A quick look at CBOW and how it looks like it in code. I explained how this model works in the previous example so check that out before you look at this one to get a better understanding.

Timestamps:
0:00 - Introduction
0:48 - Imports
0:53 - CBOW model
5:10 - Example of how to train it
8:52 - Ending

The blue LED was supposed to be impossible—until a young engineer proposed a moonshot idea. Head to https://brilliant.org/veritasium to start your free 30-day trial, and the first 200 people get 20% off an annual premium subscription.

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Huge thanks to the UC Santa Barbara Materials Dept (https://ssleec.ucsb.edu/) for taking us around.
Thanks to Álvaro Bermejillo Seco for reviewing the science.
Thanks to these especially helpful sources:
Nobel Prize Biography - Shuji Nakamura - https://ve42.co/NakamuraNobel
Johnstone, B. (2015). Brilliant!. Prometheus Books. - https://ve42.co/Johnstone2015
Nakamura, S., Pearton, S., & Fasol, G. (2010). The Blue Laser Diode: The Complete Story. Springer. - https://ve42.co/Nakamura2010

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References:
https://www.youtube.com/watch?v=O8M2z2hIbag
https://www.youtube.com/watch?v=TGUteH93xNo
https://www.youtube.com/watch?v=idwKHQEw78g
https://www.youtube.com/watch?v=yoTALRhAqWc
Touchstone, L. A. (2022). Nick Holonyak Jr. University of Illinois. - https://ve42.co/Touchstone2022
Perry, T. S. (1995). The Unsung Inventor. IEEE Spectrum. - https://ve42.co/Perry1995
Chabay, R. & Sherwood, B. (2011). Matter & interactions (4th ed.), S2: Semiconductors. Wiley. - https://ve42.co/ChabaySherwood
How MOCVD Works via Aixtron - https://ve42.co/MOCVD
Vangala, S. R., et al. (2019). Epitaxial growth of ZnSe on GaAs. Journal of Crystal Growth. - https://ve42.co/Vangala2019
Nakamura, S. (1991). GaN Growth Using GaN Buffer Layer. JJAP. - https://ve42.co/Nakamura3rd1991
Amano, H., et al. (1989). P-Type Conduction in Mg-Doped GaN w/ LEEBI. JJAP. - https://ve42.co/Amano1989
Huang, M., et al. (2021). Defects in Mg–H‐Codoped GaN. Physica Status Solidi. - https://ve42.co/Huang2021
Schubert, E. F. (2006). Light Emitting Diodes, Ch 4: LED basics. Cambridge University Press. - https://ve42.co/RPI-LEDs
Kitada, C. (2001). Blue About Japan. Japan Inc. - https://ve42.co/Kitada2001
Whitaker, T. (2002). Nakamura loses Nichia patent battle. Optics.org. - https://ve42.co/NichiaSales3
Pirates Osaka. (2014). Nakamura awarded Nobel Prize in Physics. Hatena Blog. - https://ve42.co/NichiaSales1
Growth Bozu via Twitter. - https://ve42.co/NichiaSales2
Rose, J. (2014). Blue LEDs – Filling the world with new light. The Royal Swedish Academy of Sciences. - https://ve42.co/Rose2014
Pattison, P. M., et al. (2017). LED lighting efficacy. Comptes Rendus Physique. - https://ve42.co/Pattison2017
Electricity pricing via EIA - https://ve42.co/ElectricityPricing
Lane, K., et al. (2023). Lighting. IEA. - https://ve42.co/LightingIEA
LED Footprint via The Climate Group - https://ve42.co/ClimateGroupLED
Nichia’s History via Nichia - https://ve42.co/NichiaHistory
Shuji Nakamura via Wikipedia - https://ve42.co/NakamuraWiki

Images & Video:
Lighting the World via UCTVInsight on YouTube - https://ve42.co/UCTVep2 & https://ve42.co/UCTVep3
Palo Alto Times 1971 Article via Newspapers.com - https://ve42.co/Newspapers
Nick Holonyak, Jr. and the LED via UIUC on YouTube - https://ve42.co/HolonyakIllinois
The Original Blue LED via Science History Institute on YouTube - https://ve42.co/OGBlueLED
Maxfield, M. (2022). Compound Semiconductors. EE Journal. - https://ve42.co/Maxfield2022
M. Stutzmann, et al. (2001). Playing with Polarity. pss (b). - https://ve42.co/Stutzman2001
Isamu Akasaki in 1995 via Andrey Nikolaev on YouTube - https://ve42.co/AsakiNikolaev
Pioneer TX-610 Stereo Tuner via Ian Marino on YouTube - https://ve42.co/StereoMarino
Shuji Nakamura via EPO on YouTube - https://ve42.co/NakamuraEPO
Nichia Campus via Nichia on LinkedIn - https://ve42.co/NichiaHQ
Nichia via TDElektronik on YouTube - https://ve42.co/NichiaTDE
Violeds Sterilization of COVID-19 via Seoul Viosys - https://ve42.co/SterilizationUV

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3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!

