Radu Mariescu-Istodor | Self-driving car - No libraries - JavaScript course [Lecture 8] @Radu | Uploaded 2 years ago | Updated 6 hours ago
⭐️LECTURE 8: OPTIMIZING NEURAL NETWORKS⭐️
We learn a way to optimize a neural network. First we will do trial and error in parallel. Then, I will teach how to use a genetic algorithm that does mutation. We will use local storage to save the best networks so that they are not lost when refreshing the page.
⭐️COURSE INTRODUCTION⭐️
Hi and welcome to the self-driving car (no libraries) JavaScript course.
You will learn to make a self-driving car simulation by implementing every component one by one. I will teach you how to implement the car driving mechanics, how to define the environment, how to simulate some sensors, how to detect collisions and how to make the car control itself using a neural network.
I will explain how artificial neural networks work, by comparing them with the real neural networks in our brain. I will teach you how to implement a neural network and how to visualize it so we can see it in action. You will also learn some optimization techniques to make our car smarter.
The entire system is quite complex, however, each component is relatively easy in and of itself. I made this course keeping in mind my high-school days back in Romania. So, the knowledge you get from school could be enough to follow along (depending on the curriculum).
We use JavaScript to implement this system and I will teach modern JavaScript techniques during this course, so, this course is perfect no matter if you want to become a software engineer or a machine learning specialist (like me - I have over 10 years research experience with machine learning).
⭐️MORE LINKS⭐️
NEAT: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf
JS NN Libraries: blog.logrocket.com/best-javascript-machine-learning-libraries-in-2021
⭐️CODE⭐️
Github: github.com/gniziemazity/Self-driving-car (8. Optimizing neural networks)
Use '7. Visualizing neural networks' if you plan to follow along!
My website: radufromfinland.com
⭐️ TABLE OF CONTENT ️⭐️
0:00 Intro
1:13 Parallelization
6:08 Fitness function
9:13 Saving networks in local storage
12:22 Other fitness functions
16:58 Mutation
22:42 A hard scenario
27:23 Other genetic algorithms
⭐️LECTURE 8: OPTIMIZING NEURAL NETWORKS⭐️
We learn a way to optimize a neural network. First we will do trial and error in parallel. Then, I will teach how to use a genetic algorithm that does mutation. We will use local storage to save the best networks so that they are not lost when refreshing the page.
⭐️COURSE INTRODUCTION⭐️
Hi and welcome to the self-driving car (no libraries) JavaScript course.
You will learn to make a self-driving car simulation by implementing every component one by one. I will teach you how to implement the car driving mechanics, how to define the environment, how to simulate some sensors, how to detect collisions and how to make the car control itself using a neural network.
I will explain how artificial neural networks work, by comparing them with the real neural networks in our brain. I will teach you how to implement a neural network and how to visualize it so we can see it in action. You will also learn some optimization techniques to make our car smarter.
The entire system is quite complex, however, each component is relatively easy in and of itself. I made this course keeping in mind my high-school days back in Romania. So, the knowledge you get from school could be enough to follow along (depending on the curriculum).
We use JavaScript to implement this system and I will teach modern JavaScript techniques during this course, so, this course is perfect no matter if you want to become a software engineer or a machine learning specialist (like me - I have over 10 years research experience with machine learning).
⭐️MORE LINKS⭐️
NEAT: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf
JS NN Libraries: blog.logrocket.com/best-javascript-machine-learning-libraries-in-2021
⭐️CODE⭐️
Github: github.com/gniziemazity/Self-driving-car (8. Optimizing neural networks)
Use '7. Visualizing neural networks' if you plan to follow along!
My website: radufromfinland.com
⭐️ TABLE OF CONTENT ️⭐️
0:00 Intro
1:13 Parallelization
6:08 Fitness function
9:13 Saving networks in local storage
12:22 Other fitness functions
16:58 Mutation
22:42 A hard scenario
27:23 Other genetic algorithms
![Self-driving car - No libraries - JavaScript course [Lecture 8]](https://i.ytimg.com/vi/hVctn8x3jMA/hqdefault.jpg "Self-driving car - No libraries - JavaScript course [Lecture 8]
⭐️LECTURE 8: OPTIMIZING NEURAL NETWORKS⭐️
We learn a way to optimize a neural network. First we will do trial and error in parallel. Then, I will teach how to use a genetic algorithm that does mutation. We will use local storage to save the best networks so that they are not lost when refreshing the page.
