All Machine Learning algorithms intuitively explained in 17 min

In this video I will go through all machine learning algorithms in less than 17 minutes to get you an intuitive understanding of how they work and how they relate to each other as well as help you decide how to pick the right one for your problem. Going all the way from Linear Regression to Neural Networks / Deep Learning and Unsupervised Learning.

Also Watch: How to Learn Machine Learning in 2024 (7 step roadmap) https://youtu.be/jwTaBztqTZ0

Chapters:

00:00 - Intro: What is Machine Learning?
00:59 - Supervised Learning
01:37 - Unsupervised Learning
02:20 - Linear Regression
04:04 - Logistic Regression
04:53 - K Nearest Neighbors (KNN)
06:10 - Support Vector Machine (SVM)
07:51 - Naive Bayes Classifier
08:37 - Decision Trees
09:11 - Ensemble Algorithms
09:24 - Bagging & Random Forests
09:53 - Boosting & Strong Learners
10:26 - Neural Networks / Deep Learning
12:42 - Unsupervised Learning (again)
12:57 - Clustering / K-means
14:35 - Dimensionality Reduction
15:15 - Principal Component Analysis (PCA)

Qual a diferença entre Inteligência Artificial, Machine Learning e Deep Learning?

Neste vídeo trato das diferenças que existem entre as tecnologias de Inteligência Artificial, Aprendizado de Máquina e Aprendizado Profundo, além de abordar as aplicações de cada tecnologia.

Leia também: O que é Machine Learning? http://www.bosontreinamentos.c....om.br/inteligencia-a

Ajude o canal adquirindo meus cursos na Udemy:
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#bosontreinamentos #inteligenciaartificial #machinelearning

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This Edureka "Convolutional Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you in understanding what is Convolutional Neural Network and how it works. It also includes a use-case, in which we will be creating a classifier using TensorFlow.

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Go from zero to a machine learning engineer in 12 months. This step-by-step roadmap covers the essential skills you must learn to become a machine learning engineer in 2024.

Download the FREE roadmap PDF here: https://mosh.link/machine-learning-roadmap

✋ Stay connected

- Complete courses: https://codewithmosh.com
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🔗 Other roadmaps

https://youtu.be/Tef1e9FiSR0?si=QpVnZ_o9-DAXzT71
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📚 Tutorials

https://youtu.be/_uQrJ0TkZlc?si=ZhlCrQs1SkaPNVa8
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https://youtu.be/7S_tz1z_5bA?si=QL7s_M2Ao90RDwG8

📖 Chapters

00:00 - Introduction
00:20 - Programming Languages
00:42 - Version Control
01:03 - Data Structures & Algorithms
01:35 - SQL
01:55 - The Complete Roadmap PDF
02:19 - Mathematics & Statistics
02:40 - Data Handling
03:15 - Machine Learning Fundamentals
03:57 - Advanced Topics
04:28 - Model Deployment

#machinelearning #ai #datascience #coding #programming

TensorFlow is a tool for machine learning capable of building deep neural networks with high-level Python code. It provides developer-friendly APIs that help software engineers train, analyze, and deploy ML models.

#programming #deeplearning #100secondsofcode

🔗 Resources

TensorFlow Docs https://www.tensorflow.org/
Fashion MNIST Tutorial https://www.tensorflow.org/tutorials/keras/classification
Neural Networks Overview for Data Scientists https://www.ibm.com/cloud/learn/neural-networks
Machine Learning in 100 Seconds https://youtu.be/PeMlggyqz0Y

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🔖 Topics Covered

- What is TensorFlow?
- How to build a neural network with TensorFlow
- What is TensorFlow used for?
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- How neural networks work
- Easy neural network tutorial
- What is a mathematical Tensor?

In this comprehensive exploration of the field of deep learning with Professor Simon Prince who has just authored an entire text book on Deep Learning, we investigate the technical underpinnings that contribute to the field's unexpected success and confront the enduring conundrums that still perplex AI researchers.

