How ChatGPT is Trained

In this video, we take a look at how ChatGPT is trained. ChatGPT is a large language model that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. It is trained on a massive dataset of text and code, and it is able to learn and adapt to new information.
blog link: https://www.linkedin.com/pulse..../discover-how-chatgp
Also check
Is Bard better than chatgpt : https://www.youtube.com/watch?v=Ae6q7v7pOxM&t=673s&pp=ygULa3Jpc2ggbmFpayA%3D

Timestamp
00:00:00 Introduction
00:03:25 3 stages of training
00:07:25 Generative Pretraining
00:09:36 Supervised Fine Tuning
00:13:18 Reinforcement Learning Through Human Feedbacks

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In this video, we'll explore the risks and opportunities associated with large language models (LLMs). We'll discuss the potential benefits of LLMs, including their ability to improve natural language processing, generate human-like text, and enhance decision-making. However, we'll also examine the potential risks, such as biases and misuse.

Join us for a balanced discussion of LLMs and their impact on the future of AI.
Learn more about LLMs on our latest blog article: https://www.boost.ai/blog/llms....-large-language-mode

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐨𝐮𝐫𝐬𝐞 - 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: https://www.edureka.co/openai-....chatgpt-training-cou
This 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥 is intended as a Crash Course on 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 for Beginners. 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 has been growing in popularity exponentially. But, 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 is still not known to many people. In this video, I aim to show you the different ways in which you can use 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 for yourself.
The following topics are covered in this 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 Tutorial video:
00:00:00 Introduction to ChatGPT
00:01:03 About OpenAI
00:01:40 What is ChatGPT
00:01:58 Risks of ChatGPT
00:02:18 How to use ChatGPT
00:03:10 ChatGPT Interface
00:05:12 Using prompts in ChatGPT
00:07:31 Tricking ChatGPT to give wrong answers!
00:08:00 Conversation threads in ChatGPT
00:08:46 Shopping List using ChatGPT
00:10:36 ChatGPT for Coding
00:12:35 Using ChatGPT for Writing Content
00:13:43 Conclusion

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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

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Chat GPT for Programming by simplilearn is dedicated for aspiring software developers and web developers to understand the fundamentals of Programming Using ChatGPT. This ChatGPT For Beginners by simplilearn, is a roadmap for creating a compelling YouTube description for a channel focused on programming with ChatGPT can enhance your visibility and engagement.

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⏩ Check out More AI Videos By Simplilearn: https://youtube.com/playlist?l....ist=PLEiEAq2VkUULyr_

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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.

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Large language models-- or LLMs --are a type of generative pretrained transformer (GPT) that can create human-like text and code. There's a lot of talk about GPTs and LLMs lately, but they've actually been around for years! In this video, Martin Keen briefly explains what a LLM is, how they relate to foundation models, and then covers how they work and how they can be used to address various business problems.

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdK65X

#llm #gpt #gpt3 #largelanguagemodel #watsonx #GenerativeAI #Foundationmodels

Transformer Neural Networks are the heart of pretty much everything exciting in AI right now. ChatGPT, Google Translate and many other cool things, are based on Transformers. This StatQuest cuts through all the hype and shows you how a Transformer works, one-step-at-a time.

NOTE: If you're interested in learning more about Backpropagation, check out these 'Quests:
The Chain Rule: https://youtu.be/wl1myxrtQHQ
Gradient Descent: https://youtu.be/sDv4f4s2SB8
Backpropagation Main Ideas: https://youtu.be/IN2XmBhILt4
Backpropagation Details Part 1: https://youtu.be/iyn2zdALii8
Backpropagation Details Part 2: https://youtu.be/GKZoOHXGcLo

If you're interested in learning more about the SoftMax function, check out:
https://youtu.be/KpKog-L9veg

If you're interested in learning more about Word Embedding, check out: https://youtu.be/viZrOnJclY0

If you'd like to learn more about calculating similarities in the context of neural networks and the Dot Product, check out:
Cosine Similarity: https://youtu.be/e9U0QAFbfLI
Attention: https://youtu.be/PSs6nxngL6k