⭐️COURSE INTRODUCTION⭐️
Hi and welcome to the self-driving car (no libraries) JavaScript course.
You will learn to make a self-driving car simulation by implementing every component one by one. I will teach you how to implement the car driving mechanics, how to define the environment, how to simulate some sensors, how to detect collisions and how to make the car control itself using a neural network.
I will explain how artificial neural networks work, by comparing them with the real neural networks in our brain. I will teach you how to implement a neural network and how to visualize it so we can see it in action. You will also learn some optimization techniques to make our car smarter.
The entire system is quite complex, however, each component is relatively easy in and of itself. I made this course keeping in mind my high-school days back in Romania. So, the knowledge you get from school could be enough to follow along (depending on the curriculum).
We use JavaScript to implement this system and I will teach modern JavaScript techniques during this course, so, this course is perfect no matter if you want to become a software engineer or a machine learning specialist (like me - I have over 10 years research experience with machine learning).
⭐️MORE LINKS⭐️
NEAT: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf
JS NN Libraries: https://blog.logrocket.com/best-javascript-machine-learning-libraries-in-2021
⭐️CODE⭐️
Github: https://github.com/gniziemazity/Self-driving-car (8. Optimizing neural networks)
Use 7. Visualizing neural networks if you plan to follow along!
My website: https://radufromfinland.com
⭐️ TABLE OF CONTENT ️⭐️
0:00 Intro
1:13 Parallelization
6:08 Fitness function
9:13 Saving networks in local storage
12:22 Other fitness functions
16:58 Mutation
22:42 A hard scenario
27:23 Other genetic algorithms")
![Detecting Colored Markers with JavaScript [Understanding AI - Lesson 13 / 15]](https://i.ytimg.com/vi/jy-Mxbt0zww/hqdefault.jpg "Detecting Colored Markers with JavaScript [Understanding AI - Lesson 13 / 15]
PLAYLIST: https://www.youtube.com/playlist?list=PLB0Tybl0UNfYbL1vDNrfHoYOKf1Sn0A81
Remember those early mobile games where you could steer by tilting the phone? In this video, Ill guide you through using the device orientation sensor to capture tilt movements. Its a fantastic technique that opens up a world of possibilities for game control.
Lets explore the potential of this feature not just for games, but for other applications too. Ill share my friends creation, Jazzman vs. Metalheadz, which showcases the versatility of tilt controls.
The applications extend beyond gaming – think about measuring tree heights or distances to distant objects. If youre interested, I have tutorials on those topics as well. Towards the end of the video, Ill demonstrate how to integrate tilt controls into our racing game, tying it back to the Understanding AI course.
So, let me steer you in the right direction for implementing device orientation in your projects. And by the way, did I mention well be doing some nononooooo again? You wont want to miss it! Like, share, and subscribe for more exciting tutorials on AI, game development, and innovative programming techniques!
💬DISCORD💬
discord.gg/gJFcF5XVn9
⭐LINKS⭐
Self-driving Car Course: https://www.youtube.com/playlist?list=PLB0Tybl0UNfYoJE7ZwsBQoDIG4YN9ptyY
My Videos with Markers: https://www.youtube.com/playlist?list=PLB0Tybl0UNfb2mpLdkgVNRHC1VCMGthq_
Coding my Personal Avatar: https://youtu.be/U88NM8ScsPY
Summary of the Avatar Project: https://youtu.be/JFMTudIJLH0
#ImageProcessing #JavaScriptImageProcessing #JavaScriptMarkerDetector #JavaScriptMarkers
⭐TIMESTAMPS⭐
00:00 Introduction
01:21 Basic Project Setup
02:43 Accessing the Camera
04:45 Image Processing
08:10 Getting the ImageData
13:38 Measuring Blueness of Pixels
21:43 Visualizing Pixel Properties
26:30 Thresholding
28:41 Marker Location and Size
32:49 Two Markers")