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About the Course :

Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
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Why Learn Machine Learning with Python?

Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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🖥️ Download the free AI Agents Resources: https://clickhubspot.com/39c59b

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Links:
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Summary
If you're new to AI agents, this is the perfect place to start. In just 25 minutes, you'll learn exactly what an AI agent is, how it differs from traditional automations, and how to build one from scratch using n8n — no coding required. We’ll cover the core components of agent systems, how to set up guardrails, how agents interact with APIs, and what kinds of tools they can use. By the end, you'll have a working AI agent that can think, remember, and take action — and a clear understanding of how to build more. This is a hands-on, beginner-friendly guide designed to take you from zero to a working agent with practical, real-world applications.

Chapters
0:00 Intro
0:33 What is an Agent?
0:54 Agents vs. Automations
2:09 3 Main Components
3:29 Types of Systems
4:25 Guardrails
5:05 Resources
6:01 Recap
6:58 APIs and HTTP Requests
9:07 What Can You Build?
9:52 n8n Overview
11:08 Agent Build Overview
12:12 Set Trigger
12:26 AI Agent Node
13:20 Connect the Brain
14:40 Setting up Memory
15:54 Adding Tools
22:48 Testing and Debugging
24:53 Possibilities From Here

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.

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Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.

On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.

With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.

Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is Max Norm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

🔥Purdue - Applied Generative AI Specialization - https://www.simplilearn.com/applied-ai-course?utm_campaign=QTYkoK_ty7M&utm_medium=Lives&utm_source=Youtube
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This Generative AI Full Course 2025 by Simplilearn provides a structured learning path, starting with the fundamentals of Generative AI and a detailed roadmap to mastering it. Learners explore top AI technologies, including DeepSeek R1, Deep Learning, and Search GPT, followed by essential tools like LangChain. The course dives into Generative Adversarial Networks (GANs), Transformers, and Long Short-Term Memory (LSTM) networks, forming the foundation of modern AI models. It then introduces Large Language Models (LLMs), Machine Learning concepts, and Reinforcement Learning, leading into practical applications like ChatGPT analysis and OpenAI Sora. Advanced topics include LLM benchmarking, Hugging Face tutorials, and OpenAI's ChatGPT O1 model. Practical insights into Generative AI tools for job interviews, Agentic AI, and AI monetization strategies help learners stay ahead in the field. The course concludes with a look at Google Quantum AI and Machine Learning interview preparation, ensuring a strong grasp of both theoretical and applied AI concepts.

Following are the topics covered in the Generative AI Full Course 2025:

00:00:00 - Introduction to Gen AI Full Course 2025
00:00:45 - What is Gen AI
00:11:24 - Roadmap Gen AI
01:16:27 - What is Machine Learning
01:23:26 - Deep Learning
01:24:50 - Introduction to LLM
01:47:32 - What are Gen AI Agents
02:24:34 - Agentic AI
03:21:06 - Machine Learning Tutorial
05:26:29 - Reinforcement Learning
05:42:34 - Deepseek r1
05:53:47 - Install Deepseek
06:44:30 - Hugging Face and its tutorial
06:52:47 - Search GPT
06:55:02 - Langchain
07:51:37 - Generative Adversarial Tutorial
09:21:57 - Introduction to agentic workflow
09:31:19 - What are Gans
09:32:20 - Transformers in AI
09:44:49 - LSTM
10:04:40 - CHatgpt analyse
10:15:36 - Openai sora
10:24:29 - LLM Benchmarkeing
10:39:53 - Open ai chatgpt o1 model
10:48:16 - Google Quantam AI
10:53:49 - Majorana
10:58:58 - Gen ai tools for job interview
12:59:19 - Deep learning Interview questions
13:05:13 - Claude 3 sonnet

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➡️ About Professional Certificate Program in Generative AI and Machine Learning

Dive into the future of AI with our Generative AI & Machine Learning course, in collaboration with E&ICT Academy, IIT Guwahati. Learn tools like ChatGPT, OpenAI, Hugging Face, Python, and more.

