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Mistral has release a free AI agent builder, powered by their large language model, Mistral Large 2.

With this agent builder, you can build a custom AI chatbot with your own set of instructions and prompt examples.

These AI agents are similar to Custom GPTs on ChatGPT and Projects inside of Claude, but you do not need a subscription to create them, use them or share them.

You can build your own AI agents here: https://chat.mistral.ai/chat

Use this GPT to create a system prompt: https://chatgpt.com/g/g-2Vxwgp....MMk-custom-instructi

Dave explains the reasons why and the steps needed to set up your own local AI engine ala ChatGPT. For my book on the autism spectrum, check out: https://amzn.to/3zBinWM

Helpful links and tips:
Install ollama:
curl -fsSL https://ollama.com/install.sh | sh
ollama serve

WSL Kernel Update Package: https://learn.microsoft.com/en....-us/windows/wsl/inst

Run the UI Docker Container:
Run UI with Docker:
docker run -d -p 3000:8080 --gpus=all -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama

Want to learn more? I’m launching a 6-week live BootCamp for AI Builders.
👉 Learn more: https://maven.com/s/course/13437a45a7
Save 50% at checkout with the code FOUNDER50

This is the 6th video in a series on using large language models (LLMs) in practice. Here, I review key aspects of developing a foundation LLM based on the development of models such as GPT-3, Llama, Falcon, and beyond.

More Resources:
▶️ Series Playlist: https://www.youtube.com/playli....st?list=PLz-ep5RbHos Read more: https://towardsdatascience.com..../how-to-build-an-llm

[1] BloombergGPT: https://arxiv.org/pdf/2303.17564.pdf
[2] Llama 2: https://ai.meta.com/research/p....ublications/llama-2-
[3] LLM Energy Costs: https://www.statista.com/stati....stics/1384401/energy
[4] arXiv:2005.14165 [cs.CL]
[5] Falcon 180b Blog: https://huggingface.co/blog/falcon-180b
[6] arXiv:2101.00027 [cs.CL]
[7] Alpaca Repo: https://github.com/gururise/AlpacaDataCleaned
[8] arXiv:2303.18223 [cs.CL]
[9] arXiv:2112.11446 [cs.CL]
[10] arXiv:1508.07909 [cs.CL]
[11] SentencePience: https://github.com/google/sent....encepiece/tree/maste
[12] Tokenizers Doc: https://huggingface.co/docs/tokenizers/quicktour
[13] arXiv:1706.03762 [cs.CL]
[14] Andrej Karpathy Lecture: https://www.youtube.com/watch?v=kCc8FmEb1nY&t=5307s
[15] Hugging Face NLP Course: https://huggingface.co/learn/n....lp-course/chapter1/7
[16] arXiv:1810.04805 [cs.CL]
[17] arXiv:1910.13461 [cs.CL]
[18] arXiv:1603.05027 [cs.CV]
[19] arXiv:1607.06450 [stat.ML]
[20] arXiv:1803.02155 [cs.CL]
[21] arXiv:2203.15556 [cs.CL]
[22] Trained with Mixed Precision Nvidia: https://docs.nvidia.com/deeple....arning/performance/m
[23] DeepSpeed Doc: https://www.deepspeed.ai/training/
[24] https://paperswithcode.com/method/weight-decay
[25] https://towardsdatascience.com..../what-is-gradient-cl
[26] arXiv:2001.08361 [cs.LG]
[27] arXiv:1803.05457 [cs.AI]
[28] arXiv:1905.07830 [cs.CL]
[29] arXiv:2009.03300 [cs.CY]
[30] arXiv:2109.07958 [cs.CL]
[31] https://huggingface.co/blog/ev....aluating-mmlu-leader
[32] https://www.cs.toronto.edu/~hi....nton/absps/JMLRdropo

--
Homepage: https://shawhintalebi.com/
Book a call: https://calendly.com/shawhintalebi

