Learn more: https://bit.ly/4mVUPyF

Building effective Agentic AI systems is one of the most valuable skills in AI today.

Introducing Agentic AI, a new course from Andrew Ng, now available on DeepLearning.AI.

While most developers build AI that just responds to prompts, the most productive teams are building AI that executes multi-step workflows autonomously. 

In this course, you'll learn how to build 4 key agentic workflows using raw Python so that you'll see how each step actually works.

Skills you’ll gain: 

- Build agentic design patterns: reflection, tool use, planning, and multi-agent workflows
- Integrate AI with external tools: databases, APIs, web search, and code execution
- Evaluate and optimize AI systems: performance metrics, error analysis, and production deployment

Build everything from scratch in Python—no frameworks, no black boxes.

Agentic AI is available exclusively on DeepLearning.AI. With a Pro membership you get access to quizzes, code labs, and Professional Certifications along with early access to our available catalog of advanced AI courses.

Enroll in Agentic AI on DeepLearning.AI: https://bit.ly/4mVUPyF

MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Amini
** New 2025 Edition **

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

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

Welcome to the first video in my Deep Learning With Pytorch playlist!

In this video I'll talk a little bit about what a Neural Network is, and give you a little bit of background history on Pytorch vs. Tensorflow.

Then we'll set up a development environment using Google Colab, and create a first notebook and push it to a Github repository.

#pytorch #codemy #JohnElder

Timecodes

0:00​​ - Introduction
1:12 - Pytorch vs. Tensorflow
2:32 - What is a Neural Network?
4:10 - How Much Math Do You Need?
7:14 - Pytorch Documentation
7:37 - Development Environment Google Colab
9:09 - Use GPU Runtime
10:24 - Check Latest Version of Pytorch
12:04 - Set Up Github Repository
13:36 - Authorize Colab In Github
14:35 - Push Notebook To Github
16:09 - Conclusion

In this video I show how to implement linear regression using the normal equation. If you want to check out the derivation I made a blog post that you can check out: https://aladdinperzon.github.i....o/2020/03/25/Linear-
People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

✅ Equipment I use and recommend:
https://www.amazon.com/shop/aladdinpersson

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LinkedIn - https://www.linkedin.com/in/al....addin-persson-a95384
GitHub - https://github.com/aladdinpersson

PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

I was inspired to make these videos by this specialization: https://bit.ly/3SqLuA6

Progressive Growing of GANs for Improved Quality, Stability, and Variation: https://arxiv.org/abs/1710.10196

#PaperReview #PaperExplained

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

✅ Equipment I use and recommend:
https://www.amazon.com/shop/aladdinpersson

❤️ Become a Channel Member:
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✅ One-Time Donations:
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LinkedIn - https://www.linkedin.com/in/al....addin-persson-a95384
Github - https://github.com/aladdinpersson

GAN Playlist:
https://youtube.com/playlist?l....ist=PLhhyoLH6IjfwIp8

PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

Timestamps:
0:00 - Introduction
0:34 - Overview
5:06 - Progressive growing
7:36 - MiniBatch Std
9:15 - Fading in new layers
13:37 - Normalization (PixelNorm & Eq. LR)
20:30 - Some results
20:55 - Implementations details
30:59 - Ending

#8k #china #60fps
Best of China 8K HDR Ultra HD - Relaxing nature movie with soothing music

This film was created for educational, entertainment and informative purposes.
Some footage have been originally recorded in 8K resolution and some in 4K. I upscaled all 4K footage to 8K resolution, resulting something new.
This film was re-edited in HDR10 standard and color corrected (high color correction , color grading , adjusted the blacks, adjusted the highlights and the saturation).
To watch this video in real HDR, you should use a HDR television set.
There are a lot of interesting facts that you probably didn’t know.
Shanghai is a financial and cultural hub for the entire world. Shanghai has the longest metro system in the world with 644 km of tunnels and track.
"The Pearl of Asia" and "The Paris of the East" are two nicknames for Shanghai.
Hong Kong is famous for many towering skyscrapers. The tallest skyscraper in Hong Kong is The International Commerce Center. Hong Kong is recognized as the crossroads of East and West.
Hangzhou is a famous tourist destination in this country. One of China's seven historic capitals is Hangzhou. Hangzhou has been known as "Capital of Tea" since ancient times.