Key Features:
✅ Program completion certificate from E&ICT Academy, IIT Guwahati
✅ Curriculum delivered in live virtual classes by seasoned industry experts
✅ Exposure to the latest AI advancements, such as generative AI, LLMs, and prompt engineering
✅ Interactive live-virtual masterclasses delivered by esteemed IIT Guwahati faculty
✅ Opportunity to earn an 'Executive Alumni Status' from E&ICT Academy, IIT Guwahati
✅ Eligibility for a campus immersion program organized at IIT Guwahati
✅ Exclusive hackathons and “ask-me-anything” sessions by IBM
✅ Certificates for IBM courses and industry masterclasses by IBM experts
✅ Practical learning through 25+ hands-on projects and 3 industry-oriented capstone projects
✅ Access to a wide array of AI tools such as ChatGPT, Hugging Face, DALL-E 2, Midjourney and more
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies

Skills Covered:
✅ Generative AI
✅ Prompt Engineering
✅ Chatbot Development
✅ Supervised and Unsupervised Learning
✅ Model Training and Optimization
✅ Model Evaluation and Validation
✅ Ensemble Methods
✅ Deep Learning
✅ Natural Language Processing
✅ Computer Vision
✅ Reinforcement Learning
✅ Machine Learning Algorithms
✅ Speech Recognition
✅ Statistics

Learning Path:
✅ Program Induction
✅ Programming Fundamentals
✅ Python for Data Science (IBM)
✅ Applied Data Science with Python
✅ Machine Learning
✅ Deep Learning with TensorFlow (IBM)
✅ Deep Learning Specialization
✅ Essentials of Generative AI, Prompt Engineering & ChatGPT
✅ Advanced Generative AI
✅ Capstone
Electives:
✅ ADL & Computer Vision
✅ NLP and Speech Recognition

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🔗 GitHub Repository
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https://platform.openai.com/do....cs/api-reference/int

🛠️ My VS Code / Cursor Setup
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⏱️ Timestamps
0:00 Introduction
2:45 Basic API Call
5:10 Structured Output
8:00 Using Tools
18:31 Memory and Retrieval
24:35 Building an AI Calendar Agent
34:59 Prompt Chaining
37:56 Routing Requests
41:57 Parallelization
46:18 Deploying Your AI Application

📌 Description
In this tutorial, we’ll cover everything you need to start building AI agents in pure Python. We’ll start with the essential building blocks and then dive into workflow patterns for more reliable systems. To follow along, basic Python skills are recommended, along with familiarity with the OpenAI SDK and an API key. I highly recommend cloning the GitHub repository so you can work through the code step by step. Watch me go through it first, then try it yourself to reinforce your understanding. I move quickly to cover a lot in 45 minutes, but you can always pause, rewind, or ask ChatGPT for help.

👋🏻 About Me
Hi! I'm Dave, AI Engineer and founder of Datalumina®. On this channel, I share practical tutorials that teach developers how to build production-ready AI systems that actually work in the real world. Beyond these tutorials, I also help people start successful freelancing careers. Check out the links above to learn more!

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

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

November 11, 2025
This lecture covers agents, prompts, and RAG.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

Please follow along with the course schedule and syllabus: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

NOTE: There was no class on November 4, 2025 (Lecture 7). The previous lecture is Lecture 6.

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

Intellipaat Training courses: https://intellipaat.com/

In this live session on Robot Framework Tutorial For Beginners, firstly we will learn automation
,Automation approaches
Basics of python
python unit testing
selenium WebDriver
read This video is must watch for everyone who wishes to learn Robot Framework With Python and make a career in it.

