A video about decision trees, and how to train them on a simple example.

Accompanying blog post: https://medium.com/@luis.serra....no/splitting-data-by

For a code implementation, check out this repo:
https://github.com/luisguiserr....ano/manning/tree/mas

Helper videos:
- Gini index: https://www.youtube.com/watch?v=u4IxOk2ijSs
- Entropy and information gain: https://www.youtube.com/watch?v=9r7FIXEAGvs
- Machine learning error and metrics: https://www.youtube.com/watch?v=aDW44NPhNw0

Grokking Machine Learning book:
www.manning.com/books/grokking-machine-learning
40% discount code: serranoyt

Agents are everywhere these days, but with so much information available, it’s easy to feel overwhelmed. Most tutorials and videos focus on specific frameworks without covering the fundamental principles behind these systems.

This course takes a different approach. Over four modules, you'll learn to implement the four core agentic patterns from scratch, using just Python and Groq LLMs:

* Reflection Pattern
* Tool Use Pattern
* Planning Pattern
* Multi-Agent Pattern

Explore the written lessons on my Substack blog:

https://theneuralmaze.substack.com/

Check out the code here:

https://github.com/neural-maze/agentic_patterns

––––––––––––––––––––––––––––––

If you like this content, you can also follow me here:

📩 Substack - https://theneuralmaze.substack.com/
💼 LinkedIn - https://www.linkedin.com/in/migueloteropedrido/
💻 GitHub - https://github.com/MichaelisTrofficus
🐦 Twitter - https://x.com/moteropedrido

––––––––––––––––––––––––––––––

––––––––––––––––––––––––––––––

0:00 Introduction
2:48 Module 1 - Reflection Pattern
20:40 Module 2 - Tool Pattern
44:22 Module 3 - Planning Pattern
1:13:16 Module 4 - MultiAgent Pattern
1:41:00 Conclusion

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For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

November 21, 2025
This lecture covers:
• LLM-as-a-judge overview
• Best practices and benefits
• Biases and pitfalls

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:07:08 Inter-rater agreement metrics
00:18:24 Rule-based metrics
00:21:00 METEOR, BLEU ROUGE
00:28:00 LLM-as-a-judge
00:33:44 Structured outputs
00:36:48 Variants
00:38:47 Position, verbosity, self-enhancement bias
00:47:22 Best practices
00:54:06 Factuality
01:00:15 Agent evaluation
01:23:50 Benchmarks
01:25:12 Knowledge with MMLU
01:29:34 Reasoning AIME, PIQA
01:33:57 Coding with SWE-bench
01:36:15 Safety with HarmBench
01:40:51 Agents with Tau-Bench

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

📌Generative AI Course: Masters Program : https://www.edureka.co/masters....-program/generative-
🔥Integrated MS+PGP Program in Data Science & AI:https://www.edureka.co/dual-ce....rtification-programs

Learn the fundamentals of generative artificial intelligence in this beginner-friendly course (2025 edition). We’ll cover what generative AI is, how it differs from traditional AI, key architectures (GANs, VAEs, Transformers), hands-on examples, prompt engineering, and real-world applications you can build today. No prior AI or deep-learning experience required.
✅ What you’ll learn:
What is generative AI & why it matters in 2026
Core models: GAN (Generative Adversarial Network), VAE (Variational Autoencoder)
Transformer architecture and large language models (LLMs)
Text-, image-, and audio-generation use cases
Basics of prompt-engineering and building your own simple GenAI app
Ethical, bias, and safety considerations for generative systems

00:00:00 Introduction
00:01:59 What is Generative AI?
00:15:27 Generative AI Examples
00:34:06 Generative AI Tools
00:55:31 Generative AI in Marketing
01:05:20 Exploring the Ethics of Generative AI
01:12:49 What is Deep Learning
01:24:03 Generate Images Using DC-GAN
01:48:36 Midjourney
02:06:58 What are Large Language Models?
02:26:42 LLM vs SLM
02:31:42 What Is Multimodal AI?
02:48:07 What is LangChain?
03:05:22 What is RAG?
03:28:14 LLMOps: The Future of AI Development
03:34:13 Prompt Engineering For Code Generation
03:43:32 Building a Chatbot with Prompt Engineering
03:59:03 OpenAI API using Python
04:07:18 Agentic AI vs Generative AI
04:25:18 DeepSeek vs OpenAI: Who Wins the AI Race?
04:37:51 Alibaba’s Qwen 2.5-Max Just Beat GPT-4 & DeepSeek?
04:42:14 Nvidia's Latest Breakthrough in Generative AI
04:48:52 The Future of Generative AI and Job Opportunities
04:54:36 GenAI Roadmap
05:03:21 Generative AI Interview Questions


📌 By the end of this course you’ll be equipped to explore your own generative-AI experiments and skill up for deeper tools.

