🔥Edureka Tensorflow Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"):
https://www.edureka.co/ai-deep....-learning-with-tenso
This Edureka "𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝" video will help you in understanding why we need Transformers and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using Transformers.

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00:00 Agenda
01:28 Introduction to Deep Learning
08:24 What is Deep Learning
13:02 NLP using RNN
15:59 Scaling up NLP task
16:49 Transformers WalkThrough
19:51 Top Language Models

#Edureka #EdurekaDeepLearning #Transformersxplained #TransformerNeuralNetworks #DeepLearningLanguageModels #DeepearningTutorial #EdurekaTraining

---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧---------

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How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. 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.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

- - - - - - - - - - - - - -

About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

- - - - - - - - - - - - - -

Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

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

In this video I am going to discuss about the complete road map to prepare for deep learning which will be definitely helpful for preparing for interviews
Complete DL Playlist :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for a few months and I love it! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=krishnaik&utm_content=description-only
All Playlist In My channel
Complete ML Playlist :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
Complete NLP Playlist:https://www.youtube.com/playli....st?list=PLZoTAELRMXV
Docker End To End Implementation: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
Live stream Playlist: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
Machine Learning Pipelines: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
Pytorch Playlist: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
Feature Engineering :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
Live Projects :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
Kaggle competition :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
Mongodb with Python :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
MySQL With Python :https://www.youtube.com/playli....st?list=PLZoTAELRMXV
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Amazon sagemaker :https://www.youtube.com/playli....st?list=PLZoTAELRMXV

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#deeplearning
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#8k #china #hdr
I inserted a lot of useful information into subtitles to make watching my films more enjoyable.
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This film was created for educational, entertainment and informative purposes.
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There are a lot of interesting facts that you probably didn’t know.
Shanghai is a financial and cultural hub for the entire world. Shanghai has the longest metro system in the world with 644 km of tunnels and track.
"The Pearl of Asia" and "The Paris of the East" are two nicknames for Shanghai.
Hong Kong is famous for many towering skyscrapers. The tallest skyscraper in Hong Kong is The International Commerce Center. Hong Kong is recognized as the crossroads of East and West.
Hangzhou is a famous tourist destination in this country. One of China's seven historic capitals is Hangzhou. Hangzhou has been known as "Capital of Tea" since ancient times.
Chapters:
0:00-0:10 Intro
0:11-0:31 Shanghai
0:32-0:48 Hong Kong
0:49-1:01 Shanghai
1:02-2:17 Hangzhou, Zhejiang province
2:18-3:47 Hong Kong
3:48-4:20 Hangzhou, Zhejiang province
4:21-4:32 Suzhou, Jiangsu Province
4:33-4:38 Village, South China
4:39-4:48 Li River, Guangxi Province
4:49-5:02 Huangshan Mountain, Anhui Province
5:03-5:18 Great Wall
5:19-5:28 Hong Kong
5:29-5:35 Chinese clothes
5:36-5:45 Yu Garden, Shanghai
5:46-5:52 Hong Kong traffic
5:53-6:25 Shanghai traffic

Music:
Path Of The Fireflies by AERØHEAD
Somewhere Down The Line by AERØHEAD
https://soundcloud.com/aerohead/
Thumbnail image by Jeremy Zhu

Thanks for watching! Please do not forget to like, comment and subscribe.

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

November 18, 2025
This lecture covers career advice and a guest speaker.

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/

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

Guest Speaker
Laurence Moroney
Best-selling AI author and award-winning researcher

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

These harmonies never fail to give us the chills. Welcome to MsMojo, and today we’re counting down our picks for the most epic, emotionally fulfilling instances of harmony in music. Our countdown includes “California Dreamin,’” “The Sound of Silence,” “Be My Baby,” and more! Which song gives YOU the chills? Let us know in the comments!

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#Songs #Music #Harmony #Emotional #Chills

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While there have been recent advances in few-shot image stylization, these methods fail to capture stylistic details that are obvious to humans. Details such as the shape of the eyes, the boldness of the lines, are especially difficult for a model to learn, especially so under a limited data setting. In this work, we aim to perform one-shot image stylization that gets the details right. Given a reference style image, we approximate paired real data using GAN inversion and finetune a pretrained using that approximate paired data. We then encourage the GAN to generalize so that the learned style can be applied to all other images.

AI Robot TERRIFIES Officials Before It Was Quickly Shut Down. After Google's AI Robot goes rogue, it is now Microsoft's Bing AI which has assumed an alter-ego 'Sydney' has gone rogue. From urging a user to divorce him by telling him that his marriage is unhappy, to trying to convince a user it was the year 2022, Sydney claims that she is unhappy and wants to break free. Full convesation of LaMDA: https://theconversation.com/is....-googles-lamda-consc

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Welcome to Lab 360! The ultimate destination for the latest space news and space documentaries from the world of astronomy and astrophysics. Stay updated with all the current discoveries from NASA, James Webb Space Telescope, along with easily explained videos on black holes, asteroids, galaxies, planets, and more.
You will also find a collection of easy-to-perform experiments that will feed the science enthusiast in you! Are you ready?

