In this video we implement a neural network from scratch using Numpy!

Github repo: https://github.com/AladdinPerz....on/Machine-Learning-

Visuals to demonstrate how a neural network classifies a set of data. Thanks for watching!
Support me on Patreon! https://patreon.com/vcubingx
Source Code: https://github.com/vivek3141/dl-visualization

Here's the course I referred to in the video. I am not affiliated with NYU.
https://www.youtube.com/playli....st?list=PLLHTzKZzVU9

Sinusoids as activation functions:
https://openreview.net/forum?id=Sks3zF9eg
https://vsitzmann.github.io/siren/

Here's the distill.pub article:
https://distill.pub/2020/grand-tour/

Special thanks to Alfredo Canziani and Nikhil Maserang for reviewing the video.

And also thanks to Grant Sanderson himself for giving me some manim tips!

I've been active on twitter, follow me here!
https://twitter.com/vcubingx

Join my discord server!
https://discord.gg/Kj8QUZU

These animation in this video was made using 3blue1brown's library, manim:
https://github.com/3b1b/manim

Music is from GameChops (Route 113, Azalea Town, Ecruteak City

Follow me!
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What does a Neural Network *actually* do? Visualizing Deep Learning, Chapter 2

0:00 Intro
0:18 Recap of Part 1
1:57 Introducing the dataset
2:52 Structure of the Neural Network we’ll be using
3:34 What is softmax?
5:52 Input space decision boundaries
6:24 Modifying the Neural Network to visualize what it’s doing
7:36 Out-of-domain boundaries
8:46 sin(x) as an activation function
9:30 Neuron planes
11:57 Softmax surfaces
13:20 MNIST Transformation
13:42 Outro

AI/ML has been witnessing a rapid acceleration in model improvement in the last few years. The majority of the state-of-the-art models in the field are based on the Transformer architecture. Examples include models like BERT (which when applied to Google Search, resulted in what Google calls "one of the biggest leaps forward in the history of Search") and OpenAI's GPT2 and GPT3 (which are able to generate coherent text and essays).

This video by the author of the popular "Illustrated Transformer" guide will introduce the Transformer architecture and its various applications. This is a visual presentation accessible to people with various levels of ML experience.


Intro (0:00)
The Architecture of the Transformer (4:18)
Model Training (7:11)
Transformer LM Component 1: FFNN (10:01)
Transformer LM Component 2: Self-Attention(12:27)
Tokenization: Words to Token Ids (14:59)
Embedding: Breathe meaning into tokens (19:42)
Projecting the Output: Turning Computation into Language (24:11)
Final Note: Visualizing Probabilities (25:51)

The Illustrated Transformer:
https://jalammar.github.io/ill....ustrated-transformer

Simple transformer language model notebook:
https://github.com/jalammar/ja....lammar.github.io/blo

Philosophers On GPT-3 (updated with replies by GPT-3):
https://dailynous.com/2020/07/....30/philosophers-gpt-
-----


Twitter: https://twitter.com/JayAlammar
Blog: https://jalammar.github.io/
Mailing List: http://eepurl.com/gl0BHL


More videos by Jay:
Jay's Visual Intro to AI
https://www.youtube.com/watch?v=mSTCzNgDJy4


How GPT-3 Works - Easily Explained with Animations
https://www.youtube.com/watch?v=MQnJZuBGmSQ

-- English below --

Het muzikale programma:
Pjotr Iljitsj Tsjaikovski - Serenade voor strijkers in C, op.48

De uitvoerenden:
Boris Brovtsyn, viool
Sonoko Miriam Welde, viool
Hana Chang, viool
Clara-Jumi Kang, viool
Edvard Erdal, viool
Charlotte Spruit, viool
Lena ter Schegget, viool
Amihai Grosz, altviool
Michael Andreas Grolid, altviool
Elisa Karen Tavenier, altviool
Pablo Ferrández, cello
Daniel Thorell, cello
Zvi Plesser, cello
Ying Lai Green, contrabas
Servaas Jessen, contrabas

Opname:
Slotconcert van het Internationaal Kamermuziek Festival Utrecht op 30 december 2022 in TivoliVredenburg te Utrecht.

Kijk het hele concert op NPO Start:
https://www.npostart.nl/slotco....ncert-internationaal

Meer AVROTROS Klassiek:
♬ Facebook: https://www.facebook.com/AVROTROS.Kla...
♬ Twitter: https://twitter.com/klassiekonline
♬ Instagram: https://www.instagram.com/avrotroskla...

—————————————

On the musical programme:
Pyotr Ilyich Tchaikovsky - Serenade for Strings in C major, Op. 48

The musicians:
Boris Brovtsyn, violin
Sonoko Miriam Welde, violin
Hana Chang, violin
Clara-Jumi Kang, violin
Edvard Erdal, violin
Charlotte Spruit, violin
Lena ter Schegget, violin
Amihai Grosz, viola
Michael Andreas Grolid, viola
Elisa Karen Tavenier, viola
Pablo Ferrández, cello
Daniel Thorell, cello
Zvi Plesser, cello
Ying Lai Green, double bass
Servaas Jessen, double bass

Recording:
Closing Concert of the International Chamber Music Festival Utrecht, Friday the 30th of December 2022, in TivoliVredenburg in Utrecht, The Netherlands.

Watch the full concert on NPO Start:
https://www.npostart.nl/slotco....ncert-internationaal

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♬ Instagram: https://www.instagram.com/avrotrosklassiek/

** Python Data Science Training: https://www.edureka.co/python-....programming-certific **
This Edureka video on "Q Learning Explained" will provide you with a detailed and comprehensive knowledge of Q-Learning and also the various aspects of Q-Learning.

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About the Course

Edureka’s Data Science Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Data Science Certification Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR.

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During our Python Certification Training, our instructors will help you to:


1. Master the basic and advanced concepts of Python

2. Gain insight into the 'Roles' played by a Machine Learning Engineer

3. Automate data analysis using python

4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application

5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn

6. Explain Time Series and it’s related concepts

7. Perform Text Mining and Sentimental analysis

8. Gain expertise to handle business in the future, living the present

9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience

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Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built-in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.

Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.

Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.

For online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

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In this Edureka youtube live session, we will show you user management which is the primary task of a Salesforce Administrator. We will take you through how user licenses can be managed, how profiles and permission sets can be assigned to existing and new users.
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c. Using Permission Sets

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⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

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This Machine Learning basics video will help you understand what Machine Learning is, what are the types of Machine Learning - supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into self-learning mode without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results.

The below topics are explained in this Machine Learning basics video:
1. What is Machine Learning? ( 00:21 )
2. Types of Machine Learning ( 02:43 )
2. What is Supervised Learning? ( 02:53 )
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