Intro - 0:00
How much does it cost? - 1:30
4 Key Steps - 3:55
Step 1: Data Curation - 4:19
1.1: Data Sources - 5:31
1.2: Data Diversity - 7:45
1.3: Data Preparation - 9:06
Step 2: Model Architecture (Transformers) - 13:17
2.1: 3 Types of Transformers - 15:13
2.2: Other Design Choices - 18:27
2.3: How big do I make it? - 22:45
Step 3: Training at Scale - 24:20
3.1: Training Stability - 26:52
3.2: Hyperparameters - 28:06
Step 4: Evaluation - 29:14
4.1: Multiple-choice Tasks - 30:22
4.2: Open-ended Tasks - 32:59
What's next? - 34:31

AI art is leaking into the mainstream in the form of stable diffusion and Lensa, but there are serious ethical concerns with this unregulated tech. I'm NOT anti AI, in fact, I believe AI can be of immense benefit to us in the future. But the ethics of AI in its current state MUST be talked about, in order to steer this tech in the right direction. Leave ur thoughts in the comments below!

Thumbnail art by the amazing @ CaraidArt on twitter
Check out Steven Zapata's GREAT VIDEO on the dangers of AI: https://youtu.be/tjSxFAGP9Ss

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Want to learn Generative AI? Attend the FREE Intro to Generative AI Masterclass here : https://shorturl.at/fCQZ6

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Do you want to join my team? Apply here: https://forms.gle/2iz4xmFvDCGnj2iZA

Access my code, templates and prompts inside of my community: https://www.skool.com/new-society

Follow me on Twitter - https://x.com/DavidOndrej1

My Google Colab: https://colab.research.google.....com/drive/1KHNjYT485
Andrej Karpathy Speech: https://youtu.be/fqVLjtvWgq8?si=77uSX3-jG6A6uecR
Sam Altman on GPT-5: https://twitter.com/H0wie_Xu/s....tatus/17456579924592

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AI Agents are the next big thing - and how to build your very own AI agent with Crew AI.

AI is not a one-size-fits-all technology; every AI project is customized to solve a specific business problem, with machine learning models. These models, which rely on data and algorithms, are what address the project’s needs. There are seven steps to building an effective machine learning model, from understanding the business problem, to preparing data, to adjusting the model in operation.

🔎 Read more:
Detailed explanation ➡️ https://www.techtarget.com/sea....rchenterpriseai/feat
Ease data preparation process with organization and automation➡️ https://searchdatamanagement.t....echtarget.com/featur

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🎬 *Table of Contents*

00:00 Intro

00:31 Make an AI Clone

01:09 Steps before recording sample video

02:08 Upload your sample video

04:18 Record your own voice & generate video

05:35 Download the video

05:47 Check the pricing plan

06:30 Get better result using finetune feature

Step into the future of video creation with this comprehensive guide to create your own AI clone using HeyGen Labs! Learn how to transform a simple video clip into a lifelike, talking avatar that lip-syncs to your voice seamlessly.

By the end of this video, you'll be ready to create your own stunning AI clone with HeyGen, opening up a world of creative possibilities for presentations, social media, marketing, and more!


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We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections to ChatGPT, which has taken the world by storm. We watch GitHub Copilot, itself a GPT, help us write a GPT (meta :D!) . I recommend people watch the earlier makemore videos to get comfortable with the autoregressive language modeling framework and basics of tensors and PyTorch nn, which we take for granted in this video.

Links:
- Google colab for the video: https://colab.research.google.....com/drive/1JMLa53HDu
- GitHub repo for the video: https://github.com/karpathy/ng-video-lecture
- Playlist of the whole Zero to Hero series so far: https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ
- nanoGPT repo: https://github.com/karpathy/nanoGPT
- my website: https://karpathy.ai
- my twitter: https://twitter.com/karpathy
- our Discord channel: https://discord.gg/3zy8kqD9Cp

Supplementary links:
- Attention is All You Need paper: https://arxiv.org/abs/1706.03762
- OpenAI GPT-3 paper: https://arxiv.org/abs/2005.14165
- OpenAI ChatGPT blog post: https://openai.com/blog/chatgpt/
- The GPU I'm training the model on is from Lambda GPU Cloud, I think the best and easiest way to spin up an on-demand GPU instance in the cloud that you can ssh to: https://lambdalabs.com . If you prefer to work in notebooks, I think the easiest path today is Google Colab.

