![Bhavesh Bhatt](/img/default-banner.jpg)
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Bhavesh Bhatt
India
Registrace 27. 12. 2011
Level up your Gen-AI skills: Tips, tricks, and projects from Bhavesh Bhatt
My name is Bhavesh Bhatt, & I'm the creator of this channel. I've taught data science using Python to hundreds of students in the classroom, & hundreds of thousands of students (like you) online. My focus is on creating tutorials on machine learning concepts in the simplest manner.
I've already released a wide variety of videos on NLP, LLM's, Generative AI, Data Science, ML algorithms, Python which will help you in your data science journey.
Please consider clicking the SUBSCRIBE button to be notified for new videos! I look forward to interacting with you in the comments :)
You can find my contact details on:
Blog - bhattbhavesh91.github.io
Disclaimers:
All opinions are of my own and not of my employers.
My name is Bhavesh Bhatt, & I'm the creator of this channel. I've taught data science using Python to hundreds of students in the classroom, & hundreds of thousands of students (like you) online. My focus is on creating tutorials on machine learning concepts in the simplest manner.
I've already released a wide variety of videos on NLP, LLM's, Generative AI, Data Science, ML algorithms, Python which will help you in your data science journey.
Please consider clicking the SUBSCRIBE button to be notified for new videos! I look forward to interacting with you in the comments :)
You can find my contact details on:
Blog - bhattbhavesh91.github.io
Disclaimers:
All opinions are of my own and not of my employers.
Llama 3.1 405B Review Classifier in 5 Minutes using GitHub Models
Learn how to build a powerful review classification script using Python, Meta's Llama 3.1 405B language model, and GitHub Models in just minutes! In this tutorial, we'll dive deep into the world of natural language processing (NLP) and show you how to harness the incredible capabilities of Llama 3.1 405B, a state-of-the-art large language model (LLM) developed by Meta AI. We'll leverage the user-friendly platform of GitHub Models to streamline the process and create a robust review classification model.
GitHub Models is a platform that brings the power of large and small language models (LLMs) directly to the GitHub community. It's designed to democratize access to AI, empowering developers to build more efficiently and creatively.
Key Features and Benefits of GitHub Models :
▶ Access to top-tier models: GitHub Models provides access to industry-leading LLMs like Llama 3.1, GPT-4o, and more.
▶ Easy integration: These models can be seamlessly integrated into your development workflow, enhancing productivity.
▶ Experimentation playground: A built-in playground allows you to test different models and understand their capabilities.
▶ AI-powered coding: Generate code, explain code, and even debug with the help of these models.
▶ Customizable solutions: Build custom AI tools and applications tailored to your specific needs.
How GitHub Models is Changing the Game
By making AI more accessible, GitHub Models is fostering a new generation of AI engineers. Developers can now leverage the power of AI to:
▶ Automate repetitive tasks: Free up time for more complex problem-solving.
▶ Improve code quality: Identify potential errors and suggest improvements.
▶ Accelerate development: Generate code snippets and complete tasks faster.
▶ Explore new possibilities: Experiment with different AI approaches and innovate.
Llama 3.1 is Meta AI's latest and most advanced large language model (LLM). It's a significant improvement over its predecessors, offering enhanced capabilities and flexibility.
Key Features of Llama 3.1:
▶ Unmatched Performance: Demonstrates state-of-the-art performance across various benchmarks.
▶ Increased Flexibility: Enables new workflows like synthetic data generation and model distillation.
▶ Open-Source Philosophy: Like its predecessors, Llama 3.1 is open-source, fostering community innovation and development.
▶ Multiple Sizes: Available in 8B, 70B, and 405B parameter variants to suit different computational resources and task requirements.
▶ Sponsor me on GitHub : github.com/sponsors/bhattbhavesh91/
▶ Join this channel to get access to perks: bit.ly/BhaveshBhattJoin
▶ Join the Telegram channel for regular updates: t.me/bhattbhavesh91
▶ If you like my work, you can buy me a coffee : bit.ly/BuyBhaveshCoffee
*I use affiliate links on the products that I recommend. These give me a small portion of the sales price at no cost to you. I appreciate the proceeds and they help me to improve my channel!
