In this No Black Box Machine Learning Course in JavaScript, you will gain a deep understanding of machine learning systems by coding without relying on libraries. This unique approach not only demystifies the inner workings of machine learning but also significantly enhances software development skills.

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

🎥 Watch part two: https://youtu.be/3wwiOSxDAmg

HOMEWORK
🏠 1st assignment spreadsheet: https://docs.google.com/spread....sheets/d/16wIddJ9jKA
🏠 Submit all other assignments to Radu's Discord Server: https://discord.com/invite/gJFcF5XVn9

GITHUB LINKS
💻 Drawing App: https://github.com/gniziemazity/drawing-app
💻 Data: https://github.com/gniziemazity/drawing-data
💻 Custom Chart Component: https://github.com/gniziemazity/javascript_chart
💻 Full Course Code (In Parts): https://github.com/gniziemazity/ml-course

PREREQUISITES
🎥 Interpolation: https://youtu.be/J_puRs40GhM
🎥 Linear Algebra: https://youtu.be/nzyOCd9FcCA
🎥 Trigonometry: https://youtu.be/xK3vKWMFVgw

LINKS
🔗 Check out the Recognizer we'll build in this course: https://radufromfinland.com/projects/ml/recognizer
🔗 Draw for Radu, Call for help video: https://youtu.be/Yw2QZ1vq2ek
🔗 Draw for Radu, Data collection tool: https://radufromfinland.com/projects/ml
🔗 Radu's Self-driving Car Course: https://www.youtube.com/playli....st?list=PLB0Tybl0UNf
🔗 Radu's older Machine Learning video: https://youtu.be/QXB1ytG95gs
🔗 CHART TUTORIAL (mentioned at 01:45:27): https://youtu.be/n8uCt1TSGKE
🔗 CHART CODE: https://github.com/gniziemazity/javascript_chart

TOOLS
🔧 Visual Studio Code: https://code.visualstudio.com/download
🔧 Google Chrome: https://www.google.com/chrome
🔧 Node JS: https://nodejs.org/en/download
(make sure you add 'node' and 'npm' to the PATH environment variables when asked!)


TIMESTAMPS
⌨️(0:00:00) Introduction
⌨️(0:05:04) Drawing App
⌨️(0:46:46) Homework 1
⌨️(0:47:05) Working with Data
⌨️(1:08:54) Data Visualizer
⌨️(1:29:52) Homework 2
⌨️(1:30:05) Feature Extraction
⌨️(1:38:07) Scatter Plot
⌨️(1:46:12) Custom Chart
⌨️(2:01:03) Homework 3
⌨️(2:01:35) Nearest Neighbor Classifier
⌨️(2:43:21) Homework 4 (better box)
⌨️(2:43:53) Data Scaling
⌨️(2:54:45) Homework 5
⌨️(2:55:23) K Nearest Neighbors Classifier
⌨️(3:04:18) Homework 6
⌨️(3:04:49) Model Evaluation
⌨️(3:21:29) Homework 7
⌨️(3:22:01) Decision Boundaries
⌨️(3:39:26) Homework 8
⌨️(3:39:59) Python & SkLearn
⌨️(3:50:35) Homework 9

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👉 Merry Christmas to all the music lovers. This playlist is perfect for setting the mood for the holiday season and includes all the Christmas songs you know and love. Enjoy!
May the magic of Christmas fill your heart with warmth and love. Merry Christmas!
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Check out the latest (and most visual) video on this topic! The Celestial Mechanics of Attention Mechanisms: https://www.youtube.com/watch?v=RFdb2rKAqFw

Attention mechanisms are crucial to the huge boom LLMs have recently had.
In this video you'll see a friendly pictorial explanation of how attention mechanisms work in Large Language Models.

This is the first of a series of three videos on Transformer models.
Video 1: The attention mechanism in high level (this one)
Video 2: The attention mechanism with math: https://www.youtube.com/watch?v=UPtG_38Oq8o
Video 3: Transformer models https://www.youtube.com/watch?v=qaWMOYf4ri8

Learn more in LLM University! https://llm.university

Correction (10:26). The probabilities are wrong. The correct ones are here:
For Die 1: 0.4^4 * 0.2^2 * 0.1^1 * 0.1^1 * 0.2^2
For Die 2: 0.4^4 * 0.1^2 * 0.2^1 * 0.2^1 * 0.1^2
For Die 3: 0.1^4 * 0.2^2 * 0.4^1 * 0.2^1 * 0.1^2

Kullback Leibler (KL) divergence is a way to measure how far apart two distributions are.
In this video, we learn KL-divergence in a simple way, using a probability game with dice.

