand thanks!
Hey.... part 1 came up in my feed and I'm really enjoying the series so far... You mentioned that you managed to get to a decent understanding of AI/neutral networks/ML in about 6 months! Would you mind sharing the learning pathway ie the topics and resources you used? I'm in a similar situation as you were in. My math is a bit rough and I know I need to fast track that in order to get started... Any help would be appreciated. Thanks man!
another thing, biology is actually geometric shapes that iterlocks into pattern. that is what it does. its the same with dna as with all the other stuff in the cell. its signal molecules that fits receptors. a geometric shape that fits a geometric shape of a hole just like baby toys. dna does the same. its just a string of receptors that holds signal molecules in place. each signal molecule fit its spot in the string of dna as perfect fit. when scientists cut and paste dna, they actually cut both receptor and signal had hack it the hard way. that is why the dna repair molecules dont always manage to fuse the separated dna string back into the host string. the dna mocleules had ezymes that can glue two strings of dna into one. they also have cutting enzyme. when the ezymes read dna they take off the signal molecules of the receptors on the string and then copy them and put the originals back again. it means that there is big constructor molecules that build new molecules from other molecules. the point is that the secret of dna and biology is geometric iterlocking. its like a puzzle but with geometry instead of those weird pieces we use to play with. if we look at a sorting enzyme, it does the same. it have a rotor turning a ring containg receptors designed to fit certain molecules that act as a pumb. its the same as the axons and dentries. its signal molecule that fit receptor. its geometric interlocking. the holy grail of biology is geometric interlocking and signal shape of molecule fit receptor shape of molecule. biology is written in in geometric shape fitting.
Two things i love about your videos mano: The way you explain things and honestly... You have a great personality! you make this stuff look very fun to learn! keep it up man! Subbed!
This is awesome man. You break things down super well, and you're adding humor in the video. You make me want to pursue machine learning. Keep up the awesome work and thanks for the great video! :)
Your solution requires four inputs and several hours of training to complete it's goal. My solution is four inputs, a timer (to calculate the change over time of the inputs) and a nifty formula.
i have a idea. what about making the learning rate a variable and then use the difference between the most successful path and the one some itterations before to adjust the learning rate. perhaps that would make a lazy way of doing things faster? if the program can adjust its own fitness underway, it chould find its own most optimized value by itself. we could also train a neural net to learn from the neural net used to do the path finding to imporve the way the path finder neural net works by adjusting learning rate by interprating the pathfinder network numbers and make the other network adjust the path finder network learning rate acordingly. this way you have two brain working together to solve problem faster.
You are pure genius! My friend sent me this video as we are both on a nurture.fast.ai course and I'm in over my head as a complete novice. This video helped me to understand the concepts more clearly. Definitely watching the rest of your videos - :-)
+Ms888 B whoa whoa watch it on that genius word haha. But glad to help, i've got some products launching that might help with your AI adventure! - Jabril
ha ha credit where credit is due :-) -look forward to seeing your other AI videos. I have also sent links to my 12 yr old nephew....your videos are great for kids into computers....
Nice video my dude, Im working on something very similar myself only that each player is basically a collection of neural networks that must work together to stimulate movement. Each cell which are tethered to each other through muscles have their own small NN which can trigger movement and contraction of the distance between the cells. Friction and other physics on the cells cause movement.
Positive reinforcement is the act of giving something to encourage a behavior while negative reinforcement takes away something to encourage a behavior. The second example with the dog is negative and positive punishment, since you are not only adding yelling, but also taking away the dog's right to being in the house.
Yo I just discovered your channel and it is amazing. I love the way that you build your videos, the visuals and most of all your sense of humor! Keep going man, I'll show your stuff to my friends
Just finished those two videos and now heading for the next two. Cool videos loved them as well as your editing. Well done 👌🏻 Thanks for the cool videos and keep it up man!