▶ Best Book for Python : amzn.to/3qYThqu
▶ Best Book for PyTorch & Machine Learning : amzn.to/3PyUkdy
▶ Best Book for Statistics : amzn.to/3vzvHEn
▶ Best Book for BERT: amzn.to/3lpX0fz
▶ Best Book for Machine Learning : amzn.to/2P6aZuT
▶ Best Book for Deep Learning : amzn.to/30UMTGl
▶ Best Intro Book for MLOps : amzn.to/3AoPZmM
Equipments I use for recording the videos:
▶ 1st Laptop I use : amzn.to/3AqI8Fp
▶ 2nd Laptop I use : amzn.to/3KAiYsB
▶ Microphone : amzn.to/3qUPxtz
▶ Camera : amzn.to/3rKQsM2
▶ Mobile Phone : amzn.to/3nRHP1f
▶ Ring Light : amzn.to/33LedM5
▶ RGB Light : amzn.to/3KzLgmS
▶ Bag I use : amzn.to/3AsM3RZ
If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.
If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful.
Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching.
You can find me on:
▶ Blog - bhattbhavesh91.github.io
▶ Twitter - _bhaveshbhatt
▶ GitHub - github.com/bhattbhavesh91
▶ Medium - medium.com/@bhattbhavesh91
▶ About.me - about.me/bhattbhavesh91
▶ Linktree - linktr.ee/bhattbhavesh91
▶ DEV Community - dev.to/bhattbhavesh91
▶ Telegram - t.me/bhattbhavesh91
#llama3 #githubmodels
GitHub Models is a platform that brings the power of large and small language models (LLMs) directly to the GitHub community. It's designed to democratize access to AI, empowering developers to build more efficiently and creatively.
Key Features and Benefits of GitHub Models :
▶ Access to top-tier models: GitHub Models provides access to industry-leading LLMs like Llama 3.1, GPT-4o, and more.
▶ Easy integration: These models can be seamlessly integrated into your development workflow, enhancing productivity.
▶ Experimentation playground: A built-in playground allows you to test different models and understand their capabilities.
▶ AI-powered coding: Generate code, explain code, and even debug with the help of these models.
▶ Customizable solutions: Build custom AI tools and applications tailored to your specific needs.
How GitHub Models is Changing the Game
By making AI more accessible, GitHub Models is fostering a new generation of AI engineers. Developers can now leverage the power of AI to:
▶ Automate repetitive tasks: Free up time for more complex problem-solving.
▶ Improve code quality: Identify potential errors and suggest improvements.
▶ Accelerate development: Generate code snippets and complete tasks faster.
▶ Explore new possibilities: Experiment with different AI approaches and innovate.
Llama 3.1 is Meta AI's latest and most advanced large language model (LLM). It's a significant improvement over its predecessors, offering enhanced capabilities and flexibility.
Key Features of Llama 3.1:
▶ Unmatched Performance: Demonstrates state-of-the-art performance across various benchmarks.
▶ Increased Flexibility: Enables new workflows like synthetic data generation and model distillation.
▶ Open-Source Philosophy: Like its predecessors, Llama 3.1 is open-source, fostering community innovation and development.
▶ Multiple Sizes: Available in 8B, 70B, and 405B parameter variants to suit different computational resources and task requirements.
▶ Sponsor me on GitHub : github.com/sponsors/bhattbhavesh91/
▶ Join this channel to get access to perks: bit.ly/BhaveshBhattJoin
▶ Join the Telegram channel for regular updates: t.me/bhattbhavesh91
▶ If you like my work, you can buy me a coffee : bit.ly/BuyBhaveshCoffee
*I use affiliate links on the products that I recommend. These give me a small portion of the sales price at no cost to you. I appreciate the proceeds and they help me to improve my channel!
▶ Best Book for Python : amzn.to/3qYThqu
▶ Best Book for PyTorch & Machine Learning : amzn.to/3PyUkdy
▶ Best Book for Statistics : amzn.to/3vzvHEn
▶ Best Book for BERT: amzn.to/3lpX0fz
▶ Best Book for Machine Learning : amzn.to/2P6aZuT
▶ Best Book for Deep Learning : amzn.to/30UMTGl
▶ Best Intro Book for MLOps : amzn.to/3AoPZmM
Equipments I use for recording the videos:
▶ 1st Laptop I use : amzn.to/3AqI8Fp
▶ 2nd Laptop I use : amzn.to/3KAiYsB
▶ Microphone : amzn.to/3qUPxtz
▶ Camera : amzn.to/3rKQsM2
▶ Mobile Phone : amzn.to/3nRHP1f
▶ Ring Light : amzn.to/33LedM5
▶ RGB Light : amzn.to/3KzLgmS
▶ Bag I use : amzn.to/3AsM3RZ
If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.