Understanding Deep Learning - Prof. SIMON PRINCE [STAFF FAVOURITE]

Watch behind the scenes, get early access and join private Discord by supporting us on Patreon:
https://patreon.com/mlst
https://discord.gg/aNPkGUQtc5
https://twitter.com/MLStreetTalk

Key points discussed include the surprising efficiency of deep learning models, where high-dimensional loss functions are optimized in ways which defy traditional statistical expectations. Professor Prince provides an exposition on the choice of activation functions, architecture design considerations, and overparameterization. We scrutinize the generalization capabilities of neural networks, addressing the seeming paradox of well-performing overparameterized models. Professor Prince challenges popular misconceptions, shedding light on the manifold hypothesis and the role of data geometry in informing the training process. Professor Prince speaks about how layers within neural networks collaborate, recursively reconfiguring instance representations that contribute to both the stability of learning and the emergence of hierarchical feature representations. In addition to the primary discussion on technical elements and learning dynamics, the conversation briefly diverts to audit the implications of AI advancements with ethical concerns.

Pod version (with no music or sound effects): https://podcasters.spotify.com..../pod/show/machinelea

Follow Prof. Prince:
https://twitter.com/SimonPrinceAI
https://www.linkedin.com/in/si....mon-prince-615bb9165

Get the book now!
https://mitpress.mit.edu/97802....62048644/understandi
https://udlbook.github.io/udlbook/

Panel: Dr. Tim Scarfe -
https://www.linkedin.com/in/ecsquizor/
https://twitter.com/ecsquendor

TOC:
[00:00:00] Introduction
[00:11:03] General Book Discussion
[00:15:30] The Neural Metaphor
[00:17:56] Back to Book Discussion
[00:18:33] Emergence and the Mind
[00:29:10] Computation in Transformers
[00:31:12] Studio Interview with Prof. Simon Prince
[00:31:46] Why Deep Neural Networks Work: Spline Theory
[00:40:29] Overparameterization in Deep Learning
[00:43:42] Inductive Priors and the Manifold Hypothesis
[00:49:31] Universal Function Approximation and Deep Networks
[00:59:25] Training vs Inference: Model Bias
[01:03:43] Model Generalization Challenges
[01:11:47] Purple Segment: Unknown Topic
[01:12:45] Visualizations in Deep Learning
[01:18:03] Deep Learning Theories Overview
[01:24:29] Tricks in Neural Networks
[01:30:37] Critiques of ChatGPT
[01:42:45] Ethical Considerations in AI

References:

#61: Prof. YANN LECUN: Interpolation, Extrapolation and Linearisation (w/ Dr. Randall Balestriero)
https://youtube.com/watch?v=86ib0sfdFtw

Scaling down Deep Learning [Sam Greydanus]
https://arxiv.org/abs/2011.14439

"Broken Code" a book about Facebook's internal engineering and algorithmic governance [Jeff Horwitz]
https://www.penguinrandomhouse.....com/books/712678/br

Literature on neural tangent kernels as a lens into the training dynamics of neural networks.
https://en.wikipedia.org/wiki/....Neural_tangent_kerne

Zhang, C. et al. "Understanding deep learning requires rethinking generalization." ICLR, 2017.
https://arxiv.org/abs/1611.03530

Computer Vision: Models, Learning, and Inference, by Simon J.D. Prince
https://www.amazon.co.uk/Compu....ter-Vision-Models-Le

Deep Learning Book, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
https://www.deeplearningbook.org/

Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
https://arxiv.org/abs/2210.00881

Computer Vision: Algorithms and Applications, 2nd ed. [Szeliski]
https://szeliski.org/Book/

A Spline Theory of Deep Networks [Randall Balestriero]
https://proceedings.mlr.press/....v80/balestriero18b/b

DEEP NEURAL NETWORKS AS GAUSSIAN PROCESSES [Jaehoon Lee]
https://arxiv.org/abs/1711.00165

Do Transformer Modifications Transfer Across Implementations and Applications [Narang]
https://arxiv.org/abs/2102.11972

ConvNets Match Vision Transformers at Scale [Smith]
https://arxiv.org/abs/2310.16764

Dr Travis LaCroix (Wrote Ethics chapter with Simon)
https://travislacroix.github.io/

Deep Learning powers most of the technologies we rely on every day. From Machine Translation that can translate web pages in seconds to recommendations of shows to watch or even face recognition that lets us log in to our phones many times a day.