#scenicrelaxation #8kvideo #relaxationmusic #relaxationfilm #8kvideoultrahd
------------------------------

🌿Welcome to the new relaxing music stream on Scenic Relaxation 8K channel. You can keep the video at low volume and start doing any work like studying, working, reading… or simply relaxing or getting a good night's sleep.

🌿Music to relax, meditate, study, read, massage, spa or sleep. This type of music is ideal to combat anxiety, stress or insomnia as it facilitates relaxation and helps us to get rid of bad vibrations. You can also use this music as a background for guided meditation classes or sleep relaxation.

🌿If you enjoyed the live stream and want more relaxing music content, don't forget to like and subscribe!

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🎹More soothing music on Spotify playlist: https://spoti.fi/38bwOia

Music By:
"---------------------------------------------
🌿 Music by Helios Records:
➤ Spotify Relaxing: https://heliosrecords.fanlink.to/SpotifyRelaxing
➤ Spotify Concentration: https://heliosrecords.fanlink.....to/SpotifyConcentrat
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➤ Spotify: https://spoti.fi/3kJuAy3

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

"🌞 For contact and submit music: [email protected]

►All rights belong to their respective owners.
✔ This video was given a special license directly from the artists and the right holders."

So what was the breakthrough that allowed deep nets to combat the vanishing gradient problem? The answer has two parts, the first of which involves the RBM, an algorithm that can automatically detect the inherent patterns in data by reconstructing the input.

Deep Learning TV on
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Twitter: https://twitter.com/deeplearningtv

Geoff Hinton of the University of Toronto, a pioneer and giant in the field, was able to devise a method for training deep nets. His work led to the creation of the Restricted Boltzmann Machine, or RBM.

Structurally, an RBM is a shallow neural net with just two layers – the visible layer and the hidden layer. In this net, each node connects to every node in the adjacent layer. The “restriction” refers to the fact that no two nodes from the same layer share a connection.

The goal of an RBM is to recreate the inputs as accurately as possible. During a forward pass, the inputs are modified by weights and biases and are used to activate the hidden layer. In the next pass, the activations from the hidden layer are modified by weights and biases and sent back to the input layer for activation. At the input layer, the modified activations are viewed as an input reconstruction and compared to the original input. A measure called KL Divergence is used to analyze the accuracy of the net. The training process involves continuously tweaking the weights and biases during both passes until the input is as close as possible to the reconstruction.

If you’ve ever worked with an RBM in one of your own projects, please comment and tell me about your experiences.

Because RBMs try to reconstruct the input, the data does not have to be labelled. This is important for many real-world applications because most data sets – photos, videos, and sensor signals for example – are unlabelled. By reconstructing the input, the RBM must also decipher the building blocks and patterns that are inherent in the data. Hence the RBM belongs to a family of feature extractors known as auto-encoders.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

GPT is the first of the papers which proved the effectiveness of unsupervised pre-training for language processing tasks. This video is about GPT-1 which became quite an impactful work in the series of GPT papers that we now have (GPT-2 and GPT-3).


Paper: https://www.cs.ubc.ca/~amuham0....1/LING530/papers/rad
code: https://github.com/openai/finetune-transformer-l
Official OpenAI blog: https://openai.com/blog/language-unsupervised/


Paper Abstract:
Natural language understanding comprises a wide range of diverse tasks suchas textual entailment, question answering, semantic similarity assessment, anddocument classification. Although large unlabeled text corpora are abundant,labeled data for learning these specific tasks is scarce, making it challenging fordiscriminatively trained models to perform adequately. We demonstrate that largegains on these tasks can be realized bygenerative pre-trainingof a language modelon a diverse corpus of unlabeled text, followed bydiscriminative fine-tuningon eachspecific task. In contrast to previous approaches, we make use of task-aware inputtransformations during fine-tuning to achieve effective transfer while requiringminimal changes to the model architecture. We demonstrate the effectiveness ofour approach on a wide range of benchmarks for natural language understanding.Our general task-agnostic model outperforms discriminatively trained models thatuse architectures specifically crafted for each task, significantly improving upon thestate of the art in 9 out of the 12 tasks studied. For instance, we achieve absoluteimprovements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% onquestion answering (RACE), and 1.5% on textual entailment (MultiNLI).