Your video series is awesome man! I don't understand everything but i really do appreciate the efford you put into your videos. seems like a looooot of work... Thanks, man!
thank you so much! ive been learning a lot thanks to you. pitty youtube never pointed me to you when i was actively searching for NN stuff. thanks again!
Wowowow hold up here for a minute. I kindly want to point out that supervised / unsupervised / reinforcement are NOT training techniques. These words are used to describe the data that you stuff into the network. The "learning" bit is added to describe the class of problem you are facing if your data is of a certain type. "Reinforcement learning" for example means "Training [a neural net] on sequences of data that is coming in one sample at a time in order to find behavior that maximizes the reward obtained". And "gradient descent" is not a special algorithm. It instead describes how weights are changed in order to get better: 1) you evaluate your network to see how good it currently is 2) you do some math (compute the gradient) to figure out how to change each weight to increase performance (bigger or smaller weight) 3) you change the weights (make em bigger or smaller) and repeat. This "gradient descent" is used _very_ often and is certainly not exclusive to supervised learning. Q-learning for example also uses gradient descent to update the weights of the network. (And so do methods used to train on unlabeled data [unsupervised learning])
I was wondering if this could go better if instead of controlling Forrest directly you would just create an NN to place way-points that Forrest could just follow using a path finder.
He explained that in part 1. He said he is too lazy to place markers all over the course and by doing that forrest would only master one particular route for the whole course
I finally understand why his lips don’t move while speaking, as he said before, he’s lazy so why move your lips when you don’t need to
Fenderson _ thanks Einstein
@Fenderson _ *SPEECH 100*
@Fenderson _ r/wooooosh
@Fenderson _ He has a Forest for that
It’s different, brings attention to his video and makes it entertaining to watch
I ran across your channel and I'm loving this series even though I know nothing about it,
+Animationix - HORROR ANIMATED STORIES that's great to hear! It's really tough trying to straddle a balanced line with this series so thanks for sharing that.
- Jabril
same lol
@jabrils Likewise, learning a lot even though this is not my field of study. Great stuff man. Looking forward to more content.
I have no idea what he's saying about but I love watching his videos because it's mesmerizing for me
10:55 did you just google google? Im going to pretend I didnt see that
Wait, isn't that normal... to please the overlord Google one must make Google the most searched name?
PistachioMan lol saw it too and went to the coments imeadiatly
Please go to a dictionary first. Thank you
I think that was intentional on his part.
it's like looking for the word dictionary in dictionary
This is quality content
An indian guy yeah
this dude's video editing + storytelling skills are on fire! (besides the AI/ML creation thingy)
This guy needs more subs. Please help recommending the channel to others, so that the YT algorithms can pick it up more!
+Miro Keller miro. I love you. If I meet you. I'm going to buy you a pizza slice.
- Jabril
14:37 *Thanks* for saying _"and gals."_ I feel welcomed!
Amazing set of videos, loving it!!!
Luis, greatly honored to have check out my ML project! Thanks again for your great contributions to the space!
- Jabril
My head hurts. Great episode.
+Bbmin7b5 much love bbmin. 😎
- Jabril
nub
says the guy who's avatar is Python
@@markhollas7585 Just wanted to say that
4:13 that lady totally grabbed that dogs junk 🤢🤣 Great series man!
THIS GUY HAS TAUGHT ME MORE ABOUT NEURAL NETWORKS IN TWO EPISODES THAN MY WHOLE FKIN 4 YEAR COMPUTER SCIENCE DEGREE. HAIL JABRILS!
Lmao I love your sense of humor and your style of editing. There's a bit of novelty to everything without trying too hard. Good stuff man!
The quick brown fox jumped over the lazy dog!😂😂😂
The dog example was positive punishment not negative reinforcement by the way. Basically reinforcement encourages a behavior, punishment discourages a behavior. Positive gives something, like giving a treating or scolding, whereas negative removes something, like eating to remove hunger or taking away a kid's toys.
4:32 Looks simple enough, Positive/Negative Reinforcement is the act of doing what you described to make the dog continue a behavior, or improve apon it, where as Positive/Negative Punishment is the act of doing what you described to make the dog _stop_ a behavior.
