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.

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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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Facebook: https://www.facebook.com/3blue1brown
Patreon: https://patreon.com/3blue1brown
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: adamdhalla@protonmail.com

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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Current Subs: 14,219

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

🚩 Subscribe ➜ https://bit.ly/45IwoxJ

👇 Follow Me On Social Media:

Github: https://github.com/harishneel1
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Instagram: https://www.instagram.com/hari....sh_neel?igsh=MXEwNXA

Was this video on Python using Machine Learning helpful? Let me know your thoughts in the comments.

#python #artificialintelligence #machinelearning

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

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us on @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

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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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🚀 Dive into the world of Excel like never before with the power of ChatGPT! 📊 Whether you're a seasoned spreadsheet pro or just starting your Excel journey, this video is your gateway to unleashing the full potential of Microsoft Excel. 🔍 In this tutorial, we'll explore innovative ways to streamline your data management using ChatGPT, the cutting-edge language model from OpenAI. From time-saving shortcuts to advanced formula magic, you'll learn how to elevate your Excel game to new heights.

📈 Key Highlights:

💡 Boost Your Productivity: Discover time-saving tips and tricks that will transform the way you work in Excel.
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This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

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Welcome to our in-depth tutorial on Ethical HackerGPT! In this video, we'll explore HackerGPT, a specialized version of ChatGPT designed specifically for cybersecurity and ethical hacking. With cyberattacks occurring every 39 seconds and causing billions in damages, HackerGPT provides crucial tools and knowledge to protect against these threats. Discover how HackerGPT can guide you through a variety of topics, including performing security assessments, ethical hacking techniques, and scripting for system security. HackerGPT adheres strictly to ethical guidelines, ensuring all guidance aligns with legal and professional standards. Learn how to become a skilled cybersecurity professional, equipped to defend against cyber threats ethically and effectively. Join us for this engaging and informative tutorial, and enhance your cybersecurity skills with HackerGPT.

Here in this Ethical HackerGPT Tutorial, we cover the following topics:

00:00 Introduction to Cyber Security
02:10 Exploring Ethical Hacker GPT
04:33 Asking General Cybersecurity prompts from Ethical HackerGPT
06:05 Asking Ethical Hacking prompts from Ethical HackerGPT
07:33 Asking Scripting and Automation prompts from Ethical HackerGPT
09:39 Asking Specific Security Tools prompts from Ethical HackerGPT
10:47 Asking Incident Response prompts from Ethical HackerGPT
12:40 Asking Advanced Topics prompts from Ethical HackerGPT
15:55 Asking Cybersecurity Policies and Compliance prompts from Ethical HackerGPT

FAQs

1. What is HackerGPT and how does it work?
HackerGPT is a specialized version of ChatGPT designed for cybersecurity and ethical hacking. It provides guidance on a wide range of security topics, including vulnerability assessments, ethical hacking techniques, and scripting for system security. HackerGPT helps users understand and mitigate potential cyber threats while adhering to legal and ethical guidelines.

2. How can HackerGPT help improve my cybersecurity skills?
HackerGPT offers expert advice and practical solutions for various cybersecurity challenges. It covers topics such as security assessments, ethical hacking, scripting for automation, and using security tools like Burp Suite and Nmap. This guidance helps users enhance their knowledge and skills in defending against cyber threats.

3. Is HackerGPT safe and ethical to use?
Yes, HackerGPT is designed to follow strict ethical guidelines. It does not provide assistance for any queries deemed unethical or illegal. Its purpose is to help users learn and practice cybersecurity techniques responsibly, ensuring all guidance aligns with legal and professional standards.

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#HackerGPT #hacker #cybersecurity #EthicalHacking #Hacking #2024 #simplilearn

⏩ Check out the Cybersecurity videos: https://youtube.com/playlist?l....ist=PLEiEAq2VkUUJfPO

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In this video on chatgpt for developers unlock the potential of ChatGPT for your projects with this concise video guide. Learn the fundamentals, explore practical examples, and integrate AI seamlessly into your applications. Whether you're a novice or an expert, this tutorial will help you harness the power of ChatGPT to create dynamic and engaging user experiences.

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➡️ About Post Graduate Program In AI And Machine Learning

This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

✅ Key Features

- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools

✅ Skills Covered

- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…

👉 Enroll Now: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=25Apr2024ChatGPTForDevelopers&utm_medium=Description&utm_source=youtube
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In this ChatGPT in Cybersecurity video, we delve into the role of ChatGPT in the realm of cybersecurity. Discover how this powerful language model assists in threat detection, vulnerability assessment, and incident response. Gain insights into the innovative ways ChatGPT is shaping the future of cybersecurity. Join us to explore the fascinating intersection of AI and cybersecurity.

Here in this ChatGPT in Cybersecurity video, we start with:

00:00 INtroduction to Cyberrsecurity and Artificial Intelligence
02:11 What is ChatGPT?
02:49 Uses of ChatGPT in Cybersecurity
08:12 Hands on Lab Demonstration

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➡️ About Post Graduate Program In Cyber Security

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This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

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- Explainable AI
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