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: https://youtu.be/vDDjtwQDw2k ✏️ Course created by @Radu (PhD in Computer Science) 📁 Data: https://github.com/gniziemazity/drawing-data 💻 Code: https://github.com/gniziemazity/ml-course-phase-2 💻 Ilya's code: https://gist.github.com/id-ilych/8630fb273e5c5a0b64ca1dc080d68b63 💻 Neural Network Code: https://github.com/gniziemazity/neural-network Phase 3 Poll: https://forms.office.com/e/QTMCLLaV24 ⭐️ Other Resources ⭐️ Recognizer we build in this course: https://radufromfinland.com/projects/ml/recognizer Euclidean Distance Video: https://youtu.be/3rPwfmrCwVw Interpolation Video: https://youtu.be/J_puRs40GhM Draw the Portal Game Tutorial (Inspired from Dr. Strange): https://youtu.be/0SxiyLk2IMM Why the Circle has the Largest Area: https://youtu.be/CFBa2ezTQJQ Recognizing drawings via webcam: https://youtu.be/QXB1ytG95gs Self-driving Car Course: https://youtu.be/Rs_rAxEsAvI Discord Server: https://discord.com/invite/gJFcF5XVn9 Scikit-learn documentation: http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html ❤️ Try interactive JavaScript courses we love, right in your browser: https://scrimba.com/freeCodeCamp-JavaScript (Made possible by a grant from our friends at Scrimba) ⭐️ Contents ⭐️ 0:00:00 Introduction 0: