سرفہرست ویڈیوز | Cash In Vids Make Money From Vids And Vloging CashInVids CashInVids.com

گھر
مضامین

سرفہرست ویڈیوز

تمام وقت

آج

اس ہفتے

اس مہینے

اس سال

Create Realistic Ai Influencer for Free | AI Model Instagram 2024 | Create Your Own Ai Influencer 121gamers 2 مناظر • پہلے 8 مہینے

Create Realistic Ai Influencer for Free | AI Model Instagram 2024 | Create Your Own Ai Influencer
#aiinfluencer #aimodel #instagraminfluencer

Hello Friends! In this incredible video tutorial, you're going to explore realistic AI creation and its impact. We'll show you the process of making amazingly impressive AI influencer profiles, as well as videos that make you doubt the truth of. From gathering sources to making breathtaking video and even face-changing, this class will change your understanding of artificial intelligence. If you're an experienced techie or just interested in AI's capabilities, AI this video tutorial should be watched. Learn the hidden secrets behind AI and allow people to convey their condolences. Let's look at what's coming the next step for influencer culture and AI imagination!
How to create a realistic AI influencer for free using only AI tools. I have used AI tools like Tensor Art, Insight Face bot, and Google Colab to create AI Instagram models and earn money through Instagram channel.
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

Step to Create Ai Influencer 🔗Links:
👌STEP-01➡️
TensorArt: https://tensor.art/
Demo Model: https://tensor.art/images/700972640476656117?model_id=700914916103796439
👌STEP-02 ➡️
Discord: https://discord.com/channels/@me
Face Swap: https://discord.com/oauth2/authorize?client_id=1090660574196674713&permissions=274877945856&scope=bot
👌STEP-03 ➡️
Google Colab: https://colab.research.google.....com/gist/trickydaya/
👌STEP-04 ➡️
🖼️Free Webste to Download Images & Videos:
a) Freepik: https://www.freepik.com/
b) Pexels: https://www.pexels.com/
c) Pixabay: https://pixabay.com/
d) Coverr: https://coverr.co/
🖼️Paid Webste to Download Images, Videos & Templates:
a) Envatoelements: https://elements.envato.com/
b) Storyblocks: https://www.storyblocks.com/
c) Istocks: https://www.istockphoto.com/
d) Shutterstock: https://www.shutterstock.com/
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
👧To Create Realistic AI Influencers Timestamps:
00:00 - Introduction
00:48 - Create Realistic AI Influencer Roadmap
01:33 - Generate Realistic AI Influencer Using Tensor Art
03:48 - AI Generated Pictures
04:07 - How to Generate face swap Images from same AI Influencer's face
04:38 - Face Swap Using Insight Face Bot
05:18 - How to Download Pictures and Videos
06:10 - Generate Instagram Reels & Videos Using Google Colab AI
07:04 - Create Instagram Account
07:08 - Create AI Influencer's Bio & Post
07:19 - Conclusion
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
👍Aaditri Official Instagram ID: https://www.instagram.com/aaditritheai/

⚠ Disclaimer:
I don't recommend using videos of other people for deepfakes. Try to make them yourself; ask your girlfriend, sister, mother, or anyone on Fiverr to do it. It was shown in the tutorial to demonstrate the capabilities of AI.
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
👍Credit:
Instagram links: https://www.instagram.com/naina_avtr/?hl=en
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
🌐 Stay Connected:
▶YouTube: www.youtube.com/@theailearning
▶Twitter: https://twitter.com/theailearning
▶Facebook: https://www.facebook.com/theailearning
▶Instagram: https://www.instagram.com/theailearning
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
📺 Related Videos:
📹AI Infographic Generator form Text: https://youtu.be/fEtMYjXPItc
📹How to Create Realistic AI Influencer From Shakker AI: https://youtu.be/1Wp2ToHSNnA
📹How to Make Animated Cartoon Video Using AI 2024: https://youtu.be/n714HoPxV1Q
📹Create Viral Talking AI Avatar for Free: https://youtu.be/gL_YBXIPS4c
📹Best Ways To Turn Any Video To Animation AI 2024: https://youtu.be/-Re1ydSt3z4
📹We Create Realistic AI Influencer 2024 | Ai Consistent Characters & Clothes Change AI | DeFooocus AI: https://youtu.be/vRqwWknZHnY
📹Create Your Own Realistic Indian AI Influencer: https://youtu.be/1-E7fvhbIwM
📹Google Colab Deepfake Face Swap: https://youtu.be/yJlQ_ZXALlI
📹Text to Video AI Free Tool & Images to VIdeo AI 2024: https://youtu.be/LX0cIyQgxHo
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