Quality content btw! Subbed!
Fascinating! Love it, dude. Looking forward to part 3.
Thank you David. Much love happy holidays!
- Jabril
reinforcement: encouraging a behavior
punishment: discouraging a behavior
positive: giving something
negative: taking away something
If it helps anyone (given how late I am here) the point of that psych chart is that positive vs negative isn't about good or bad things but rather about giving vs taking away. In the example where you give the dog a treat, that's obviously positive reinforcement because you're giving the dog a good thing. But when you punish the dog by yelling at it for peeing on the carpet, you are also "giving" it a stimulus, it's just now a bad stimulation instead of good. Negative reinforcement is like punishing a kid by taking away their xbox. You are removing a thing they like, instead of adding something they don't like to the equation.
I like how you googled "google" 😂
AND he didn't break the internet 🤔
+almothana I swear no one knows about the search benefits that are enabled when you do that
- Jabril
@@veosotano Someone at google turned it off and on again real quick
@@asharbabar2706 ac electricity
Negative punishment :). Negative/positive refers to adding or taking away something and reinforcement/punishment refers to whether the target likes it. In your case it's taking away the house the dog likes... Or adding yelling at the dog... Really both :). I learned a lot. Thanks for making the video
Loving the videos!
Just a note with regards to the use of positive/negative reinforcement. The positive/negative part refers to a stimulus being added or removed, whereas the reinforcement/punishment refers to whether the outcome is an increased or decreased likelihood of the behaviour being repeated in the future.
Feeding a dog when it does something you like is positive reinforcement, because you are adding something (the treat/taste) and you are doing it to reinforce the behaviour so it will happen more often.
Hitting a dog when it's doing something you don't want is positive punishment, because you are adding something (pain) and you are doing it to diminish a behaviour so it will happen less often.
Putting a dog in a timeout when it pees on the carpet is negative punishment, because you are taking away something (being free to roam) and you are doing it to diminish a behaviour so it will happen less often.
If you're wanting to teach your dog to bark on cue, pinching it on the ear in order for it to yelp and letting go when it does is negative reinforcement, because you are taking away something (pain) and you are doing it to reinforce the behaviour so it will happen more often.
So when you're doing reinforcement training, you're always doing positive reinforcement, but then you're doing positive punishment (or no punishment at all if you only reward good emergent behaviour and ignore bad behaviour).
I want to thank you for creating these videos and google for bringing them into my awareness. I'm a psych/linguistics major who knows a thing or two about programming and genetics and your little project here has given me an idea for a project of my own. Thank you for sharing this.
These are LITERALLY the GREATEST videos I have EVER WATCHED
I absolutely love how you infuse humor to teach an otherwise difficult-to-understand concept. Bravo!
wow, somehow you have managed to turn a incredibly complicated process into something from "machine learning for dummies" good job
"so that i dont have to do work", "im lazy"
*does shit ton of work to be lazy*
Could relate
balanced, as all things should be.
Its an investment so his computer can do this for the rest of his life
programmers and engineers in a nutshell
If you are programing all that and also doing a job and also preparing youtube videos... you are a super genius!!
I'm a visual learner as well. I listen to the theory channels and get very little as they do not show examples for me to visualize each step. "A picture is worth a thousand words."
I love your inner monologue style of presenting!
5:26 Gradient descent is typically applied in Reinforcement Learning/Deep Q L:earning
Example (See paragraph 4 in introduction)::
www.cs.toronto.edu/~vmnih/docs/dqn.pdf
Awesome video. I like how you are showing the big picture and what categories some of the buzzwords of the field fit into.
After going through the process of learning how to do all of this, what would you say are the absolute minimum prerequisites for getting into machine learning?
I'm learning so much from this! I really want to get into this now as soon as I finish my school project :DD
this reminds me of WWII German Enigma cipher machine. Poles and Brits built boxes to help predict the unknown "black box" part and use them to help guess the next day's radio traffic.
