Setting up a local AI model can be intimidating, which is why I created this video. In this video I will show you everything you need to know about setting up local AI models from the basics of how local AI models work, to how to optimize local AI models for your hardware, and even how to use these local AI models in real world agentic environments.


📚 Materials/References:

LM Studio: https://lmstudio.ai/
Hugging Face: https://huggingface.co/
Pi Coding Agent: https://pi.dev/


🌎 Find Me Here:

My Blog: https://blog.webdevsimplified.com/
My Courses: https://courses.webdevsimplified.com/
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Twitter: https://twitter.com/DevSimplified
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GitHub: https://github.com/WebDevSimplified
CodePen: https://codepen.io/WebDevSimplified


⏱️ Timestamps:

00:00 - Introduction
01:08 - How Local AI Works
05:33 - Picking Models
11:46 - Configuring Your Model
21:57 - Using Local Models In Your IDE
34:17 - Using Local Models With Copilot
38:17 - Using Local Models With Pi
40:55 - Comparing Local Models to Anthropic Models


#LocalAI #WDS #AgenticCoding

Checkout the roadmap: https://www.krishnaik.in/ai-roadmaps
Agentic AI involves creating autonomous systems that use Large Language Models (LLMs) to plan, reason, and act to achieve goals, rather than just generating content. To learn this, focus on mastering frameworks like LangGraph, CrewAI, and AutoGen to build multi-agent workflows, and focus on agentic RAG for data access. Key skills include planning, tool usage, and human-in-the-loop interaction.
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Learn from me and my team
visit : https://krishnaik.in/liveclasses

High Quality HDR 12K VIDEO ULTRA HD 120FPS, 60FPS, 30FPS For Your HDR 8K resolution devices. Amazing combination of 16k sensor and one of the sharpest lenses in the world Zeiss Otus set in addition of HDR brings image to life! You can use this collection of Hight Resolution clips in your Tv For The Living Room, Office, Lounge, Waiting Room, Spa, Showroom, Restaurant and more. Play It On Your LG Qled TV, Samsung Oled TV, Smart TV, Sony Device, Samsung Technology, Roku, Apple TV, IPad Pro, Apple XDR, Chromecast, Xbox, Playstation and more.

🔥Note: To view at 8K 60P you will need to use Chrome & opera.

🔥 All Videos was shot, edited & color graded by me.
THESE 8K VIDEOS ARE FOR TV'S FOR DEMO AND TEST THE QUALITY OF ALL 8K TV'S.


🔥 I have done High color correction, Color changing, Raw Videos editing, HDR color Setting, merge files & 8K Export file etc.

Editing Video by
PREETI NAAGAR

This video created for entertainment informative, educational purposes and Film & Animation.

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ALL CREDIT GOES TO YOUTUBE

Copyright
🎦 All The footage Was Edited And Color Corrected By Me.
🎦 Video Footage Copyright Under License.

All other rights reserved.

( Sulphur-crested cockatoo, Domestic Muscovy duck, Old World sparrows, Pembroke Welsh Corgi, Capybara Rodents, Oriental Shorthair cat, European mantis, African penguin, Chihuahua Dog, Sun conure bird, Toulouse goose, Bearded vulture, British Shorthair, Marabou stork, Common frog, Mandarin duck, Spotted Dove, Fainting goat, Aegean cat )




#12khdr #DolbyVision #60fps

Welcome to a tutorial series covering how to do reinforcement learning with the Stable Baselines 3 (SB3) package. The objective of the SB3 library is to be for reinforcement learning like what sklearn is for general machine learning.

Text-based tutorial and sample code: https://pythonprogramming.net/....introduction-reinfor

Neural Networks from Scratch book: https://nnfs.io
Channel membership: https://www.youtube.com/channe....l/UCfzlCWGWYyIQ0aLC5
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Gemini Man (2019) - Epic Sniper Scene 4K
Gemini Man - Epic Sniper: Henry (Will Smith) pulls off a one-in-a-million shot.

CREDITS:
TM & © Paramount (2019)
Cast: Will Smith
Director: Ang Lee
Screenwriter: Billy Ray, Darren Lemke, David Benioff

Akshaya and Masha, two fraudsters, learn about the existence of a magical vessel from an old lady. Soon, they set out to acquire it for themselves and face various challenges along the way.

Movie:- Jackpot
Starcast:- Jyothika, Revathi, Yogi Babu, Anandaraj
Creative Producer:- Manish Shah
Directed by:- Kalyaan
Music by:- Vishal Chandrasekhar

#Jackpot # Jyothika #Revathi #YogiBabu #Goldmines

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With so many alternatives available, why are neural nets used for Deep Learning? Neural nets excel at complex pattern recognition and they can be trained quickly with GPUs.

Deep Learning TV on
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Historically, computers have only been useful for tasks that we can explain with a detailed list of instructions. As such, they tend to fail in applications where the task at hand is fuzzy, such as recognizing patterns. Neural Networks fill this gap in our computational abilities by advancing machine perception – that is, they allow computers to start to making complex judgements about environmental inputs. Most of the recent hype in the field of AI has been due to progress in the application of deep neural networks.

Neural nets tend to be too computationally expensive for data with simple patterns; in such cases you should use a model like Logistic Regression or an SVM. As the pattern complexity increases, neural nets start to outperform other machine learning methods. At the highest levels of pattern complexity – high-resolution images for example – neural nets with a small number of layers will require a number of nodes that grows exponentially with the number of unique patterns. Even then, the net would likely take excessive time to train, or simply would fail to produce accurate results.

Have you ever had this problem in your own machine learning projects? Please comment.

As a result, deep nets are essentially the only practical choice for highly complex patterns such as the human face. The reason is that different parts of the net can detect simpler patterns and then combine them together to detect a more complex pattern. For example, a convolutional net can detect simple features like edges, which can be combined to form facial features like the nose and eyes, which are then combined to form a face (Credit: Andrew Ng). Deep nets can do this accurately – in fact, a deep net from Google beat a human for the first time at pattern recognition.

However, the strength of deep nets is coupled with an important cost – computational power. The resources required to effectively train a deep net were prohibitive in the early years of neural networks. However, thanks to advances in high-performance GPUs of the last decade, this is no longer an issue. Complex nets that once would have taken months to train, now only take days.

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