Top videos
The KL divergence of distributions P and Q is a measure of how similar P and Q are.
However, the KL Divergence of P and Q is not the same as the KL Divergence of Q and P.
Why?
Learn the intuition behind this in this friendly video.
More about the KL Divergence formula:
https://www.youtube.com/watch?v=sjgZxuCm_8Q
Github repo: http://www.github.com/luisguis....errano/singular_valu
Grokking Machine Learning Book:
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In this video, we learn a very useful matrix trick called singular value decomposition (SVD), in which we express a matrix as a product of two rotation matrices and one scaling matrix.
We also show a very interesting application to image compression.
Similar videos:
Principal component analysis (PCA): https://www.youtube.com/watch?v=g-Hb26agBFg
Matrix factorization and Netflix recommendations: https://www.youtube.com/watch?v=ZspR5PZemcs
Introduction: (0:00)
Transformations: (0:50)
A puzzle: (1:27)
A harder puzzle: (2:21)
Linear transformations: (3:50)
Dimensionality reduction: (10:50)
Image compression: (23:57)
The Gini Impurity Index is a measure of the diversity in a dataset. In this short video you'll learn a very simple way to calculate it using probabilities.
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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For a code implementation, check out these repos:
https://github.com/luisguiserr....ano/manning/tree/mas
https://github.com/luisguiserr....ano/manning/tree/mas
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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An introduction to logistic regression and the perceptron algorithm that requires very little math (no calculus or linear algebra), only a visual mind.
0:00 Introduction
0:08 Series of 3 videos
0:41 E-mail spam classifier
7:19 Classification goal: split data
11:36 How to move a line
12:21 Rotating and translating
18:47 Perceptron Trick
23:20 Correctly and incorrectly classified points
24:20 Positive and negative regions
27:18 Perceptron Error
29:40 Gradient Descent
34:36 A friendly introduction to deep learning and neural networks
37:48 Activation function (sigmoid)
38:31 Log-Loss Error
41:37 Perceptron Algorithm
42:45 Logistic regression algorithm
44:48 Thank you!
CORRECTION: At 10:56 we shouldn't divide by 4 to get the covariance, we should divide by 1+1+1+1/3, which is 10/3. That means the covariances are the following:
Var(x) = 1.056
Var(y) = 0.864
Cov(x,y) = 0.768
(Thank you Shivkumar Pippal!)
Mean, variance, covariance, and the covariance matrix for a dataset and a weighted dataset.
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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0:00 Introduction
0:09 The covariance matrix
2:22 Average
3:23 X-variance
5:06 Problem: Same variances
7:59 Formulas
10:30 Center points
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Hi my name is Chris and I build productivity apps 👋 and this is EVERYTHING I learned building an AI agent using the Anthropic Agent SDK :)
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Timestamps:
0:00 – Intro / What we are covering
1:03 - What I built with the Agent SDK
2:28 - How Agents Work & Agent SDK Bascs
4:50 - What makes the Agent SDK special
7:55 - Using Convex as the database for my agent
9:19 - Building tools for the agent
10:28 - Agent Skills
12:32 - Agent memory
14:24 - Why I could not release my agent
17:07 - Practical use cases for agents
18:26 - Deploying agents
19:47 - Final thoughts and thank you :)
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AI isn’t just replacing jobs — it’s creating AI workforces that replace entire departments with digital teams that run 24/7 without salaries, burnout, or hiring bottlenecks. In this video, you’ll learn how to build AI agents and multi-agent workforces that automate real business processes like booking meetings, preparing presentations, transcribing calls, and assigning tasks, using the Relevance AI platform and powered by integrations with Gamma.app, HubSpot, Google Meet, Trello, and more. I’ll show you an end-to-end build of a personal AI assistant workforce that researches participants, designs presentation decks, manages scheduling, and sends follow-up summaries — all while you sleep. If you want to stay ahead in the $1 trillion AI automation market, monetize automation skills, and become the person companies trust to guide their AI transformation, this is your roadmap to surviving and thriving in the new era of autonomous digital employees.
⏱️ Timestamps:
00:00 - What We’re Covering
01:58 - Chapter 1: Foundations
05:28 - What Is an AI Workforce?
07:00 - How AI Workforces Actually Work
10:53 - Foundations Recap
11:47 - Chapter 2: Building
11:55 - Relevance AI Orientation
14:55 - What We’re Building
18:08 - Meeting Booker Agent
27:48 - Participant Finder Agent
31:07 - Orchestrator Agent
39:39 - Gamma Agent
47:39 - Lead Locator Agent
52:42 - Note Taker Agent
56:54 - Full AI Workforce Demo
59:14 - Publishing to Relevance Marketplace
1:01:12 - Chapter 3: Monetization (Selling workforces)
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This is the ultimate, no-code guide to building and deploying your first AI agent in under 30 minutes using N8N!
In this step-by-step tutorial, you'll learn how to connect the world's best AI models with a powerful no-code automation platform (N8N) to create an agent that works for you 24/7.
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You will learn:
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- The complete process from concept to deployment.
No coding is required. If you want to get ahead in the AI revolution, this is an in-depth guide that can help you.
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MIT 6.7960 Deep Learning, Fall 2024
Instructor: Sara Beery
View the complete course: https://ocw.mit.edu/courses/6-....7960-deep-learning-f
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6
This video provides a course overview and introduces deep neural networks, covering their fundamental concepts and basic building blocks. It sets the stage for understanding how these models work and what components they are built from.
License: Creative Commons BY-NC-SA
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MIT 6.868J The Society of Mind, Fall 2011
View the complete course: http://ocw.mit.edu/6-868JF11
Instructor: Marvin Minsky
In this lecture, students discuss Chapter 1 of The Emotion Machine, covering topics such as love, infatuation, and the Self.
License: Creative Commons BY-NC-SA
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MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15....-773-hands-on-deep-l
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6
Continues discussion of natural language processing with a focus on embeddings, including stand-alone and contextual embeddings.
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MIT 14.02 Principles of Macroeconomics, Spring 2023
Instructor: Ricardo J. Caballero
View the complete course: https://ocw.mit.edu/courses/14....-02-principles-of-ma
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6
In this lecture, Prof. Caballero discusses basic macroeconomic concepts such as aggregate output, the unemployment rate, and the inflation rate.
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MIT 15.401 Finance Theory I, Fall 2008
View the complete course: http://ocw.mit.edu/15-401F08
Instructor: Andrew Lo
License: Creative Commons BY-NC-SA
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MIT 6.006 Introduction to Algorithms, Spring 2020
Instructor: Jason Ku
View the complete course: https://ocw.mit.edu/6-006S20
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6
The goal of this introductions to algorithms class is to teach you to solve computation problems and communication that your solutions are correct and efficient. Models of computation, data structures, and algorithms are introduced.
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MIT 22.01 Introduction to Nuclear Engineering and Ionizing Radiation, Fall 2016
Instructor: Michael Short
View the complete course: https://ocw.mit.edu/22-01F16
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6
Prof. Short uses all the concepts introduced thus far to introduce the study of nuclear materials and radiation damage - his field of study. The concept of ionizing radiation creating nuclear displacements, not just electron ionization, is introduced as the first event in radiation damage. The structural defects produced from these displacements are shown to cluster, move, and evolve, resulting in drastic changes to material properties. Key structural material properties and their formal definitions are introduced and demystified by watching a pair of Finnish scientists smash various items with a 50 ton hydraulic press.
License: Creative Commons BY-NC-SA
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