Suggested exercises:
- EX1: The n-dimensional tensor mastery challenge: Combine the `Head` and `MultiHeadAttention` into one class that processes all the heads in parallel, treating the heads as another batch dimension (answer is in nanoGPT).
- EX2: Train the GPT on your own dataset of choice! What other data could be fun to blabber on about? (A fun advanced suggestion if you like: train a GPT to do addition of two numbers, i.e. a+b=c. You may find it helpful to predict the digits of c in reverse order, as the typical addition algorithm (that you're hoping it learns) would proceed right to left too. You may want to modify the data loader to simply serve random problems and skip the generation of train.bin, val.bin. You may want to mask out the loss at the input positions of a+b that just specify the problem using y=-1 in the targets (see CrossEntropyLoss ignore_index). Does your Transformer learn to add? Once you have this, swole doge project: build a calculator clone in GPT, for all of +-*/. Not an easy problem. You may need Chain of Thought traces.)
- EX3: Find a dataset that is very large, so large that you can't see a gap between train and val loss. Pretrain the transformer on this data, then initialize with that model and finetune it on tiny shakespeare with a smaller number of steps and lower learning rate. Can you obtain a lower validation loss by the use of pretraining?
- EX4: Read some transformer papers and implement one additional feature or change that people seem to use. Does it improve the performance of your GPT?

Chapters:
00:00:00 intro: ChatGPT, Transformers, nanoGPT, Shakespeare
baseline language modeling, code setup
00:07:52 reading and exploring the data
00:09:28 tokenization, train/val split
00:14:27 data loader: batches of chunks of data
00:22:11 simplest baseline: bigram language model, loss, generation
00:34:53 training the bigram model
00:38:00 port our code to a script
Building the "self-attention"
00:42:13 version 1: averaging past context with for loops, the weakest form of aggregation
00:47:11 the trick in self-attention: matrix multiply as weighted aggregation
00:51:54 version 2: using matrix multiply
00:54:42 version 3: adding softmax
00:58:26 minor code cleanup
01:00:18 positional encoding
01:02:00 THE CRUX OF THE VIDEO: version 4: self-attention
01:11:38 note 1: attention as communication
01:12:46 note 2: attention has no notion of space, operates over sets
01:13:40 note 3: there is no communication across batch dimension
01:14:14 note 4: encoder blocks vs. decoder blocks
01:15:39 note 5: attention vs. self-attention vs. cross-attention
01:16:56 note 6: "scaled" self-attention. why divide by sqrt(head_size)
Building the Transformer
01:19:11 inserting a single self-attention block to our network
01:21:59 multi-headed self-attention
01:24:25 feedforward layers of transformer block
01:26:48 residual connections
01:32:51 layernorm (and its relationship to our previous batchnorm)
01:37:49 scaling up the model! creating a few variables. adding dropout
Notes on Transformer
01:42:39 encoder vs. decoder vs. both (?) Transformers
01:46:22 super quick walkthrough of nanoGPT, batched multi-headed self-attention
01:48:53 back to ChatGPT, GPT-3, pretraining vs. finetuning, RLHF
01:54:32 conclusions

Corrections:
00:57:00 Oops "tokens from the _future_ cannot communicate", not "past". Sorry! :)
01:20:05 Oops I should be using the head_size for the normalization, not C

Easy System Administration Web Interface Raspberry Pi with Webmin! In this video i will be showing you how to setup your raspberry pi as a web administration system using Webmin. #raspberrypi #webmin #sstec tutorials.

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Get ready to master Webmin on Ubuntu 22.04! In this easy-to-follow tutorial, I'm Josh from KeepItTechie, and I'll guide you through every step of installing Webmin. Perfect for beginners and pros alike!