If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful.
Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching.
You can find me on:
▶ Blog - bhattbhavesh91.github.io
▶ Twitter - _bhaveshbhatt
▶ GitHub - github.com/bhattbhavesh91
▶ Medium - medium.com/@bhattbhavesh91
▶ About.me - about.me/bhattbhavesh91
▶ Linktree - linktr.ee/bhattbhavesh91
▶ DEV Community - dev.to/bhattbhavesh91
▶ Telegram - t.me/bhattbhavesh91
#llama3 #githubmodels
zhlédnutí: 444
Video
LLM Basics: Top-p vs. Top-K Sampling Explained for Beginners
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Unlock the power of large language models (LLMs) with the in-depth guide on Top-p vs. Top-K Sampling! Whether you're a beginner or looking to deepen your understanding of AI, this video breaks down these essential LLM sampling techniques in an easy-to-follow manner. ▶ What are Top-p (nucleus) sampling and Top-K sampling? ▶ How do these techniques impact the performance of LLMs like GPT-3 and GP...
Ultimate Guide to LLM Benchmarks: MMLU, HellaSwag, MBPP, GSM-8K, ARC Challenge & More!
zhlédnutí 495Před 2 měsíci
In this video, we dive deep into the most important LLM benchmarks, including: MMLU (Massive Multitask Language Understanding), HellaSwag (Harder Endings, Longer contexts, and Low-shot Activities for Situations With Adversarial Generations), ARC Challenge (AI2 Reasoning Challenge), Winogrande, MBPP (Massive Multi-Task Programming Problems), GSM-8K (Grade School Math 8K) & MT Bench (Multi-turn B...
Generative AI and the Future of Jobs: What You Need to Know
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Is AI coming for your job? Generative AI is changing the game, but will it leave you unemployed or unlock new opportunities? Watch this video to find out and discover how to THRIVE in the future of work! The rise of Generative AI is a hot topic, and for good reason! It's capable of automating tasks and generating creative content, but what does that mean for YOUR job? In this video, we'll break...
Don't Waste Your Time : The Dark Reality of Quick AI Courses
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Are you tempted by those flashy ads promising to turn you into an AI expert in quick time? Think twice before you click. In this eye-opening video, I'm pulling back the curtain on the shady world of rapid AI learning courses. Learning Artificial Intelligence (AI) is a rewarding journey, but unrealistic timelines can lead to frustration. In this video, I'll delve into the hidden dangers of quick...
Build a RAG application in Python with Gradient in minutes | Retrieval Augmented Generation
zhlédnutí 826Před 3 měsíci
This video will show you how easy it is to build a RAG app with just GradientAI using Python and nothing else. You can be up and running in less than 5 or so minutes. GradientAI just released an API for RAG that enables you to setup RAG instantly, without having to worry about the complexities. Experience a fully managed RAG service that's been optimized for performance and accuracy - incorpora...
Accurately Extract Text from PDFs with Gradient's PDF Extraction API | Build a RAG app in Python
zhlédnutí 999Před 4 měsíci
This video discusses Gradient's new API for extracting data from PDFs. This API can address the challenges of using PDFs, such as extracting text, identifying formatting, and recognizing the structure of tables. ▶ Link to the code : github.com/bhattbhavesh91/gradient-rag/blob/main/gradient-pdf-api-example.ipynb ▶ Sign up to Gradient's Awesome LLM API's : auth.gradient.ai/register ▶ Link to the ...
Effortless NLP with Open-Source LLMs : Sentiment Analysis, Entity Extraction & Summarization
zhlédnutí 549Před 4 měsíci
Ready to revolutionize your LLM development process? In this video, I'll unveil how you can effortlessly create a Sentiment Analysis, Entity Extraction & Text Summarization Solution in mere minutes using the game-changing Gradient Accelerator Blocks! Say goodbye to the challenges and resource-intensive nature of LLM Solution development! With Gradient Accelerator Blocks, you'll dive into AI dev...
Chat with Time-Series Data in PostgreSQL using LlamaIndex and Timescale Vector
zhlédnutí 1,2KPřed 4 měsíci
Ever wished you could just talk to your timeseries data? Now you can! This video unlocks the power of conversational analytics for your time-series data in PostgreSQL using the magic of LlamaIndex and Timescale Vector. ▶ Try Timescale Vector free for 90 days : tsdb.co/b-sign-up ▶ Learn more about Timescale : tsdb.co/ai-homepage ▶ Link to the notebook : github.com/bhattbhavesh91/timeseries-rag/b...