#RobotFrameworkTutorialForBeginners #RobotFrameworkWithPython #IntroductiontoRobotframework #Robotframework
#intellipaat

Following topics are covered in this session:
00:00 - Introduction to Robot Framework
02:30 - Course content
06:07- Benefits of Automation Testing
14:03- Why Automation
22:49- Automation Testing Process
31:04- Test Tool Selection
34:20- Framework For Automation
37: 32- Difference Between Manual and Automation Testing
41:49- Python Introduction
50:00- How To Install Python on Windows
56:25- Data Types and Variables
1:31:18- Looping Concepts
3:08:45- Polymorphism
3:31:48-Multiple Inheritance
3:34:36- Collection

Intellipaat is a global online professional training provider. We are offering some of the most updated, industry-designed certification training programs which includes courses in Big Data, Data Science, Artificial Intelligence and 150 other top trending technologies.
We help professionals make the right career decisions, choose the trainers with over a decade of industry experience, provide extensive hands-on projects, rigorously evaluate learner progress and offer industry-recognized certifications. We also assist corporate clients to upskill their workforce and keep them in sync with the changing technology and digital landscape.

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Website: https://intellipaat.com/

🔥AWS Training: https://www.edureka.co/aws-certification-training
This Edureka Live tutorial on ‘AWS Machine Learning Tutorial’ will introduce you to the nitty-gritty of Cloud Computing, Machine Learning and help you build an ML model using AWS.

🔹Amazon AWS Video Tutorial Playlist https://goo.gl/9fQX6J

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---------------------------------------Edureka Post Graduate Courses-------------------------------------------

🔵 Artificial and Machine Learning PGD: https://bit.ly/3AylL0q


How it Works?
1. This is a 5 Week Instructor led Online Course.
2. The 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, a 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

Who should go for this course?

This course is designed for students and IT professionals who want to pursue a career in Cloud Computing. The course is the best fit for:
1. Professionals interested in managing highly-available and fault-tolerant enterprise and web-scale software deployments.
2. Professionals who want Project Experience in migrating and deploying cloud-based solutions.
3. DevOps professionals.

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.

For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).

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

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

September 23, 2025
This lecture covers:
1. Class introduction
2. Examples of deep learning projects
3. Course details

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

To follow along with the course schedule and syllabus, visit: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

Wild life of Madagascar is just amazing. Are you ready for a new adventure? This amazing 8K wildlife documentary film will take you on a wonderful trip to Madagascar! Watch Madagascar's animals lying in the shade, hunting, eating, relaxing, or playing in the trees on a hot summer day. Get up close and personal with beautiful wildlife, enjoy the stunning island wildlife in 8K Ultra HD quality, and feel relaxed. Listen to the relaxing background music and let your imagination run to the wild. In this documentary film, you will see the most incredible and stunning animals. These are some of them: cat-eyed snakes, fossas, chameleons, leaf-tailed geckos, turtles, Malagasy Giant Sticks, different species of wild lemurs such as red ruffed lemur, and much more. Sticks Insects are really amazing creatures disguised as small tree branches. Look closely (59:54), it isn't eucalyptus branch, but unique stick's insect living in the wooded areas of island! You can see lemurs close up in the wild, and even take a look at lemur female with a baby (1:10:36). Our wildlife film will for sure help you spot these exceptional footage. You will plunge into the mysterious Madagascar's natural world full of dense forests, picturesque beaches, fabulous waterfalls, and calm rivers! Admire the gorgeous sunset on the island, listen to soothing nature sounds, and discover new countries with our team!

Video from: Republic of #Madagascar
Video Resolution: #8K
Video Type: nature documentary film
Filmmaker: Robert Hofmeyr
Assistant: Andrew Caldwell
Editor and Colorist: Andrii Mryha
Special THANKS to our professional filmmakers and editors for their fascinating, creative, hard, and challenging work.

Escape to wild nature and enjoy more Madagascar's #wildlife videos from http://proartinc.net and http://beautifulwashington.com

Soak in the views of Madagascar's nature face to face and from a bird’s eye view. You will see lots of local animals and spend your time in the most beautiful natural places on the Madagascar island. All these a unique nature scenes you can see in our 8K wildlife documentary film. Listen to the nature sounds and admire Madagascar's natural beauty. Enjoy almost 2 hours of wildlife whenever you want.
5 benefits of this wildlife documentary film:
1. Enjoy the wild animals in their natural habitats;
2. Get inspired by Madagascar's landscapes;
3. Take in the best wildlife watching moments;
4. Unwind and contact with wild nature;
5. Use this wildlife documentary as stunning video walls to create a relaxing atmosphere.
Explore Madagascar and its amazing wildlife on your Oled TV, Samsung 8K HDR TV, Sony 8K TV, LG 8K TV, etc. Relax with wild nature and use this 8K film as a beautiful background video, screensaver, or video walls for any waiting room/ relax room, office, dental clinic, public transport, pet shop, and other public places.