Shannon entropy and information gain: https://www.youtube.com/watch?v=9r7FIXEAGvs&t=1066s&pp=ygUic2hhbm5vbiBlbnRyb3B5IGluZm9ybWF0aW9uIHRoZW9yeQ%3D%3D

Grokking Machine Learning book: www.manning.com/books/grokking-machine-learning
40% discount code: serranoyt

Non-technical friendly course explaining machine learning and deep learning that everyone can understand.

Full course playlist here: https://www.youtube.com/playli....st?list=PLnrO0TOwDbu

A quick look at CBOW and how it looks like it in code. I explained how this model works in the previous example so check that out before you look at this one to get a better understanding.

Timestamps:
0:00 - Introduction
0:48 - Imports
0:53 - CBOW model
5:10 - Example of how to train it
8:52 - Ending

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 Editor : Vivek Vfx
 Producer : Prem Yadav
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 Digital Partner : TNA Digital Media


© Copyright (c) : Kashi Music World
℗ Copyright (P) : Nueva Era Digital Media Pvt. Ltd.

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यदि आपको यह गाना अच्छा लगा हो , तो Please
| Like | Share | Comment | जरूर करे !

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Los vitrales antiguos son más gruesos en la parte de abajo, ¿entonces es un líquido? El manto de la Tierra permite la tectónica de placas, ¿entonces significa que es un líquido?
🥂SUSCRÍBETE para ver todos nuestros videos:
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Experimento de la Brea: http://www.thetenthwatch.com

Gracias a Meg Rosenburg por el guión y la animación, Raquel Nuno por filmar y Aaron White por la consulta del guión.

Como dijo el famoso geólogo Grove Karl Gilbert:
"En mi mente la dificultad aparenta ser solo imaginaria y no real.
Rigidez y plasticidad no son términos absolutos, son relativos, y todos los sólidos son ambos, rígidos y plásticos.
Cuando grandes masas y fuerzas están involucradas, la distinción pierde su valor"

¿El vidrio es un Sólido o un Líquido? - El estado del vidrio
Video en Inglés del Canal @Veritasium: https://youtu.be/c6wuh0NRG1s
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🕹 Este canal de Youtube es administrado por: https://www.unilingo.tv/
❓Comentarios o sugerencias de traducción: [email protected]
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This video shows how you can detect if a sequence is periodic or not, and also find its period, using the Discrete Fourier Transform.

This is the second in a series of videos about Fourier transforms.

- First video: The Discrete Fourier Transform https://www.youtube.com/watch?v=T8XZxR5H04E
- Second video: Detecting Periodicity with the DFT (this one)

Grokking Machine Learning Book:
https://www.manning.com/books/....grokking-machine-lea
40% discount promo code: serranoyt

00:00 Introduction
2:37: Periodic sequences
12:54 In the inverse DFT

In this video we do a paper walk through of the GCN (Graph Convolutional Network) paper that is the most cited and one of the impactful in graph neural networks. We understand how they derived it and how it works in detail.

Big thanks to MakinaRocks for sponsoring this video, and I encourage you to check out Link which is a jupter lab extension they have developed to make notebooks more intuitive and easier to use. A few of the features I’ve found useful are:
1️⃣ Caching result from running cells in notebooks to avoid re-running them
2️⃣ Visualizing cell dependencies
3️⃣ Integrated version control

Check it out⬇️
https://bit.ly/3F4COvv

Link Demo Video 👉 https://youtu.be/uM2uPG-1eQQ
Link Documentation 👉https://makinarocks.gitbook.io/link/

Timestamps:
0:00 - Introduction
1:00 - Sponsored Segment: Link
2:09 - Abstract and overview
4:13 - Introduction to the problem
8:53 - How GCNs work
13:47 - Theory of GCN derivation
21:25 - GCN Node classification example
24:33 - Conclusions & Results
25:13 - Ending thoughts

#link #gcn

Christmas Fireplace Ambience (12 HOURS). Fireplace sounds (NO Music). Fireplace background 4K UHD. Fireplace with Burning Logs & Fire Sounds. This fireplace is perfect for adding a warm and cozy atmosphere to your Christmas celebrations. If you're looking for a fireplace without any music, then this is the fireplace for you! We'll be setting it up and operating it in perfect 4K quality for you. so you can experience the beauty and warmth of a real Christmas fireplace in your home. Enjoy 12 hours of burning fire and the crackle of burning logs. A Christmas fireplace will give you a festive mood and make your room more cozy. Use the relaxing sounds of the fireplace to study, work and sleep.

🔥 Playlist: Fireplace 4K (10 HOURS) ー https://www.youtube.com/playli....st?list=PLpJBTXjS2E9

🔥 Cozy Winter Day by the Campfire 4K (10 HOURS) - https://youtu.be/taM71seGnG4

YouTube: https://www.youtube.com/channe....l/UCaJs3QtwUWJ2u_sdQ
Twitter: https://twitter.com/emelyanoof
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Email: [email protected]

© FOBOS PLANET, Ilya Emelyanov 2023. All rights reserved. Any reproduction or republication of all or part of this video/audio is prohibited.

#FobosPlanet #Campfire #Fireplace #Fireplace4k #firesound #cozyambience #fire #campfiresound #wood #relaxing #winter #forest #YuleLog #12hours

Learn Python programming from Harvard University. It dives more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap. Topics include database design, scalability, security, and user experience. You will learn how to write and use APIs, create interactive UIs, and leverage cloud services like GitHub and Heroku.

This course will teach you how to read and write code as well as how to test and “debug” it. It is designed for students with or without prior programming experience who’d like to learn Python specifically. Learn about functions, arguments, and return values (oh my!); variables and types; conditionals and Boolean expressions; and loops. Learn how to handle exceptions, find and fix bugs, and write unit tests; use third-party libraries; validate and extract data with regular expressions; model real-world entities with classes, objects, methods, and properties; and read and write files. Hands-on opportunities for lots of practice. Exercises inspired by real-world programming problems. No software required except for a web browser, or you can write code on your own PC or Mac.

Whereas CS50x (the original CS50 course) itself focuses on computer science more generally as well as programming with C, Python, SQL, and JavaScript, this course, aka CS50P, is entirely focused on programming with Python. You can take CS50P before CS50x, during CS50x, or after CS50x. But for an introduction to computer science itself, you should still take CS50x!

💻 Slides, source code, and more at https://cs50.harvard.edu/python

✏️ Dr. David J. Malan teaches this course.

🖥 Watch the original CS50x course: https://youtu.be/8mAITcNt710

⭐️ Course Contents ⭐️
(00:00:00) Introduction
(00:04:48) Lecture 0 - Functions, Variables
(01:50:24) Lecture 1 - Conditionals
(02:46:23) Lecture 2 - Loops
(04:07:10) Lecture 3 - Exceptions
(04:51:45) Lecture 4 - Libraries
(06:09:15) Lecture 5 - Unit Tests
(07:00:22) Lecture 6 - File I/O
(08:32:32) Lecture 7 - Regular Expressions
(10:37:35) Lecture 8 - Object-Oriented Programming
(13:28:47) Lecture 9 - Et Cetera

HOW TO JOIN CS50 COMMUNITIES

Discord: https://discord.gg/cs50
Ed: https://cs50.harvard.edu/x/ed
Facebook Group: https://www.facebook.com/groups/cs50/
Faceboook Page: https://www.facebook.com/cs50/
GitHub: https://github.com/cs50
Gitter: https://gitter.im/cs50/x
Instagram: https://instagram.com/cs50
LinkedIn Group: https://www.linkedin.com/groups/7437240/
LinkedIn Page: https://www.linkedin.com/school/cs50/
Medium: https://cs50.medium.com/
Quora: https://www.quora.com/topic/CS50
Reddit: https://www.reddit.com/r/cs50/
Slack: https://cs50.edx.org/slack
Snapchat: https://www.snapchat.com/add/cs50
SoundCloud: https://soundcloud.com/cs50
Stack Exchange: https://cs50.stackexchange.com/
TikTok: https://www.tiktok.com/@cs50
Twitter: https://twitter.com/cs50
YouTube: https://www.youtube.com/cs50

HOW TO FOLLOW DAVID J. MALAN

Facebook: https://www.facebook.com/dmalan
GitHub: https://github.com/dmalan
Instagram: https://www.instagram.com/davidjmalan/
LinkedIn: https://www.linkedin.com/in/malan/
TikTok: https://www.tiktok.com/@davidjmalan
Twitter: https://twitter.com/davidjmalan

LICENSE

CC BY-NC-SA 4.0
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
https://creativecommons.org/licenses/by-nc-sa/4.0/

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