If you'd like to support StatQuest, please consider...
Patreon: https://www.patreon.com/statquest
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YouTube Membership: https://www.youtube.com/channe....l/UCtYLUTtgS3k1Fg4y5

...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store...
https://statquest.org/statquest-store/

...or just donating to StatQuest!
paypal: https://www.paypal.me/statquest
venmo: @JoshStarmer

Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer

0:00 Awesome song and introduction
1:26 Word Embedding
7:30 Positional Encoding
12:53 Self-Attention
23:37 Encoder and Decoder defined
23:53 Decoder Word Embedding
25:08 Decoder Positional Encoding
25:50 Transformers were designed for parallel computing
27:13 Decoder Self-Attention
27:59 Encoder-Decoder Attention
31:19 Decoding numbers into words
32:23 Decoding the second token
34:13 Extra stuff you can add to a Transformer

#StatQuest #Transformer #ChatGPT

A video about reinforcement learning, Q-networks, and policy gradients, explained in a friendly tone with examples and figures.

Introduction to neural networks: https://www.youtube.com/watch?v=BR9h47Jtqyw

Introduction: (0:00)
Markov decision processes (MDP): (1:09)
Rewards: (5:39)
Discount factor: (8:51)
Bellman equation: (10:48)
Solving the Bellman equation: (12:43)
Deterministic vs stochastic processes: (16:29)
Neural networks: (19:15)
Value neural networks: (21:44)
Policy neural networks: (25:44)
Training the policy neural network: (30:46)
Conclusion: (34:53)

Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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This video shows how you can detect if a sequence is periodic or not, and also find its period, using the Discrete Fourier Transform.

This is the second in a series of videos about Fourier transforms.

- First video: The Discrete Fourier Transform https://www.youtube.com/watch?v=T8XZxR5H04E
- Second video: Detecting Periodicity with the DFT (this one)

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00:00 Introduction
2:37: Periodic sequences
12:54 In the inverse DFT

In this video, learn how to build your own Small Language Model (SLM) from scratch using Python, PyTorch, and Hugging Face datasets. We’ll walk through every step — from data collection and preprocessing to tokenization, transformer architecture, and model training.

You’ll discover how a compact 10–15 million parameter model can still generate coherent and meaningful text, using concepts inspired by GPT models. This tutorial simplifies complex AI topics like embedding layers, attention mechanisms, loss computation, and next-token prediction — all explained clearly with working code examples.

🔹 Topics Covered:

What are Small Language Models (SLMs)?

Using the TinyStories dataset from Hugging Face

Tokenization with OpenAI’s tiktoken library

Creating .bin training files for efficient processing

Building a Transformer architecture step-by-step

Implementing GPT-like attention blocks

Training configuration and hyperparameter tuning

Generating text from your trained SLM

🎯 Perfect for AI researchers, ML engineers, and enthusiasts who want to understand how GPT-style models actually work under the hood — without requiring massive GPUs or billion-parameter setups.

#SmallLanguageModel #BuildLLM #GPT2Tutorial #TransformerModel #PyTorch #AIML #DeepLearning #NLP #HuggingFace #TrainYourOwnGPT #SLM #LanguageModel #MachineLearning #ArtificialIntelligence #NanoGPT #AIProjects #PythonCoding #AICoding #OpenSourceAI #generativeai

Building a research multi agent system → https://goo.gle/41TYswp
Agent starter pack → https://goo.gle/4iZiz35
Human-in-the-Loop LangGraph app → https://goo.gle/4iyii7h

What is agentic AI and how can developers apply agentic AI in their applications? Welcome back to season 2 of Real Terms for AI with Googlers Aja Hammerly and Jason Davenport. In this video, Aja and Jason discuss agentic workflows, how agents are different from workflows, and when to use an agentic workflow. Follow along with code snippets in the Github link and help implement these concepts in many use cases.

Chapters:
0:00 - Intro
0:43 - What does agentic mean?
2:08 - Agentic workflow examples
3:28 - When to use agentic workflow or AI agent
4:33 - Wrap up

Watch more Real Terms for AI → https://goo.gle/AIwordsExplained
Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech

#GoogleCloud #GenerativeAI

Speakers: Aja Hammerly, Jason Davenport
Products Mentioned: Gemini, Gemma

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.