🔔 If you enjoyed this video, Subscribe & hit the bell icon so you won’t miss further AI-tutorials in 2026. Leave a comment if you have questions or want to see a project walkthrough.

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py

📝Feel free to share your comments below.📝

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🔵 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT
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Please write back to us at [email protected] or call us at IND: 9606058406 / US: +18885487823 (toll-free) for more information.

Baraka is a 1992 American non-narrative documentary film directed by Ron Fricke and produced by Mark Magidson.

The word Baraka is defined within various Eastern religions as a powerful spiritual blessing that flows throughout many different aspects of life.

The Baraka project was filmed by a only 5 person crew, over a period of 14 months, in 24 different countries, and while spanning 6 of the 7 continents.

This is a short documentary released in 2008 about the 8K scanning and restoration process undertaken with the original film stock

Baraka was shot on 70mm film and then scanned at an oversampled 8K Ultra High Resolution (8,192 pixels across the frame) with DTS-HD Master Audio 5.1 @96k/24bits. It was then output onto Blu-ray in Full High Definition 1080p at a 16:9 Widescreen aspect ratio.

The scan done at 8K was oversampled, which means the film was scanned at a higher resolution than what was necessary for the final 1080p output that they intended and produced.

The purpose of scanning at 8K though was to create a super sharp, but down-sampled version of the film scan once it was output at Full HD 1080p. Thus, the resulting image from the 8K scan is a sharper quality 1080p output than if they had just scanned the film at 1080p or at a 4K scan resolution. The 1080p output from the 8K scan is also now perhaps an even better quality version than the original film print when viewed digitally on either a 1080p or 4K display.

The unanswered question though is if an 8K scan from the 65mm native film stock would produce an even more desirable looking image at 4K than at the 1080p resolution output which they had decided upon for this production.

But without there being any 4K sample outputs available (AFAIK) from the original film print to 8K scan (in order to rule out whether or not a 4K output might introduce any undesirable flaws, artifacts, or reveal any weaknesses from the physical film print at such a high resolution scan) it remains a question that still can’t be fully addressed.

In this video they also briefly mention DVD, which may be a bit confusing, but I can confirm that the 8K scan was never output at 480p resolution and was only ever released at 1080p resolution on Blu-ray.

One can still purchase the full Baraka 1080p film on Blu-ray from the 8K scan, which also includes this short documentary titled "Restoration" as a special feature on the disc. The Blu-ray disc also includes another 1+ hour documentary titled "A Closer Look” which is a BTS documentary on the shooting and production of Baraka itself.

Thus, the full 1080p version of the Baraka film, the "A Closer Look" documentary, and this short "Restoration" documentary are all included together on a single Blu-ray disc which is available from the link here: https://amzn.to/2Ii5na4

A final note in that I do not possess any copyrights to the content of this video nor is it my intention to infringe upon any existing copyrights of this content in any way. I also have no personal connection or otherwise to the producers of this video or to the creators of the Baraka film.

I sit down with my dear friend Vin (Internet Vin) for a deep, hands-on walkthrough of how he uses Obsidian and Claude Code together as a thinking partner, idea generator, and personal operating system. Vin demonstrates live how Claude Code can read, reference, and surface patterns across an entire Obsidian vault of interlinked markdown files — turning years of personal notes into actionable insights, project ideas, and even custom commands. This episode covers everything from the basic setup to advanced workflows like tracing how ideas evolve over time, generating contextual startup ideas, and delegating tasks to autonomous agents. If you are serious about getting the most out of LLMs, this is the episode that shows you how your own writing becomes the fuel.