The way you delivered is extraordinary!
This guy is so interesting and everyone can clearly see the effort and time he puts into making these videos... Even if he is showing us what he does.. He is practically teaching us a lot of valuable info as well... Definitely deserves more subs!! Great human being (never met him, but you can make a judgment based on someones video)
Your thought processies are amazing
@Jabrils dude, I learned neural networks the hard way too, but your videos VERY WELL explain every key aspect of neural networks. And you are right, that hierarchy chart you made is absolutely 👌🏼 beautiful. Great work man. Stay on! It's content like this that grows the community in knowledge and understanding, and in turn, in curiosity and interest in these developing fields.
Started coding at 12, laughed heartily 7:00 & Keep this rolling, Love it.
I actually had a 3 credit subject about machine learning in college but only got explained gradient descent so your explanation about genetic algorithms was very helpful
Negative reinforcement is REMOVAL of a negative stimulus. Imagine I'm constantly spraying your face with cold water (Negative stimulus), I keep doing it until you touch your knees with your hands(action I want done). You realize that touching your knees stops me from spraying on your face. You constantly touch your knees. You doing the action makes me stop giving you a negative - negative reinforcement.
Punishment(What you are talking about) is giving somebody a negative when they do something wrong. Like, giving Forrest a negative point when he goes right instead of left.
I'm bored so I typed this out. Apparently, this is one of the most confused pairs in psychology. Great video btw.
3Blue1Brown is probably the greatest math channel i've ever seen on TH-cam.
Awesome to see that there is a channel which can be categorized under machine learning AND entertainment - you gamify education. In my opinion, you definitely should be a guest lecturer in some university which teaches machine learning!
Not the quick brown fox😂😂😂😂
Dude! I've seen a lot of ML/AI instructors (including training in person with Andrew Glassner) and I have to tell you: you're an awesome guide!
Please keep nourishing that crazy fun gift that you have of syntax and simplification.
I just wanted to take a pause and say: I appreciate you brother.
This.. is.. sweet as!
First video I watched of your channel and I must say that it's awesome. Subscribed. I really appreciate that you pointed out to other resources and channels. Keep up all the good work!
Thank you Hadrhune, part 3 just went live & part 4 will be out next week! I hope that you enjoy them :)
- Jabril
I leave myself on unsupervised learning mode while watching your video and after finishing it I do supervised learning and test my self with test cases (other youtube channels)
Jesus, what an entertaining and joyful learning video! Great work!
Jabrils, very impressive explanation of neural networks and genetic algorithm processing! You can teach PhD Computer Science now! I've never even read anything as clear on these topics as your witty narrative!
Hahahaha, I love those "wall-hitting" sounds.. "UTSCH.. WASTED"
I know this is an old video but I thought I'd shed some light on negative reinforcement and punishment.
Positive reinforcement - eg. Giving a treat when doing something correctly
Negative reinforcement - eg. Constantly annoying someone until they correct their action, IE your car beeping at you for not having a seat belt on.
Punishment - eg. Actually performing a negative stimulus when doing something incorrectly.
The difference between negative and punishment is you are actually rewarding them for making a correct choice. The reward is just you stopping something bad. Unlike punishment which focuses on targeting bad behavior.
My wife taught a class on this for child upbringing but people got confused and thought the word "negative" just meant what you shouldn't do as a parent. But positive/negative has nothing to do with the morality of the reinforcement. Just the type. There are immoral ways to do any of the three training methods. For example, giving your child cocaine when they do something right is technically positive reinforcement, but obviously morally wrong.
I didn't learn about specifically negative and positive punishment but imagine it is similar yet opposite.
Plz make video on how you get started and more specifically how you learn the maths behind it.
make sure to subscribe, I've got a cool announcement coming :)
- Jabril
I'm getting into python now. Iike how you are newer like me, I normaly cant learn from the coders who try and teach and end up having to read. With your vids your explaining it slowly enough to understand thanks!