From updating your system to configuring your firewall, we've got it all covered. You'll be managing your server like a pro in no time!

👉 What's inside:

Introduction to Webmin and its benefits
Step-by-step installation instructions
Tips for accessing and using Webmin
Firewall configuration for secure access
❓ Questions? Drop them in the comments!

👍 Like and subscribe for more awesome Linux guides. Let's dive into the world of server management with ease and confidence! #Webmin #Ubuntu2204 #LinuxTutorial"

Linux Operating System | Beginners Crash Course - 3 Hours
https://youtu.be/BgGeGVqgt0s

Rocky Linux by CIQ: https://ciq.co/rocky-linux/

Remember to Like, Share, and Subscribe if you enjoyed the video! Also, if you are interested in more Linux content, please consider becoming a channel member so I can continue to produce great content!

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We will install IOTStack and Webmin on a raspberry Pi4. IOTStack is a complete collection of famous home automation software such as Home Assistant, Openhab, Node Red and many others. With this you can run your home automation software in docker on a pi.

0:00 Use a Pi as Home Automation Server
0:38 Intro jingle
0:48 TLDR/TLDW
1:01 description of IOTStack
2:06 Get IOTStack and Install Docker on the Pi
3:33 Installing IOTStack on the Pi
5:22 Installing Webmin
7:30 Call to Action

the commands used in this video

To download, install and start IOTStack:


sudo apt install -y git curl
git clone https://github .com/SensorsIot/IOTstack.git IOTstack
# please remove the space before the dot com
cd IOTstack/
./menu.sh
sudo raspi-config
locale

sudo reboot
cd IOTstack/
./menu.sh
docker-compose up -d



To install webmin:


wget https://prdownloads.sourceforge .net/webadmin/webmin_1.962_all.deb
# please remove the space before the dot net

clear
sudo dpkg -i webmin_1.962_all.deb
sudo apt -f install




Licence-free music on / Lizenzfreie Musik von https://www.terrasound.de/lize....nzfreie-musik-fuer-y

In this Python Programming Tutorial for Beginners video I am going to show you How to Install Pip packages on Ubuntu 18.04 or How to use Ubuntu 18.04 Linux for installing and managing Pip and PyPI (Python Package Index) Python packages. So What Is PIP for Python? The pip is a command line tool for installing and managing Python packages, which a generally available in the Python Package Index. The official website for Python Package Index (PyPI) is https://pypi.org. PyPI enables us to find and install python related software developed and shared by the Python community. How to Install PIP for Python on Windows, Mac, and Linux ?
So let us see How to Install Python Pip On Ubuntu 18.04?

Give the following command to get the help:
pip --help

Usage:
pip [command] [options]

Commands:
install Install packages.
download Download packages.
uninstall Uninstall packages.
freeze Output installed packages in requirements format.
list List installed packages.
show-Show information about installed packages.
check-Verify installed packages have compatible dependencies.
config - Manage local and global configuration.
search-Search PyPI for packages.
wheel-Build wheels from your requirements.
hash-Compute hashes of package archives.
completion-A helper command used for command completion.
help Show help for commands.

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DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!

In this video, I will be providing you with a walkthrough on how to build an advanced multi-agent AI app through Python, Langflow, Astra DB, Streamlit, and more. This app will use multiple LLMs that to handle different tasks, routing those tasks to different LLMs, and will even have a full frontend that allows you to interact with the completed application. It's one of the most advanced AI apps I've built on this channel so be sure to stick to the very end of this tutorial to learn everything it has to offer and have the power to build some insanely cool apps.