Timestamps
00:00 – Intro
02:10 – What Is Claude Code?
06:45 – What Is Obsidian?
10:28 – Obsidian CLI: Giving Claude Code Access to Your Vault
14:53 – Thinking Tools: Ghost, Challenge, Emerge, Drift, Ideas, Trace
22:51 – The Role of Reflection in Building a Powerful Vault
25:15 – How This Relates to OpenClaw (Autonomous Agents)
29:13 – Live Demo: /Connect — Bridging Two Domains
31:25 – Meeting Notes & External Info
33:23 – Why Vin Keeps a Strict Separation: Human-Written vs. Agent-Written
35:42 – How Claude Code uses Obsidian
41:46 – Live Demo: /Ideas — Generating Actionable Ideas from Your Vault
47:10 – The /Graduate Command
50:29 – Why Obsidian Is the Missing Link for AI Companies
54:53 – The Alpha: Why 99.99% of People Won't Do This
57:38 – Closing Thoughts & Where to Follow Vin

Key Points

* Claude Code is a command-line agent that can control your computer through natural language — and its power multiplies when you feed it rich, persistent context files instead of re-explaining projects every session.
* Obsidian is uniquely valuable because it sits on top of interlinked markdown files; the new Obsidian CLI lets Claude Code see both the files and the relationships between them.
* Vin built custom slash commands (/trace, /connect, /ideas, /ghost, /drift, /challenge) that let him use Claude Code as a thinking partner — surfacing latent patterns, contradictions, and ideas he would never see on his own.
* Writing and daily reflection are the engine of the entire system: the more you write, the more context the agent has, and the more it can do for you.
* Markdown files are the real oxygen of LLMs; if you are serious about building a personal OS with AI, a centralized note-taking tool built on markdown is foundational

Numbered Section Summaries

1. Obsidian as an Interlinked Knowledge Base

Vin introduces Obsidian as an interface that sits on top of a folder of markdown files, with the critical addition of backlinks — connections between files that mirror how the brain forms associations. He walks through his own vault, showing how daily notes, project files, and notes on people all link together in a visual graph.

2. Obsidian CLI: The Bridge Between Your Vault and Claude Code

The real breakthrough comes from Obsidian CLI, which gives Claude Code access to both the files and their interrelationships. This means the agent can see that a note about filmmaking is connected to a note about world building, and can surface cross-domain patterns you have been circling for months without realizing it.

3. Custom Slash Commands as Thinking Tools

Vin demonstrates a suite of custom commands he built: /context loads his full life and work state; /today pulls calendar, tasks, and daily notes into a prioritized plan; /trace tracks how an idea has evolved over time; /connect bridges two domains using the vault's link graph; /ghost answers a question the way Vin would; /challenge pressure-tests his current beliefs. These turn Claude Code from a generic assistant into a deeply personalized thinking partner.

4. Markdown Files as the Foundation of the AI Era

I make the case that if you are serious about using LLMs to their full potential, a centralized markdown-based note-taking system is table stakes. Writing and reflection are the raw material; files are perfect memory where human recall is flawed; and the 99.99% of people who skip this step are leaving massive value on the table.

The #1 tool to find startup ideas/trends - https://www.ideabrowser.com/
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/

FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/

FIND VIN ON SOCIAL
X: https://x.com/internetvin
Youtube: https://www.youtube.com/@otherstuffpod
Personal Website: https://internetvin.com/Index

Presented at Code w/ Claude by @anthropic-ai on May 22, 2025 in San Francisco, CA, USA.

Speakers:
Cal Rueb, Member of Technical Staff at @anthropic-ai

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

October 17, 2025
This lecture covers:
• Pretraining
• Quantization
• Hardware optimization
• Supervised finetuning (SFT)
• Parameter-efficient finetuning (LoRA)

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:07:19 Pretraining
00:13:26 FLOPs, FLOPS
00:16:34 Scaling laws, Chinchilla law
00:24:49 Training optimizations overview
00:31:09 Data parallelism with ZeRO
00:35:51 Model parallelism
00:38:26 Flash Attention
00:52:37 Quantization
00:56:00 Mixed precision training
01:02:31 Supervised finetuning
01:09:21 Instruction tuning
01:37:53 Parameter-efficient finetuning with LoRA
01:45:16 QLoRA

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

Agentic AI is taking over conversations — but what does it really mean? In this video, we explore how Agentic AI goes beyond prompts and conversations to act, learn, and evolve — just like real-world autonomous systems. From perception to action, from reasoning to learning, we dive deep into the architecture of modern agents. You’ll learn how to build your own AI agents using tools like GPT-4, LangChain, and OpenAI's Agent SDK, and why the Model Context Protocol (MCP) is a critical layer for intelligent orchestration. Whether you're building with LLMs, deploying code autonomously, or scaling intelligent systems, this video has the insights to elevate your understanding.