"The quick brown fox jumps over the lazy dog." That sentence has all the letters from the alphabet lmao.
Hey.... part 1 came up in my feed and I'm really enjoying the series so far...
You mentioned that you managed to get to a decent understanding of AI/neutral networks/ML in about 6 months!
Would you mind sharing the learning pathway ie the topics and resources you used?
I'm in a similar situation as you were in.
My math is a bit rough and I know I need to fast track that in order to get started...
Any help would be appreciated.
Thanks man!
My man really searched google on google to get to google! Amazing videos! Love your style of videos!
Awesome stuff sir!
YOOOO ITS MINEWORKS i didnt think id see you here lol
I like that man, always feed your curiosity. Been interested in this sort of stuff for ages! Thanks for sharing your project with the world :).
Thank you for taking the time to make this video sir. You have my sub and likes!
The magic power of editing
4:29 positive discipline. Negative reinforcement, is rewarding for bad behaviors.
Thank you for taking the time and effort to document your learning and making it so much fun!
another thing, biology is actually geometric shapes that iterlocks into pattern. that is what it does. its the same with dna as with all the other stuff in the cell. its signal molecules that fits receptors. a geometric shape that fits a geometric shape of a hole just like baby toys. dna does the same. its just a string of receptors that holds signal molecules in place. each signal molecule fit its spot in the string of dna as perfect fit. when scientists cut and paste dna, they actually cut both receptor and signal had hack it the hard way. that is why the dna repair molecules dont always manage to fuse the separated dna string back into the host string. the dna mocleules had ezymes that can glue two strings of dna into one. they also have cutting enzyme. when the ezymes read dna they take off the signal molecules of the receptors on the string and then copy them and put the originals back again. it means that there is big constructor molecules that build new molecules from other molecules. the point is that the secret of dna and biology is geometric iterlocking. its like a puzzle but with geometry instead of those weird pieces we use to play with. if we look at a sorting enzyme, it does the same. it have a rotor turning a ring containg receptors designed to fit certain molecules that act as a pumb. its the same as the axons and dentries. its signal molecule that fit receptor. its geometric interlocking. the holy grail of biology is geometric interlocking and signal shape of molecule fit receptor shape of molecule. biology is written in in geometric shape fitting.
Two things i love about your videos mano: The way you explain things and honestly... You have a great personality! you make this stuff look very fun to learn! keep it up man! Subbed!
I can’t get over the spongebob soundtracks your using!😂 Spongebob is the absolute greatest show!
Bruh, these spongebob sound bites . A+ on all your stuff, thank you.
This is the best description of machine learning I've ever seen
Holy shit shit dude, you are a genius. Great edition, dubbing and explanation. I'm amazed
This is the first time I haven't felt completely dumb while watching a video on machine learning
That visualization hierarchy is some next level shit. mad kudos to you!!!!!
This is awesome man. You break things down super well, and you're adding humor in the video. You make me want to pursue machine learning. Keep up the awesome work and thanks for the great video! :)
Your solution requires four inputs and several hours of training to complete it's goal. My solution is four inputs, a timer (to calculate the change over time of the inputs) and a nifty formula.
i have a idea. what about making the learning rate a variable and then use the difference between the most successful path and the one some itterations before to adjust the learning rate. perhaps that would make a lazy way of doing things faster? if the program can adjust its own fitness underway, it chould find its own most optimized value by itself. we could also train a neural net to learn from the neural net used to do the path finding to imporve the way the path finder neural net works by adjusting learning rate by interprating the pathfinder network numbers and make the other network adjust the path finder network learning rate acordingly. this way you have two brain working together to solve problem faster.
You are pure genius! My friend sent me this video as we are both on a nurture.fast.ai course and I'm in over my head as a complete novice. This video helped me to understand the concepts more clearly. Definitely watching the rest of your videos - :-)
+Ms888 B whoa whoa watch it on that genius word haha. But glad to help, i've got some products launching that might help with your AI adventure!