Vector Databases Explained in 10 Minutes & Get Started with Qdrant Vector Database with Python
zhlédnutí 1,5KPřed 5 měsíci
In this video, we're diving deep into the fascinating world of vector databases and introducing you to Qdrant, a cutting-edge solution for efficient data management. Whether you're a seasoned data scientist or a Python enthusiast, this tutorial will equip you with the knowledge and skills to harness the power of vector databases using Python. Ready to embark on your data-driven adventure? Let's...
Natural Language to SQL with Google Gemma : A Comprehensive Guide
zhlédnutí 5KPřed 5 měsíci
In this video, I show you how to fine-tune Google Gemma for your converting natural language question to SQL queries. Google Gemma is a family of open-source, large language models (LLMs) that are designed to be accessible and lightweight.This allows your Google Gemma model to perform much better for your business or personal use case. Give Google Gemma detailed information that it doesn't alre...
Testing the Power of Google's Gemini PRO 1.5 : A Game-Changing 1 Million Token AI Multimodal Model!
zhlédnutí 3,1KPřed 6 měsíci
Get ready for the ultimate AI breakthrough as Google drops Gemini Pro 1.5, outshining GPT-4 Turbo with a staggering 1 million tokens, setting the new standard in AI supremacy! With lightning-fast training capabilities, Gemini 1.5 Pro handles an hour of video, 11 hours of audio, or over 700,000 words in a document, leaving competitors like OpenAI and Anthropic in the dust! ▶ Link to the Gemini P...
Ride the Generative AI Wave: Overcome Imposter Syndrome & FOMO in Data Science, Machine Learning!
zhlédnutí 421Před 6 měsíci
Are you a data scientist or a machine learning engineer struggling to stay afloat in the ever-expanding ocean of Large Language Models (LLM) and General AI (GenAI)? You're not alone! The rapid pace of innovation in the field of artificial intelligence can be both exhilarating and overwhelming, leading many to experience a serious case of FOMO - the Fear of Missing Out or Imposter Syndrome. But ...
Join the Celebration: Silver Play Button Unboxing | Bhavesh Bhatt
zhlédnutí 154Před 6 měsíci
Six years ago, I embarked on a journey that started with a single click - the upload of my very first video. Little did I know that this path would be paved with countless hours of dedication and a passion that burned brighter with every new subscriber. Today, I'm beyond thrilled to share with you a milestone that marks both an end and a beginning: the unboxing of my CZcams Silver Play Button. ...
How I'd Learn ML in 2024 (If I Had to Start Over)
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In this video, I am going to show you my entire roadmap that I would follow to learn Machine Learning (ML), Artificial Intelligence (AI) or Data Science if I had to start over today. Over the last 9 years, my professional journey has revolved around natural language processing, data science, machine learning, and deep learning. This experience has culminated in various achievements, including t...
How I'd Learn NLP in 2024 (If I Had to Start Over)
zhlédnutí 3,8KPřed 7 měsíci
How I'd Learn NLP in 2024 (If I Had to Start Over)
Natural Language to SQL using Google's Gemini Pro Model | SQL Database | Python
zhlédnutí 27KPřed 7 měsíci
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I've Built a Diabetes Management App With Google's New AI Gemini
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Ask Questions Directly to Invoices using Google's Gemini Pro Vision | Python | Google AI Studio
zhlédnutí 156KPřed 8 měsíci
Ask Questions Directly to Invoices using Google's Gemini Pro Vision | Python | Google AI Studio
Build a PDF Document Question Answering System with Llama2, LlamaIndex
zhlédnutí 171KPřed 9 měsíci
Build a PDF Document Question Answering System with Llama2, LlamaIndex
Supercharge Your Pandas Code with RAPIDS cuDF & pandas accelerator mode | RAPIDS cuDF 23.10
zhlédnutí 28KPřed 9 měsíci
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How to Chat with YouTube Videos Using LlamaIndex, Llama2, OpenAI's Whisper & Python
zhlédnutí 88KPřed 9 měsíci
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Getting Started with Google Cloud Speech-To-Text API Using Python for Novices
zhlédnutí 2,1KPřed 10 měsíci
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OpenAI's Whisper Model Showdown: GPU vs. OpenAI API - Which Transcribes Audio to Text Faster?