📩 Business Inquiries: technicaldayanand@gmail.com

How to Build an LLM from Scratch | An Overview 121gamers 2 مناظر • پہلے 8 مہینے

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

Let's build GPT: from scratch, in code, spelled out. 121gamers 2 مناظر • پہلے 8 مہینے

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

What is LDAP and Active Directory ? How LDAP works and what is the structure of LDAP/AD? 121gamers 2 مناظر • پہلے 8 مہینے

Best AI Mindmap (desktop): https://bit.ly/46Q4TTn

#ldap #ldapauth #ldapauthentication
What is LDAP and Active Directory ? How LDAP works and what is the structure of LDAP/AD?

In this video, I have discussed regarding LDAP, Active Directory, LDAP vs Active Directory, Structure of LDAP, How LDAP works, LDAP Authentication.

GitHub:
https://github.com/talenteddeveloper

LDAP in Java:
https://www.youtube.com/playli....st?list=PLyzY2l387Al

LDAP Updated:
https://www.youtube.com/playli....st?list=PLyzY2l387Al

What is LDAP?
LDAP, the Lightweight Directory Access Protocol, is a mature, flexible, and well supported standards-based mechanism for interacting with directory servers. It’s often used for authentication and storing information about users, groups, and applications, but an LDAP directory server is a fairly general-purpose data store and can be used in a wide variety of applications

How Does LDAP work?
In short, LDAP specifies a method of directory storage that allows for adding, deleting, and modifying records, and it enables the search of those records to facilitate both authentication and authorization of users to resources.

LDAP’s three main functions are:

Update: This includes adding, deleting, or modifying directory information.

Query: This includes searching and comparing directory information.

Authenticate: The main authentication functions include binding and unbinding; a third function, abandon, can be used to stop a server from completing an operation

LDAP Directory Information Tree
LDAP organizes information in a hierarchical tree structure, referred to as a directory information tree (DIT). The LDAP DIT can vary based on the software or directory service you use; however, LDAP directories generally follow this tree structure, where entries without subordinates (users, for example) are leaves, and the root is the overarching entity that encompasses all the information within the directory.

LDAP Authentication and Authorization
The LDAP protocol both authenticates and authorizes users to their resources. The protocol authenticates users with a bind operation that allows the user to communicate with an LDAP directory, then authorizes the authenticated user to the resources they need if their input login information matches what’s listed for them in the database.

1.What Is LDAP Authentication ?
LDAP authentication relies on a client/server bind operation, which allows the LDAP-ready client, referred to as the directory user agent (DUA), and the directory server, referred to as the directory system agent (DSA), to communicate within a secure, encrypted session.

When authenticating against an LDAP server in an attempt to gain access to the database, the user is prompted to provide their username and password.

If the values the user inputs into the client matches what is found in the LDAP database, the user is granted access by the LDAP server to whatever the IT resource may be.

2. What Is LDAP Authorization ?
Once a user is successfully authenticated, they need to be authorized to the resource(s) requested. While different LDAP instances may structure and encode this slightly differently, this is essentially accomplished by assigning permissions with groups and roles in the directory.

#LDAP #ActiveDirectory #DirectoryServices #LDAPTutorial #LDAPvsActiveDirectory #DirectoryAccess #LDAPAuthentication #NetworkSecurity #ITInfrastructure #IdentityManagement

Other Query:
What is LDAP (Lightweight Directory Access Protocol)?
What is LDAP,
LDAP explained,
LDAP tutorial,
Lightweight Directory Access Protocol,
LDAP basics for beginners,
LDAP directory service,
How LDAP works,
LDAP vs Active Directory,
LDAP authentication,
LDAP protocol overview,
What is LDAP, LDAP explained, LDAP tutorial, Lightweight Directory Access Protocol, LDAP basics for beginners, LDAP directory service, How LDAP works, LDAP vs Active Directory, LDAP authentication, LDAP protocol overview, LDAP and Active Directory explained, What is LDAP and AD, LDAP structure and components, Active Directory overview, LDAP authentication in AD, LDAP schema and hierarchy, LDAP and Active Directory integration, Directory services LDAP vs AD