🎞 Video Resources 🎞
Get started with DataStax: https://dtsx.io/techwithtim-astradb
Langflow Github Rep: https://dtsx.io/techwithtim-langflow
Check out DataStax AI PaaS: https://dtsx.io/techwithtim-aipaas
Code in this video (prompts/code/flows): https://github.com/techwithtim..../Advanced-Multi-Agen

If you want to land a developer job check out my program with CourseCareers: https://techwithtim.net/dev

⏳ Timestamps ⏳
00:00:00 | Video Overview
00:01:26 | Project Demo
00:05:10 | Langflow Setup
00:08:03 | Building a Simple Flow/Agent
00:17:45 | Building a Complex Flow/Agent
00:39:35 | Using the AI From Python Code (Locally) | 00:39:35
00:46:50 | Using the AI From Python Code (Cloud)
00:53:40 | Frontend Setup
00:56:07 | Personal Data Form
01:01:57 | Saving Data in AstraDB
01:17:05 | Updating Data in AstraDB
01:26:27 | Nutrition Form
01:39:27 | Handling Notes
01:44:09 | Ask AI Feature

Hashtags
#aiagents #advanced #tutorial

Hello Everyone! Today in this video I am going to step-by-step guide you on How to install Python 3.11.4 on Windows 11. Windows (64-bit).

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✻ I really hope you find this video useful. ♥

✻ Please click on the CC button to read the subtitles of this video.

Step 1. How to Download Python 3.11.4?
https://www.python.org/downloads/
This page will lead to the Python download page.

Find the installation file and click on it to download. Here is this video we showed how to download and install Python in Windows 11.
Now after downloading Python save the file to the disk and follow the next step for installation.

Step 2. Install Python 3.11.4
Double-click to run the Python download file. Just follow the prompt as shown in the video and install Python 3.11.4 on your OS.

The instructions in this video tutorial can be applied to laptops, desktops, computers, and tablets that run the Windows 11 operating system like Windows 11 Home, Windows 11 Professional, Windows 11 Enterprise, and Windows 11 Education. This tutorial will work on all hardware manufacturers, like Dell, HP, Acer, Asus, Toshiba, Lenovo, and Samsung.

#Python3.11.4 #PythonInstallation #Pythontutorialforbeginners #Pythontutorial #Pythonprogramming #Pythonprogrammingtutorial #Pythonbasicsforbeginners #Windows11

Topics addressed in this tutorial:
How To Download And Install Python On Windows 10/11 64 bit OS
Install Python on Windows 10/11
How to Install Python and Get Started

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Chrome is Slow. Sometimes you might be frustrated with google chrome. That's why you should Ditch Google Chrome, create your own browser with 40 lines of python. This is super fun python project using PyQt5.

#python #project #pyqt5 #python​ #hack​ #python #tutorial​ #free​ #programming​ #fun​ #project​

Code: https://github.com/Programming....Hero1/my_cool_browse

If you face any coding related issues or anything, just join our Discord server and we're waiting for you: https://discord.gg/abk4RTjJjC

Credit for this video goes to Minhaj, Mujahid, Sufi, Shajedul, Geeks for Geeks (https://www.geeksforgeeks.org/....creating-a-simple-br ), and Google Chrome (please don't sue us). Also, we do not own any credit for the images used in this video. Credit goes to the actual owner/creator of the image.

Welcome to Sam Whitby Coding!

In this newest episode of Python Career Development you'll learn how to build web applications using Flask, one of the most popular Python web frameworks, and integrate databases with SQLAlchemy. Whether you're new to Flask or looking to expand your Python skill set, this tutorial will guide you through creating a fully functional web application.

By the end of this video, you'll have a solid foundation in Flask, understand how to work with databases in web apps, and be ready to apply these skills to real-world projects. This is an essential step in advancing your Python career!

Thank you very much for watching this video and I hope you learn something new and enjoy!

Chapters:
0:00 Intro
0:40 Installing Flask
1:15 The Flask Set Up
4:20 Rendering HTML Templates
8:30 Creating a Form
11:47 SQLAlchemy and Databases
15:45 Sign Up Page
18:34 Login Page
19:50 Displaying a Database
22:09 The Final Page
24:10 Quiz
25:05 Outro

Last video, Sorting and Searching Algorithms:
https://youtu.be/x4UX7vUPbt8?si=6EAJowPPzDdJFtwI

Episode 1 Python Career Development:
https://youtu.be/qpQ6o5h74xw?si=hSpnIqwHZOZAiJ6h


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Tags:
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