AI Bites
YouTube: https://www.youtube.com/c/AIBites
Twitter: https://twitter.com/ai_bites
Patron: https://www.patreon.com/ai_bites
github: https://github.com/ai-bites

In this video, I go over how to download and run the open-source implementation of GPT3, called GPT Neo. This model is 2.7 billion parameters, which is the same size as GPT3 Ada. The results are very good and are a large improvement over GPT-2. I am excited to play around with this model more and for the future of even larger NLP models.

Notebook https://github.com/mallorbc/GPTNeo_notebook
GPT Neo github https://github.com/EleutherAI/gpt-neo (use the first release tag)
GPT Neo HuggingFace docs https://huggingface.co/transfo....rmers/model_doc/gpt_
A useful article about transformer parameters https://huggingface.co/blog/how-to-generate

00:00 - GPT3 Background
01:07 - GPT3 Interview
02:06 - GPT Neo Github
03:14 - GPT Neo HuggingFace
03:52 - Setting up Anaconda and Jupyter
05:05 - Starting the Jupyter notebook
06:14 - Installing dependencies in the notebook
06:58 - Importing needed dependencies
07:33 - Selecting what GPT model to use
08:45 - Checking our computer hardware
09:36 - Loading the tokenizer
09:55 - Giving our inputs
10:39 - Generating the tokens with the model
11:42 - Decoding and reading the result
13:22 - Reflections on Transformers
14:26 - Outro questions future work

NEWSLETTER ✉️ http://DylanCurious.com
PATREON 💰 https://patreon.com/DylanCurious (Monthly Video Call)

In today's episode, we're charting the course of Artificial General Intelligence (AGI) as of June 2023, guided by the timeline presented by AI expert Dr. Alan D. Thompson.

A critical turning point came in 2017, with the birth of the attention mechanism by Google researchers, paving the way for the creation of Transformers. These are the foundation of powerful AI models like GPT, and according to Dr. Thompson, we're about halfway to achieving AGI from here.

The period between 2017 and 2023 has been rich with major AI advancements, each one a stepping stone on our path to AGI. These include our deeper understanding of GPT-3's capabilities, innovative applications of AI in microchip design, and the emergence of multitasking models like DeepMind's Gato.

By 2020, we had made roughly 30% progress towards AGI, nudging up to 31% by 2021. Significant leaps, like the unveiling of GPT-3's remarkable abilities and the use of AI in hardware design, were clear indicators of this progress. In May 2022, the introduction of DeepMind's Gato model propelled us to 39% completion, thanks to its impressive capabilities and the demonstration of human-like general intelligence.

As 2022 came to a close, progress continued with developments in autonomous AI systems and AI-assisted human tasks. Into 2023, we saw AGI estimates rise to 41%, largely due to Microsoft's innovative use of ChatGPT in operating robotic components. The introduction of Google's PaLM-E 562B, capable of planning its own future based on visual and language data, took AGI estimates up to 42%.

The launch of the even more powerful GPT-4 model and the integration of AI into robotics have substantially fast-tracked our journey towards AGI. Stay tuned to "Curious Future" for more updates on the fascinating world of AI, and don't forget to subscribe for the latest in AI, technology, and innovation.

CURIOUS FUTURE: @dylan_curious
↪ https://www.youtube.com/channe....l/UCpdyFxSktWo3W6kMY

CURIOUS PODCAST: @dontsweatitpod
↪ https://www.youtube.com/channe....l/UChChPrDzfifYB9Si1

CURIOUS FRIENDS: @vegasfriends
↪ https://www.youtube.com/channe....l/UCJ2Q3smwCLmbEDDA3