Even though we use it every day, it might still not be clear to you how deep learning works. Let's dive into the behind-the-scenes of deep learning in this video.

Get your free speech-to-text API token 👇
https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=yt_mis_1

In this video, we will take a closer look at deep learning. We will learn what deep learning is, where in the world of Artificial Intelligence it stands, why it has been very successful in becoming part of our lives, and how it compares to more traditional machine learning algorithms.

Become better at machine learning in 5 min/ week 👉🏻 https://borismeinardus.substack.com/

In this video, I share how I would learn Machine Learning in 2024 if I could start over.
For the past 3 years, I have been studying machine learning (and 2 years before that basic computer science), which has now led me to work with an amazing ex-Meta professor, collaborate with Google DeepMind researchers, and have interviews at amazing companies.
Having learned from all of my failures and successes, this video breaks down how I would learn machine learning all over again, focusing on the essentials and learning from the best resources.
Enjoy!

👉🏻 Book a one-on-one call
https://calendly.com/boris-meinardus/consulting

👇🏻 Links to resources 👇🏻
==== Maths ====
https://www.edx.org/learn/prob....ability/harvard-univ
https://www.edx.org/learn/line....ar-algebra/the-unive
https://www.coursera.org/learn/matrix-algebra-engineers?irclickid=x-U2gpTSJxyLTxPwUx0Mo3EoUkDXeNXFjUFXWo0&irgwc=1
==== ML/ DL ====
https://www.coursera.org/speci....alizations/machine-l
https://www.youtube.com/playli....st?list=PLAqhIrjkxbu
https://www.coursera.org/specializations/deep-learning?irclickid=x-U2gpTSJxyLTxPwUx0Mo3EoUkDXeu01jUFXWo0&irgwc=1#courses
https://huggingface.co/learn/nlp-course/chapter1/1

⬇️ Follow me on my other socials and feel free to DM questions! ⬇️
⚫⚪ Medium: https://medium.com/@boris.meinardus
🐦 Twitter: https://twitter.com/BorisMeinardus

================== Timestamps ================
00:00 - Intro
00:40 - Python
01:29 - Maths
02:47 - ML Developer Stack
04:00 - Learn Machine Learning
06:06 - How To Really Get Good
=============================================

#ai #learning #machinelearning

Learn more about watsonx: https://ibm.biz/BdvxDS

What is really the difference between Artificial intelligence (AI) and machine learning (ML)? Are they actually the same thing? In this video, Jeff Crume explains the differences and relationship between AI & ML, as well as how related topics like Deep Learning (DL) and other types and properties of each.

#ai #ml #dl #artificialintelligence #machinelearning #deeplearning #watsonx

Demystifying attention, the key mechanism inside transformers and LLMs.
Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support
Special thanks to these supporters: https://www.3blue1brown.com/le....ssons/attention#than
An equally valuable form of support is to simply share the videos.

Demystifying self-attention, multiple heads, and cross-attention.
Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

The first pass for the translated subtitles here is machine-generated, and therefore notably imperfect. To contribute edits or fixes, visit https://translate.3blue1brown.com/

And yes, at 22:00 (and elsewhere), "breaks" is a typo.

------------------

Here are a few other relevant resources

Build a GPT from scratch, by Andrej Karpathy
https://youtu.be/kCc8FmEb1nY

If you want a conceptual understanding of language models from the ground up, @vcubingx just started a short series of videos on the topic:
https://youtu.be/1il-s4mgNdI?si=XaVxj6bsdy3VkgEX

If you're interested in the herculean task of interpreting what these large networks might actually be doing, the Transformer Circuits posts by Anthropic are great. In particular, it was only after reading one of these that I started thinking of the combination of the value and output matrices as being a combined low-rank map from the embedding space to itself, which, at least in my mind, made things much clearer than other sources.
https://transformer-circuits.p....ub/2021/framework/in

Site with exercises related to ML programming and GPTs
https://www.gptandchill.ai/codingproblems