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Create a Smart Voice Assistant using Google's PaLM 2, MakerSuite, Neo4j , Whisper, Python & Gradio
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zhlédnutí 3,5KPřed 11 měsíci
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GitHub Actions Certification Exam: My Experience & Tips for Success
How to Speed Up Your Python Development with GitHub Codespaces and GitHub Copilot
zhlédnutí 694Před rokem
How to Speed Up Your Python Development with GitHub Codespaces and GitHub Copilot
Meta Unleashes Llama 2 : Empowering Commercial Applications with Open Source AI
zhlédnutí 998Před rokem
Meta Unleashes Llama 2 : Empowering Commercial Applications with Open Source AI
Bhavesh, I currently use a laptop which has an integrated graphic card by intel(basic one),and the time of doing a project using tensorflow n keras libraries, it has shown the potential and literally it didn't even fetch me an output in 3 hours. Now because I ll be working on machine learning/deep learning projects, I need to buy a laptop, would you suggest me one laptop which satiate my need, should I buy just another win + rtx GPU laptop or m2?
thanks!
Yo,I’m in 8th right now, not gonna lie the school is in a pretty bad state LIKE the school hurried the first term for some reason like only for 3 months this year starting from June ,and the first exam for second term is IN DECEMBER
Do you have full access to this software?
Including Hokkien dialects?
This was extremely useful content , the way you defined and explained gamma and c value in the 4 sentences , cleared my concepts real quick. Thankyou from your new subscriber :)
Glad it was helpful!
Can you tell i should do scaling before or after the smote?
Good information. Generate millions of memes using SmythOS AI Agent. It generates accurate memes
I have joined the waitlist.
Does any one have any idea- How to downgrade tensorflow in colab. because it is only supoorting 2nd version.
Please make a video on adding custom vocabularies for transcriptions. Generally, while transcripting, it only transcripts english, but when we have the vedic verses also being spoken, then it cannot correctly identify it. So, For Spiritual Videos, Can we make such AI which will do transcriptions with english, and vedic verses with reference. I can personally set up all the verses into a file for it. So, is this possible? Please make a video for it.
Bro is a life saver 🤩
sir pls provide coe
nice video , thanks preparing such video
Great video. Just a quick correction, in @1:11 you have written that "Stemming: merging words that are equivalent in meaning". That actually is called Lemmatization. Stemming actually reduces the words to their radicals, since the topic analysis can be done with just them, reducing the size of the analyzed data.
While downloading its saying-> [NVIDIA installer cannot continue No NVIDIA is detected in your computer This graphics driver could not find compatible graphics hardware.] How to fix it
which SLM you would recommend for handling summaries in both English and French .thank you
that is called an explanation. great work.
Glad it helped
important stuff. keep working on it friend
can u add keepdims also and explain?
Would u recommend macbook air(8gb) or windows laptop for machine learning?
what if in the end it show false as out put
Hello , I am getting an error FileNotFoundError: [Errno 2] No such file or directory: '/content/tmp0soh8cpf/config.json' cloud_config= {'secure_connect_bundle': 'secure-connect-bhavesh-astra-test.zip'} it is not able to identify the zip file
Please create video on topic Modeling By using LLM
Great Explanation....👏
Hello, Professor, I need your account on Telegram, not a channel. I hope you can help. I have a question about Mars
After all the installation and all process , I got "False " in the GPU check part. Can you help me with it ?
After all the installation and all , I got False in the last part of checking GPU Connectivity. Can you help me with it ?
Very concisely explained. Thanks !
thanks a lot. THIS way really helps me
Hello, I need deepxde. How can I download it
Really help
Hey, for my m1 mac its using the CPU and not the gpu. Its not even detecting GPU. what should I do?
i want to print only output of text.
Hi, Nice video. Can you make a video for running gemma on local machine using pytorch? Thank you
can you please tell how to specify a llm model here. For example, I want to use llama-3 for RAG. How can I do that ?
I changed a bit of code from 1.0 to 2.0 and my output completely changed its not upto the mark how can I tune it to get results like you
loved the video ..easy explanation
Thanks!
please make a video about axis=3
Gracias por compartir tu experiencia! Es muy útil
Wow this is magic
Could somebody help me understand why does the confusion matrix not have the same number of observations as y_test has? While y_test has 254 observations, the first confusion matrix makes predictions only for 39 + 23 + 15 + 8 observations.
Thanks Bhavesh. Where does Temperature fit in?
Wait for the next video!
Tamil jai