00:00 - Artificial General Intelligence, 50% Complete: Fact or Fiction?
00:42 - Did Google’s Invention of the Transformers Take Us 20% of the Way to AGI?
01:23 - 30% to AGI in 2020? Unraveling the AI Progress Debate
02:24 - AI Designing AI: The 31% Milestone Towards AGI in 2021
03:14 - DeepMind’s Gato Model: The Super Multitasker Pushing AGI to 39%?
03:48 - We Are 39% Of The Way To AGI: Unraveling Antropic’s Human Reinforcement Method
04:55 - Microsoft’s ChatGPT Pushed Us 41% Closer To AGI
05:49 - AGI at 42%: After Google’s PaLM-E 562B’s Shows Adaptive Abilities
07:19 - AGI at 48%: What Does this Mean for AI’s Future?
08:43 - Humans at 94%, AI at 90.9% | AGI Progress Is Moving Fast
09:37 - 2 Milestone To Look For Before AGI Arrives On Earth

SOURCES:
https://lifearchitect.ai/agi/
https://ai.googleblog.com/2017..../08/transformer-nove
https://www.youtube.com/watch?v=HrV19SjKUss&t=175s&ab_channel=MachineLearningStreetTalk
@MachineLearningStreetTalk
https://www.theverge.com/2021/....6/10/22527476/google
https://www.nature.com/articles/s41586-021-03544-w
https://lifearchitect.ai/the-sky-is-on-fire/
https://www.youtube.com/watch?v=6fWEHrXN9zo&list=PLqJbCeNOfEK-o63ACEKEbwE6-XpEXXS_I&ab_channel=DrAlanD.Thompson
https://en.wikipedia.org/wiki/Gato_(DeepMind)
https://www.deepmind.com/publi....cations/a-generalist
@DrAlanDThompson
https://developer.nvidia.com/b....log/designing-arithm
https://www.microsoft.com/en-u....s/research/group/aut
https://www.youtube.com/watch?v=wLOChUtdqoA&ab_channel=MicrosoftAutonomousSystems%26RoboticsResearch
@msftautonomoussys
https://palm-e.github.io/
https://arxiv.org/abs/2303.12712
https://www.youtube.com/watch?v=XyCKe3rrYik&ab_channel=STrucBot
https://www.fastcompany.com/90....889271/boston-dynami
https://tidybot.cs.princeton.edu/
https://www.youtube.com/watch?v=Vq_DcZ_xc_E&ab_channel=AgilityRobotics
@Structon
@AgilityRobotics

WATCH THE FULL VIDEO ⤵
https://www.youtube.com/watch?v=xyzY3cj7tTA

#ai #artificialintelligence #tech #agi #gpt3 #gpt4 #deepmind #chatgpt #robotics #google #microsoft #machinelearning #autonomousai #palm-e562b #gato #transformers #futuretech #innovation #aitechnology #aitimeline #curiousfuture #alanthompson #artificialgeneralintelligence #aidesign #aipredictions #aiadvancements #aiupdates

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Top 20 Most Important Excel Formulas by simplilearn is dedicated to guide the aspiring data analysts and business analysts with the top Excel Formulas For Data Analysis in 2024 and beyond. This Excel tutorial for beginners by Simplilearn will cover the following topics. In this video, we learn into the top 20 to 25 most important Excel formulas every data analyst needs to excel in their role.This excel tutorial will help you to refine your skills, and building a strong foundation in Excel proficiency.

0:00:00 How To Change Lower Case To Upper Case In Excel?
0:01:14 How To Add Rows In Excel?
0:02:47 How To Add Columns In Excel?
0:05:44 How To Select Entire Column In Excel?
0:08:32 How to Compare Two Columns In Excel?
0:15:51 How To Convert Rows To Columns In Excel?
0:17:28 How to Group Rows in Excel?
0:19:13 How To Remove Blank Rows In Excel?
0:23:00 How To Freeze Row In Excel Tutorial
0:25:29 How to Convert Number to Words in Excel?
0:28:14 Combining Data From Multiple Cells In Excel
0:30:42 How To Merge Cells In Excel
0:35:27 How to Add Date in Excel?
0:37:03 How To Change Date Format In Excel (dd/mm/yyyy) To (mm/dd/yyyy)
0:40:57 How To Calculate Age In Excel From A Date Of Birth?
0:42:51 How To Calculate Time Difference in Excel?
0:44:29 DAX In Excel Explained
0:49:34 Checkboxes In Excel
0:53:45 How to Insert Excel in PPT?
0:55:51 How To Insert Image In Excel?
0:59:18 How To Insert PDF in Excel Sheet ?
1:02:46 How To Convert PDF To Excel

✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

#excel #DataAnalysis #exceltutorial #msexcel #2024 #Simplilearn

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In this video, we dive deep into Generative AI, exploring the power of Large Language Models like ChatGPT. Discover how these advanced models generate human-like text and uncover their possibilities for various applications. Want to learn more about it? Visit our blog for an in-depth exploration of Generative AI, LLMs, and ChatGPT's capabilities! https://bit.ly/3WXuHIi

#artificialintelligence #generativeai #largelanguagemodels #gpt #chatgpt #deeplearning #machinelearning #explainervideo #simpleshow

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ChatGPT Full Course 2024 by simplilearn is a latest variant of ChatGPT Full Course. the couse will guide the aspiring AI Engineers to learn the critical AI and machine learning and ChatGPT AI models from the very Basic To Advanced This ChatGPT Tutorial by simplileatn will cover how to use ChatGPT 4 Omni, Data Analysis with chatGPT, programming with chatgpt, automation with chatgpt and much more. The ChatGPT full course with 4 Omni demonstration will cover the following topics:

00:00:00 ChatGPT Full Course For Beginners 2023
00:02:22 Top AI Tools
00:00:07:50 What is GPT 4?
00:13:52 Automate Excel using ChatGPT
01:24:12 How to Build a Website Using ChatGPT
02:06:35 How To Use ChatGPT For Web Development
02:32:22 Can ChatGPT Solve LeetCode Problems?
04:02:21 Create a Telegram Bot Using ChatGPT
04:19:21 Automate WhatsApp using ChatGPT
04:29:45 Build a Game using Python and ChatGPT
04:54:25 ChatGPT in Cybersecurity
05:24:09 ChatGPT for UI/UX Design
06:10:28 How to use ChatGPT for SEO?
07:02:19 Chat GPT for Content Creation
07:06:14 Master SEO Content Using ChatGPT
7:11:53 Explore 10 Mind-Blowing GPTs Inside ChatGPT - WebPilot Will Amaze You
7:23:43 OpenAI ChatGPT-4o
7:40:21 How To Use ChatGPT Memory ?
7:45:51 ChatGPT For Voice Activated AI Assiatant
7:58:04 How To Use ChatGPT Plugins - Step-By-Step Guide
8:07:15 ChatGPT 4.0 vs Gemini 1.5
8:17:11 WEB SCRAPPING Using CHATGPT
8:21:38 How To Use AI In Excel ?
8:52:36 ChatGPT Made This Excel Dashboard 😱😱
9:03:03 How To Write Resume Using ChatGPT
9:08:40 What Is Multimodal AI?

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Transformer Neural Networks are the heart of pretty much everything exciting in AI right now. ChatGPT, Google Translate and many other cool things, are based on Transformers. This StatQuest cuts through all the hype and shows you how a Transformer works, one-step-at-a time.

NOTE: If you're interested in learning more about Backpropagation, check out these 'Quests:
The Chain Rule: https://youtu.be/wl1myxrtQHQ
Gradient Descent: https://youtu.be/sDv4f4s2SB8
Backpropagation Main Ideas: https://youtu.be/IN2XmBhILt4
Backpropagation Details Part 1: https://youtu.be/iyn2zdALii8
Backpropagation Details Part 2: https://youtu.be/GKZoOHXGcLo

If you're interested in learning more about the SoftMax function, check out:
https://youtu.be/KpKog-L9veg

If you're interested in learning more about Word Embedding, check out: https://youtu.be/viZrOnJclY0

If you'd like to learn more about calculating similarities in the context of neural networks and the Dot Product, check out:
Cosine Similarity: https://youtu.be/e9U0QAFbfLI
Attention: https://youtu.be/PSs6nxngL6k

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0:00 Awesome song and introduction
1:26 Word Embedding
7:30 Positional Encoding
12:53 Self-Attention
23:37 Encoder and Decoder defined
23:53 Decoder Word Embedding
25:08 Decoder Positional Encoding
25:50 Transformers were designed for parallel computing
27:13 Decoder Self-Attention
27:59 Encoder-Decoder Attention
31:19 Decoding numbers into words
32:23 Decoding the second token
34:13 Extra stuff you can add to a Transformer

#StatQuest #Transformer #ChatGPT

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