History of language models by Brit Cruise,  @ArtOfTheProblem 
https://youtu.be/OFS90-FX6pg

An early paper on how directions in embedding spaces have meaning:
https://arxiv.org/pdf/1301.3781.pdf

------------------

Timestamps:
0:00 - Recap on embeddings
1:39 - Motivating examples
4:29 - The attention pattern
11:08 - Masking
12:42 - Context size
13:10 - Values
15:44 - Counting parameters
18:21 - Cross-attention
19:19 - Multiple heads
22:16 - The output matrix
23:19 - Going deeper
24:54 - Ending

------------------

These animations are largely made using a custom Python library, manim. See the FAQ comments here:
https://3b1b.co/faq#manim
https://github.com/3b1b/manim
https://github.com/ManimCommunity/manim/

All code for specific videos is visible here:
https://github.com/3b1b/videos/

The music is by Vincent Rubinetti.
https://www.vincentrubinetti.com
https://vincerubinetti.bandcam....p.com/album/the-musi
https://open.spotify.com/album..../1dVyjwS8FBqXhRunaG5

------------------

3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly.

Mailing list: https://3blue1brown.substack.com
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Website: https://www.3blue1brown.com

Become better at machine learning in 5 min/ week 👉🏻 https://borismeinardus.substack.com/

Learning machine learning is really hard, but during my 3.5 years of studying ML, I learned 5 secrets that made understanding ML much easier and helped me a lot in "mastering" it!
In this video, I will share these 5 secrets with you, so that you don't have to spend years figuring them out yourself.
Enjoy 💛

⬇️ Follow me on my other socials and feel free to DM questions! ⬇️
🔹 LinkedIn: https://www.linkedin.com/in/bo....ris-meinardus-ba2302
🐦 Twitter: https://twitter.com/BorisMeinardus

================== Timestamps ================
00:00 - Intro
00:29 - The Secret to Math 1
03:32 - The Secret to Math 2
05:36 - The Secret to Coding
07:53 - The Secret to Understanding Code
10:13 - The Secret to Mastering ML
=============================================

#ai #datascience #machinelearning

A complete guide to the mathematics behind neural networks and backpropagation.

In this lecture, I aim to explain the mathematical phenomena, a combination of linear algebra and optimization, that underlie the most important algorithm in data science today: the feed forward neural network.

Through a plethora of examples, geometrical intuitions, and not-too-tedious proofs, I will guide you from understanding how backpropagation works in single neurons to entire networks, and why we need backpropagation anyways.

It's a long lecture, so I encourage you to segment out your learning time - get a notebook and take some notes, and see if you can prove the theorems yourself.

As for me: I'm Adam Dhalla, a high school student from Vancouver, BC. I'm interested in how we can use algorithms from computer science to gain intuition about natural systems and environments.

My website: adamdhalla.com
I write here a lot: adamdhalla.medium.com
Contact me: [email protected]

Two good sources I recommend to supplement this lecture:

Terence Parr and Jeremy Howard's The Matrix Calculus You Need for Deep Learning: https://arxiv.org/abs/1802.01528

Michael Nielsen's Online Book Neural Networks and Deep Learning, specifically the chapter on backpropagation http://neuralnetworksanddeeple....arning.com/chap2.htm

ERRATA----
I'm pretty sure the Jacobians part plays twice - skip it when you feel like stuff is repeating, and stop when you get to the part about the "Scalar Chain Rule" (00:24:00).

And, here are the timestamps for each chapter mentioned in the syllabus present at the beginning of the course.