AWS Tutorial 3 - How to Install XAMPP Server on EC2 Windows Server at AWS EC2 Instance 121gamers 2 مناظر • پہلے 8 مہینے

Hi guys, let's see how to install the XAMPP server on a Windows EC2 machine. Launch Windows Ec2 instance on EC2 Instance. You will also learn how to host a website on the Ec2 Windows machine.

How to Launch Windows Machine on EC2
https://youtu.be/l2q7pSz8l1E

🔊 Watch till last for a detailed description

💯 Read Full Blog with Code
https://kgptalkie.com
💬 Leave your comments and doubts in the comment section
📌 Save this channel and video for watch later
👍 Like this video to show your support and love ❤️

~~~~~~~~
🆓 Watch My Top Free Data Science Videos
👉🏻 Python for Data Scientist
https://bit.ly/3dETtFb
👉🏻 Machine Learning for Beginners
https://bit.ly/2WOVh7N
👉🏻 Feature Selection in Machine Learning
https://bit.ly/2YW6ZQH
👉🏻 Text Preprocessing and Mining for NLP
https://bit.ly/31sYMUN
👉🏻 Natural Language Processing (NLP)
Tutorials https://bit.ly/3dF1cTL
👉🏻 Deep Learning with TensorFlow 2.0
and Keras https://bit.ly/3dFl09G
👉🏻 COVID 19 Data Analysis and Visualization
Masterclass https://bit.ly/31vNC1U
👉🏻 Machine Learning Model Deployment Using
Flask at AWS https://bit.ly/3b1svaD
👉🏻 Make Your Own Automated Email Marketing
Software in Python https://bit.ly/2QqLaDy

***********
🤝 BE MY FRIEND
🌍 Check Out ML Blogs: https://kgptalkie.com
🐦Add me on Twitter: https://twitter.com/laxmimerit
📄 Follow me on GitHub: https://github.com/laxmimerit
📕 Add me on Facebook: https://facebook.com/kgptalkie
💼 Add me on LinkedIn: https://linkedin.com/in/laxmimerit
👉🏻 Complete Udemy Courses: https://bit.ly/32taBK2
⚡ Check out my Recent Videos: https://bit.ly/3ldnbWm
🔔 Subscribe me for Free Videos: https://bit.ly/34wN6T6
🤑 Get in touch for Promotion: info@kgptalkie.com

✍️🏆🏅🎁🎊🎉✌️👌⭐⭐⭐⭐⭐
ENROLL in My Highest Rated Udemy Courses
to 🔑 Unlock Data Science Interviews 🔎 and Tests

📚 📗 NLP: Natural Language Processing ML Model Deployment at AWS
Build & Deploy ML NLP Models with Real-world use Cases.
Multi-Label & Multi-Class Text Classification using BERT.
Course Link: https://bit.ly/bert_nlp

📊 📈 Data Visualization in Python Masterclass: Beginners to Pro
Visualization in matplotlib, Seaborn, Plotly & Cufflinks,
EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data.
Course Link: https://bit.ly/udemy95off_kgptalkie

📘 📙 Natural Language Processing (NLP) in Python for Beginners
NLP: Complete Text Processing with Spacy, NLTK, Scikit-Learn,
Deep Learning, word2vec, GloVe, BERT, RoBERTa, DistilBERT
Course Link: https://bit.ly/intro_nlp

📈 📘 2021 Python for Linear Regression in Machine Learning
Linear & Non-Linear Regression, Lasso & Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection & Outliers Removal. You will learn how to build a Linear Regression model from scratch.
Course Link: https://bit.ly/regression-python

📙📊 2021 R 4.0 Programming for Data Science || Beginners to Pro
Learn Latest R 4.x Programming. You Will Learn List, DataFrame, Vectors, Matrix, DateTime, DataFrames in R, GGPlot2, Tidyverse, Machine Learning, Deep Learning, NLP, and much more.
Course Link: http://bit.ly/r4-ml