- Jabril
ha ha credit where credit is due :-) -look forward to seeing your other AI videos. I have also sent links to my 12 yr old nephew....your videos are great for kids into computers....
Just a quick kudos for what you are doing! waiting for part 3 &4
+solovo much love solovo! Thanks for watching part 3 is coming!
- Jabril
LSTM isn’t a neural network, it has 4 neural networks in it though
This motivates me to learn machine learning, thanks for the great videos! Can't wait for part 3
YES! I am so glad to have that effect! Please do get into ML research/demos, its loads of fun, see you on part 3!
- Jabril
Dear stranger, can I ask, how much developed yourself from this comment?
@@teyyub- i never learned machine learning lol
Nice video my dude, Im working on something very similar myself only that each player is basically a collection of neural networks that must work together to stimulate movement. Each cell which are tethered to each other through muscles have their own small NN which can trigger movement and contraction of the distance between the cells. Friction and other physics on the cells cause movement.
interphase, prophase, metaphase, anaphase...I love phyiso!
Thank you for making such amazing videos! They're tons of fun to watch and I've learned a ton too!
Dude, this is so approachable, thanks!
Positive reinforcement is the act of giving something to encourage a behavior while negative reinforcement takes away something to encourage a behavior. The second example with the dog is negative and positive punishment, since you are not only adding yelling, but also taking away the dog's right to being in the house.
Hi Jabril, Cool editing. Really liked it.
Thanks for existing.
I love you so much for this series and your whole channel bro, hell yeah
I’ve never thought that I would be so interested in something I thought was so boring!! Wow
Yo I just discovered your channel and it is amazing. I love the way that you build your videos, the visuals and most of all your sense of humor! Keep going man, I'll show your stuff to my friends
5:53 i distracted so hard because of that dance i forgot everything i learned i had to go back to the beginning of the video
5:11 Genewtic algo, yes! :)
There is a lot of time put behind that video so don't be fooled into thinking that it is just an afternoon of hacking. Good job man! I love it!
Talk about truly fascinating...And presented in a truly entertaining way.. You just got a new subscriber.
Just finished those two videos and now heading for the next two. Cool videos loved them as well as your editing. Well done 👌🏻
Thanks for the cool videos and keep it up man!
Your video series is awesome man! I don't understand everything but i really do appreciate the efford you put into your videos. seems like a looooot of work... Thanks, man!
thank you so much! ive been learning a lot thanks to you. pitty youtube never pointed me to you when i was actively searching for NN stuff. thanks again!
Wowowow hold up here for a minute. I kindly want to point out that supervised / unsupervised / reinforcement are NOT training techniques. These words are used to describe the data that you stuff into the network. The "learning" bit is added to describe the class of problem you are facing if your data is of a certain type. "Reinforcement learning" for example means "Training [a neural net] on sequences of data that is coming in one sample at a time in order to find behavior that maximizes the reward obtained".
And "gradient descent" is not a special algorithm. It instead describes how weights are changed in order to get better: 1) you evaluate your network to see how good it currently is 2) you do some math (compute the gradient) to figure out how to change each weight to increase performance (bigger or smaller weight) 3) you change the weights (make em bigger or smaller) and repeat.
This "gradient descent" is used _very_ often and is certainly not exclusive to supervised learning. Q-learning for example also uses gradient descent to update the weights of the network. (And so do methods used to train on unlabeled data [unsupervised learning])
I was wondering if this could go better if instead of controlling Forrest directly you would just create an NN to place way-points that Forrest could just follow using a path finder.
He explained that in part 1. He said he is too lazy to place markers all over the course and by doing that forrest would only master one particular route for the whole course
Just now getting into machine learning, wanted to say you've done an excellent job with these videos. I'd love to swap notes sometime.
Dude, I honestly love your videos. Thanks for the introduction.
Man, I love your videos. Your humour makes the video much more interesting. Keep it up!
Instantly subscribed. U explain things a lot better bro. Keep it up