Watch Online: https://4krelax.com
Install Our ROKU App: https://goo.gl/tEyJQW
Install Our Android App: https://goo.gl/BFMznj
Install Our IOS App: https://itunes.apple.com/us/ap....p/4k-nature-relax-tv
Install Our Amazon Fire TV App: https://goo.gl/9TGEkm

Instagram: https://instagram.com/roman.nature.filmmaker
Visit my Travel Blog: http://goo.gl/AluKHt
For licensing questions: http://goo.gl/i0VUj6

If you want to help us with translation our videos into your language, you can do that by clicking on the link. This will help us to reach more people!
http://youtube.com/timedtext_cs_panel?c=UCg72Hd6UZAgPBAUZplnmPMQ&tab=2

Please SUBSCRIBE http://goo.gl/PZTVzf to my RELAXATION CHANNEL so you do not miss anything.

All the music used in the video is licensed through musicbed.com MB01U10DGYQPM5T

Watch live to learn about how the deep learning frameworks in MATLAB and Simulink can be used with TensorFlow and PyTorch to provide enhanced capabilities for building and training your Machine Learning model.

Heather Gorr, PhD and Yann Debray will show you can take full advantage of the MATLAB ecosystem and integrate it with resources developed by the open-source community. You can combine workflows that include data-centric preprocessing, model tuning, model compression, model integration, and automatic code generation with models developed outside of MATLAB.

Explore the options and benefits, along with examples, of the various interoperability pathways available, including:
- Importing and exporting models from TensorFlow, PyTorch, and ONNX into and from MATLAB
- Coexecuting MATLAB alongside installations of TensorFlow and PyTorch

🔥Edureka AWS Architect Certification Training: https://www.edureka.co/aws-certification-training
This video is about the features and benefits of AWS Kinesis. It shows how AWS Kinesis can be effectively used for processing the streaming data. The topics that we will cover in this session are as follows:
· What is AWS Kinesis?
· Advantages
· Capabilities
· Use Cases
· Kinesis vs SQS
· How it works?
· Demo

Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV
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#edureka #EdurekaAWS #AWSKinesis #awstraining #cloudcomputing
----------------------------------------------------------------------------
How does it work?
1. This is a 5 Week Instructor-led Online Course.
2. The 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, the learner will be able to:
1. Design and deploy scalable, highly available, and fault-tolerant systems on AWS
2. Understand the 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 the US: + 18338555775 (Toll-Free) or India: +91 9606058406 for more information.

🔥Edureka Machine Learning Certification Training: https://www.edureka.co/machine....-learning-certificat
This Edureka video on 'Data Exploration - Extract, Transform, Load' is the third class in the Python Machine Learning Series which gives a brief introduction to how we can explore the data starting from extraction and leading up to loading the data in a program.

🟠 𝐂𝐥𝐚𝐬𝐬 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐞 🟠

[𝗦𝗮𝘁𝘂𝗿𝗱𝗮𝘆 - 𝟭𝟱𝘁𝗵 𝗔𝘂𝗴𝘂𝘀𝘁]
Class 1 - Introduction To Machine Learning
Class 2 - Statistics For Machine Learning
Class 3 - Data Exploration 1 - ETL
Class 4 - Data Exploration 2 - Visualization
Class 5 - Data Exploration 3 - Data Cleaning
[𝐒𝐮𝐧𝐝𝐚𝐲 - 𝟏𝟔𝐭𝐡 𝐀𝐮𝐠𝐮𝐬𝐭]
Class 6 - Data Modeling 1 - Feature Engineering
Class 7 - Data Modeling 2 - Training The Model
Class 8 - Data Modeling 3 - Model Evaluation
Class 9 - Machine Learning Example
Class 10 - Advanced Machine Learning

Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s

🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV

-----------------------Edureka Online Training and Certification-----------------
🔵 DevOps Online Training: https://bit.ly/2BPwXf0
🟣 Python Online Training: https://bit.ly/2CQYGN7
🔵 AWS Online Training: https://bit.ly/2ZnbW3s
🟣 RPA Online Training: https://bit.ly/2Zd0ac0
🔵 Data Science Online Training: https://bit.ly/2NCT239
🟣 Big Data Online Training: https://bit.ly/3g8zksu
🔵 Java Online Training: https://bit.ly/31rxJcY
🟣 Selenium Online Training: https://bit.ly/3dIrh43
🔵 PMP Online Training: https://bit.ly/3dJxMTW
🟣 Tableau Online Training: https://bit.ly/3g784KJ
----------------------------Edureka Masters Programs--------------------------
🔵DevOps Engineer Masters Program: https://bit.ly/2B9tZCp
🟣Cloud Architect Masters Program: https://bit.ly/3i9z0eJ
🔵Data Scientist Masters Program: https://bit.ly/2YHaolS
🟣Big Data Architect Masters Program: https://bit.ly/31qrOVv
🔵Machine Learning Engineer Masters Program: https://bit.ly/388NXJi
🟣Business Intelligence Masters Program: https://bit.ly/2BPLtn2
🔵Python Developer Masters Program: https://bit.ly/2Vn7tgb
🟣RPA Developer Masters Program: https://bit.ly/3eHwPNf

-----------------------------Edureka PGP Courses--------------------------------

🔵Artificial and Machine Learning PGP: https://bit.ly/2Ziy7b1
🟣CyberSecurity PGP: https://bit.ly/3eHvI0h
🔵Digital Marketing PGP: https://bit.ly/38cqdnz
🟣Big Data Engineering PGP: https://bit.ly/3eTSyBC
🔵Data Science PGP: https://bit.ly/3dIeYV9
🟣Cloud Computing PGP: https://bit.ly/2B9tHLP

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Meetup: https://www.meetup.com/edureka/

#Edureka #PythonEdureka #pythonML #pythonMLseries #ETL #dataexploration #pythonprojects #pythonprogramming #pythontutorial #PythonTraining #learnPython
--------------------------
How it Works?

Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in the python programming language and many more.
--------------------------
Why Learn Machine Learning using Python?

Data Science is a set of techniques that enables computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms.
--------------------------
Who should go for this Machine Learning Certification Training using Python?
- Developers aspiring to be a ‘Machine Learning Engineer'
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- 'Python' professionals who want to design automatic predictive models
--------------------------
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775

🔥Professional Certificate Program in Generative AI and Machine Learning - IITG (India Only) - https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utm_campaign=y4wh1vbtYp8&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Purdue - Ai And Machine Learning Post Graduate Certificate Program - https://www.simplilearn.com/applied-ai-course?utm_campaign=y4wh1vbtYp8&utm_medium=DescriptionFirstFold&utm_source=Youtube

In this, How To Integrate ChatGPT with WhatsAppvideo, we will learn how to connect ChatGPT, the powerful language model developed by OpenAI, with WhatsApp on a Windows operating system. This ChatGPT On WhatsApp tutorial will guide you through the step-by-step process of setting up the integration, including installing the necessary software and configuring the settings. By the end of this ChatGPT integration video, you can use ChatGPT to generate responses to WhatsApp messages in real-time. This will allow you to automate your messaging and save time, making it easier to handle large numbers of messages at once

In this ChatGPT Whatsapp Integration Video, We will cover the following topics:

00:00 Introduction to Integrate ChatGPT in WhatsApp
00:39 Demonstration of our Project
04:23 Simplilearn Course Promotion
05:05 Quiz Question
05:44 Download the required files from Git
06:13 Create a folder for our project
07:20 Install Python for the project
08:52 Install Golang for the project
11:54 Install and Import Libraries
14:33 Download and Install gcc compiler
16:53 Hands-on Demonstration

🔥Link to source code files: https://github.com/abhisarahuj....a/Integrate_ChatGPT_

🔥Link to download gcc compiler: https://jmeubank.github.io/tdm-gcc/

While announcing the chatbot, OpenAI wrote on its announcement page, “We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.”

#ChatGPT #IntegrateChatGPTWithWhatsApp #ChatGPTWhatsAppTutorial #WhatsAppIntegration #ChatGPTTutorial #ChatGPTPython #PythonTutorial #Python #PythonProjects #GoLang #GoLangProjects #PythonForBeginners #AI #Simplilearn

✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

⏩ Check out more Python videos: https://youtube.com/playlist?l....ist=PLEiEAq2VkUUJO27

➡️ About Post Graduate Program In AI And Machine Learning

This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

✅ Key Features

- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools

✅ Skills Covered

- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…

👉 Learn More At: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=20FebHowToIntegrateChatGPTWithWhatsapp&utm_medium=Description&utm_source=youtube