Machine Learning & Neural Networks without Libraries – No Black Box Course

Welcome to this No Black Box Machine Learning Course in JavaScript. It’s a course where we code without using libraries because it’s the best way to learn all inner workings of a machine learning system and you’ll greatly improve your software development skills as well.

The goal in this course is to build a web app that learns to recognize drawings. This is phase 2, where we increase the accuracy of the method we developed in Phase 1. We do this by implementing more sophisticated features and using other classification methods (like the Neural Network). In Phase 2 we also learn about Data Cleaning, Confusion Matrices, Geometry and the difference between Vector and Raster data (pixels).

🎥 No Black Box Phase 1 Course:    • No Black Box Machine Learning Course – Lea...  

✏️ Course created by ‪@Radu‬ (PhD in Computer Science)

📁 Data: https://github.com/gniziemazity/drawi...
💻 Code: https://github.com/gniziemazity/ml-co...
💻 Ilya's code: https://gist.github.com/id-ilych/8630...
💻 Neural Network Code: https://github.com/gniziemazity/neura...
Phase 3 Poll: https://forms.office.com/e/QTMCLLaV24

⭐️ Other Resources ⭐️
Recognizer we build in this course: https://radufromfinland.com/projects/...
Euclidean Distance Video:    • Euclidean Distance: Math, Applications and...  
Interpolation Video:    • Linear Interpolation (Lerp) - Math and Jav...  
Draw the Portal Game Tutorial (Inspired from Dr. Strange):    • Doctor Strange portal game in vanilla Java...  
Why the Circle has the Largest Area:    • Why the Circle encloses the Largest Area |...  
Recognizing drawings via webcam:    • Learn Machine Learning (!BB - Ep1)  
Self-driving Car Course:    • Self-Driving Car with JavaScript Course – ...  
Discord Server:   / discord  
Scikit-learn documentation: http://scikit-learn.org/stable/module...

❤️ Try interactive JavaScript courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Java... (Made possible by a grant from our friends at Scrimba)

⭐️ Contents ⭐️
0:00:00 Introduction
0:04:07 Phase 1 Code Review
0:23:11 Data Cleaning
0:41:30 Confusion Matrix
1:16:00 Euclidean Distance Marker
1:16:06 Measuring the Elongation
1:39:23 Measuring the Roundness
1:59:20 Vector vs Raster (Pixels)
2:22:40 Neural Networks
3:04:49 Optimizing Neural Networks
3:25:15 Deep Neural Networks

🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
👾 jedi-or-sith
👾 南宮千影
👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
👾 Oscar Rahnama

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