PART I - Introduction
--------------------------------------------------------------
00:00:52 1.1 Prerequisites
00:02:47 1.2 Agenda
00:04:59 1.3 Notation
00:07:00 1.4 Big Picture
00:10:34 1.5 Matrix Calculus Review
00:10:34 1.5.1 Gradients
00:14:10 1.5.2 Jacobians
00:24:00 1.5.3 New Way of Seeing the Scalar Chain Rule
00:27:12 1.5.4 Jacobian Chain Rule

PART II - Forward Propagation
--------------------------------------------------------------
00:37:21 2.1 The Neuron Function
00:44:36 2.2 Weight and Bias Indexing
00:50:57 2.3 A Layer of Neurons

PART III - Derivatives of Neural Networks and Gradient Descent
--------------------------------------------------------------
01:10:36 3.1 Motivation & Cost Function
01:15:17 3.2 Differentiating a Neuron's Operations
01:15:20 3.2.1 Derivative of a Binary Elementwise Function
01:31:50 3.2.2 Derivative of a Hadamard Product
01:37:20 3.2.3 Derivative of a Scalar Expansion
01:47:47 3.2.4 Derivative of a Sum
01:54:44 3.3 Derivative of a Neuron's Activation
02:10:37 3.4 Derivative of the Cost for a Simple Network (w.r.t weights)
02:33:14 3.5 Understanding the Derivative of the Cost (w.r.t weights)
02:45:38 3.6 Differentiating w.r.t the Bias
02:56:54 3.7 Gradient Descent Intuition
03:08:55 3.8 Gradient Descent Algorithm and SGD
03:25:02 3.9 Finding Derivatives of an Entire Layer (and why it doesn't work well)

PART IV - Backpropagation
--------------------------------------------------------------
03:32:47 4.1 The Error of a Node
03:39:09 4.2 The Four Equations of Backpropagation
03:39:12 4.2.1 Equation 1: The Error of the last Layer
03:46:41 4.2.2 Equation 2: The Error of any layer
04:03:23 4.2.3 Equation 3: The Derivative of the Cost w.r.t any bias
04:10:55 4.2.4 Equation 4: The Derivative of the Cost w.r.t any weight
04:18:25 4.2.5 Vectorizing Equation 4
04:35:24 4.3 Tying Part III and Part IV together
04:44:18 4.4 The Backpropagation Algorithm
04:58:03 4.5 Looking Forward

Don't click this: https://tinyurl.com/bde5k7d5

💚 Link to Code: https://www.patreon.com/greencode

How I Learned This: https://nnfs.io/ (by the awesome @sentdex )

I'm not an AI expert by any means, I probably have made some mistakes. So I apologise in advance :)

Also, I only used PyTorch to test the forward pass. Apart from that, everything else is written in pure Python (+ use of Numpy).


⭐ Other Social Media Links:
🔊 Discord: https://discord.gg/JsFRUtfMzq
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🔊 Music I Used in this Video: https://share.epidemicsound.com/7i1d0b



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In this beginner-friendly crash course, I’ll show you how to use real-world data with Python to create something cool from scratch.

Timeline:

00:00 - Getting Started
0:54 - What is Machine Learning
3:19 - Machine Learning Pipeline
7:18 - Popular ML Libraries & Tools
12:42 - Importing Data
20:17 - Jupyter Shortcuts
25:16 - A Real Machine Learning Problem
29:13 - Preparing the Data
32:34 - Learning & Predicting
37:28 - Calculating Model Accuracy
44:10 - Persisting Models
48:32 - Visualizing Decision Trees

Playlist Link: https://www.youtube.com/watch?v=rE9XJGakAUM&list=PLNIQLFWpQMRVKC_zohdgl3pjDk4rlN3jc

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MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2024 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini

For all lectures, slides, and lab materials: http://introtodeeplearning.com/

Lecture Outline
0:00​ - Introduction
7:25​ - Course information
13:37​ - Why deep learning?
17:20​ - The perceptron
24:30​ - Perceptron example
31;16​ - From perceptrons to neural networks
37:51​ - Applying neural networks
41:12​ - Loss functions
44:22​ - Training and gradient descent
49:52​ - Backpropagation
54:57​ - Setting the learning rate
58:54​ - Batched gradient descent
1:02:28​ - Regularization: dropout and early stopping
1:08:47 - Summary

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Join Jeff Crume as he dives into the distinctions between Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Foundation Models and how these technologies have evolved over time. He also explores the latest advancements in Generative AI, including large language models, chatbots, and deepfakes - and clarifies common misconceptions, simplifies complex concepts, and discusses the impact these technologies have on various fields.

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