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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

Learn about Stanford's online artificial intelligence professional and graduate programs:
https://stanford.io/3CDAIOV

Stanford Online Graduate and Professional AI programs provide the foundational and advanced skills you need to accelerate your career in AI, including machine learning, reinforcement learning, neural networks, and natural language processing and understanding. Learn about the differences between the programs in this video.

#artificialintelligence #machinelearning #deeplearning #reinforcementlearning #neuralnetworks #naturallanguageprocessing

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Join us for the AI & ML Full Course 2025, a complete live tutorial hosted by edureka! designed to help you master Artificial Intelligence and Machine Learning from the ground up. This live session will guide you through key concepts such as supervised and unsupervised learning, deep learning, neural networks, natural language processing, and real-world model deployment. Whether you're a beginner looking to enter the world of AI or a professional aiming to enhance your skills, this course is structured to make complex topics easy to understand. Using hands-on projects and Python-based examples, you’ll gain the practical knowledge needed to build intelligent systems and data-driven applications. This session is ideal for students, job seekers, developers, and tech enthusiasts who want to stay ahead in the AI/ML landscape in 2025. Make sure to join live to engage with instructors in real time and earn your certificate of participation. Subscribe to our channel and turn on notifications so you don’t miss this powerful learning experience.
00:00:00 Introduction
00:01:47 What is Artificial Intelligence?
00:07:49 Types of Artificial Intelligence
00:18:56 AI vs Machine Learning vs Deep Learning
00:32:41 Artificial Intelligence with Python
02:11:47 What is Machine Learning?
02:23:29 Types of Machine Learning Models
02:30:35 Machine Learning Algorithm
02:52:29 Linear Regression Algorithm
02:59:55 Logistic Regression Algorithm
03:47:04 Linear Regression Vs Logistic Regression
03:50:35 Decision Tree Algorithm
04:36:09 Random Forest
05:01:24 KNN Algorithm
05:33:47 Naive Bayes Classifier
05:55:28 Support Vector Machine
06:20:59 K- Means Clustering Algorithm
06:44:19 Hierarchical Clustering
06:50:41 Apriori Algorithm Explained
07:08:11 What is Deep Learning?
07:30:14 Artificial Neural Network
08:02:35 Convolutional Neural Network
08:23:06 Recurrent Neural Networks
08:52:02 LSTM Explained
09:45:59 Transformers Neural Networks Explained
09:56:02 What are GANs?
10:08:22 Future of AI/ML
10:22:58 Artificial Intelligence Interview Questions & Answers

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

📢📢Check out the latest 2025 video on Top 10 Technologies for the most up-to-date insights!
📌 Top 10 Technologies to Learn in 2025 → https://youtu.be/5kjWh8lBxC4

📝Feel free to share your comments below.📝

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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This Edureka video on Build MERN Stack App from Scratch" will provide you with fundamental concepts of MongoDB, Express.js, React.js, and Node.js with hands-on.

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00:00:00 - Introduction
00:02:43 - How to Master Data Science
00:14:50 - Demand for Data Science Professionals
00:28:24 - Data Science Learning Path
00:35:52 - Statistics & Probability
00:57:26 - What is Statistics?
01:39:19 - What is probability?
01:55:06 - Bayesian tree
02:52:13 - Sampling distribution
03:23:25 - Systematic Sampling
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04:37:32 - Numpy Indexing and Slicing
04:43:15 - Array Manipulation
05:04:33 - Introduction to Pandas
06:13:50 - Introduction to Matplotlib
06:17:09 - Types of Plots in Python
06:29:59 - Line plots
06:45:51 - Bar charts
06:50:18 - Scatter plots
06:54:21 - Histogram
07:26:51 - Pie charts
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08:36:41 - Logistic Regression
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In this Data Analytics for beginners video you will learn what is data analysis, the life cycle of data analysis, the various skills to become a data analyst, the difference between data analysis and data science, the difference between a data analyst and data engineer, pandas, data analysis using pandas, NumPy and data analysis interview questions in detail.

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00:01:30 - What is Data Analytics?
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01:42:32 - Creating Numpy Array
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