My Linkedin Profile: https://www.linkedin.com/in/bytemonk/

📌 Timestamps
00:00 – Hook: Agentic AI – Hype or Reality?
00:35 – How Agentic AI Goes Beyond Autonomous AI
00:49 – Perceive, Reason, Act, Learn: The Agentic Loop
02:32 – How Modern Agents Actually Work (Architecture Breakdown)
04:14 – Real-World Example: Code Deployment Agent
05:38 – Tools to Build Agents: LangChain, Agent SDK & More
07:00 – What is MCP & Why It Matters in Agentic Workflows
07:55 – Final Thoughts: Future of AI is Agentic


https://www.youtube.com/playli....st?list=PLJq-63ZRPdB
https://www.youtube.com/playli....st?list=PLJq-63ZRPdB
https://www.youtube.com/playli....st?list=PLJq-63ZRPdB
https://www.youtube.com/playli....st?list=PLJq-63ZRPdB
https://www.youtube.com/playli....st?list=PLJq-63ZRPdB
https://www.youtube.com/playli....st?list=PLJq-63ZRPdB

AWS Certification:
AWS Certified Cloud Practioner: https://youtu.be/wF1pldkQrOY
AWS Certified Solution Architect Associate: https://youtu.be/GzomXNLFgkk
AWS Certified Solution Architect Professional: https://youtu.be/KFZrBxSA9tI

#AgenticAI #AIEngineering #LangChain #GPT4 #AutonomousAI #MCP #SoftwareEngineering

Xiaoqing Ge, Senior Innovation Engineer at Baker Hughes, discusses the following:
• Overview of computer vision
• How deep learning works in computer vision?
• Traditional methods vs deep learning
• Survey of Baker Hughes applications
• Looking ahead

🔥Edureka AWS Training: https://www.edureka.co/aws-certification-training
This Edureka video on "Deploy an ML Model using Amazon Sagemaker" discusses what is Amazon Sagemaker and how you can build, train and deploy your Machine Learning Models in Amazon Sagemaker. These are the topics covered in the AWS Machine Learning Tutorial video:
00:00:00 Introduction
00:01:14 What is Amazon Sagemaker?
00:04:21 Create your AWS Account
00:06:46 Create your First Notebook Instance
00:17:39 Train your Model on AWS
00:24:37 Deploy your Model on AWS
00:26:33 Evaluate your Model on AWS
00:29:03 AWS SageMaker Case Study: Grammarly

🔹Check Edureka's complete DevOps playlist here: http://goo.gl/O2vo13
🔹Check Edureka's Blog playlist here: https://bit.ly/3gfNuZr

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--------------------------------------------------------------------------------------------
How it Works?
1. This is a 5 Week Instructor led Online Course.
2. Course consists of 30 hours of online classes, 30 hours of assignment, 20 hours of project
3. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
4. You will get Lifetime Access to the recordings in the LMS.
5. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!

- - - - - - - - - - - - - -
About the Course

AWS Architect Certification Training from Edureka is designed to provide in depth knowledge about Amazon AWS architectural principles and its components. The sessions will be conducted by Industry practitioners who will train you to leverage AWS services to make the AWS cloud infrastructure scalable, reliable, and highly available. This course is completely aligned to AWS Architect Certification - Associate Level exam conducted by Amazon Web Services.

During this AWS Architect Online training, you'll learn:
1. AWS Architecture and different models of Cloud Computing
2. Compute Services: Amazon EC2, Auto Scaling and Load Balancing, AWS Lambda, Elastic Beanstalk
3. Amazon Storage Services : EBS, S3 AWS, Glacier, CloudFront, Snowball, Storage Gateway
4. Database Services: RDS, DynamoDB, ElastiCache, RedShift
5. Security and Identity Services: IAM, KMS
6. Networking Services: Amazon VPC, Route 53, Direct Connect
7. Management Tools: CloudTrail, CloudWatch, CloudFormation, OpsWorks, Trusty Advisor
8. Application Services: SES, SNS, SQS

Course Objectives

On completion of the AWS Architect Certification training, learner will be able to:
1. Design and deploy scalable, highly available, and fault tolerant systems on AWS
2. Understand lift and shift of an existing on-premises application to AWS
3. Ingress and egress of data to and from AWS
4. Identifying appropriate use of AWS architectural best practices
5. Estimating AWS costs and identifying cost control mechanisms

Pre-requisites

There are no specific prerequisites for this course. Any professional who has an understanding of IT Service Management can join this training. There is no programming knowledge needed and no prior AWS experience required.

If you are looking for live online training, write back to us at [email protected] or call us at US: + 18338555775 (Toll-Free) or India: +91 9606058406 for more information.