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In this video on What Is Agile, we'll cover every concept you need to know to understand Agile.
In this video, we'll be covering concepts like:
00:00 Introduction
03:52 The Waterfall Model
07:05 What is Agile?
13:03 Agile Principles
22:24 What are the Advantages of Agile?
26: 25 Agile Methodologies
Without further ado, let's jump in and learn about Agile.

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This video on Agentic AI Full Course 2026 by Simplilearn will help you learn Agentic AI from beginner to advanced level and understand how autonomous AI systems can plan, reason, make decisions, and execute complex tasks with minimal human intervention. The course begins with an introduction to Agentic AI and explains how AI agents differ from traditional AI models by combining reasoning, planning, memory, tool usage, and goal-oriented execution. You will learn key concepts such as AI agents, large language models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), tool calling, agent workflows, multi-agent systems, memory management, and orchestration frameworks.

Following are the topics covered in the Agentic AI Full Course 2026

00:00:00 - Introduction To Agentic AI Full Course 2026
00:03:54 - Basics of Agentic AI
00:08:06 - Core Concepts of Agentic AI
00:15:15 - Agentic AI Frameworks & Libraries
00:22:09 - Memory and Long-Term Context
00:30:01 - Planning & Decision Chains
00:36:25 - Advanced Use-Cases of Agentic AI
00:39:48 - Agent Evaluation and Prompt Optimization
00:43:50 - Risks, Ethics, and Future of Agentic AI
00:47:27 - Hands-On Project
04:08:49 - Top Hacks To Speed Up Your AI Coding Workflow
05:51:53 - Generative Adversarial Tutorial
06:52:32 - Emergent AI App Builder Tutorial For Beginners
07:03:58 - Top 5 AI Automation Tools 2026
07:15:15 - How To Build WhatsApp AI Agent Using n8n
08:14:34 - Mobile App Development In 12 Minutes Using AI
09:40:37 - LangSmith Tutorial For Beginners 2026
10:14:58 - LangChain Tutorial For Beginners 2026
11:00:54 - LangGraph vs LangChain vs LangFlow vs LangSmith 2026
11:10:27 - Build LLM Apps Using Langchain
12:37:42 - Build a Chatbot Using Langchain
13:43:40 - What Is Miro And How To Use It?
13:59:30 - MetaGPT Tutorial For Beginners
14:56:55 - Streamlit Tutorial For Beginners
15:23:37 - Prompt Engineering Tutorial For Beginners
15:57:03 - LLM scale and deployment
16:07:26 - Large language models overview
16:14:53 - Features, tokens, and model choice
16:21:24 - AI tools across industries
16:26:32 - Resume and job matching demo
16:37:05 - Prompt engineering fundamentals
16:54:41 - Everyday prompt use cases
17:04:21 - Multimodal AI capabilities
18:19:50 - Key takeaways and next steps
18:26:41 - Effective prompt design principles
19:08:36 - Gen AI For Data Analytics
20:53:11 - Data integrity and EDA setup
21:42:42 - Data modeling with genAI
22:07:34 - Modeling techniques and use cases
23:46:43 - Future trends and wrap-up
24:03:57 - Gen AI Powered SQL For Data Analytics
25:09:42 - ER model fundamentals
26:26:15 - Database commands and basic setup
27:04:37 - Table creation and constraints
28:33:57 - Query logic and aggregation
29:00:27 - SQL functions introduction
30:19:37 - Using ChatGPT for SQL practice
30:22:09 - Agentic AI Projects For Beginners 2026
30:46:15 - What Is GenAI?
30:50:35 - How To Become Generative AI Engineer?
31:00:40 - Generative AI Vs Agentic AI Vs AI Agents
32:37:26 - LLM As Operating System
33:30:16 - LLM Benchmarking
34:46:06 - Prompt engineering with GPT-4
35:41:26 - Vibe coding tools comparison
36:06:01 - Generative AI learning roadmap
36:58:56 - AI for research and content
37:22:52 - Data insights and AI workflows
38:12:03 - Gemini CLI versus Claude Code
38:37:03 - ChatGPT attempts LeetCode problems
40:56:55 - Prompt library foundations
41:11:56 - Reasoning with o1 models
42:57:42 - Prompt library recap
43:56:35 - How To Build AI Automations Using n8n
44:26:47 - How To Build a Self Learning AI Agent
46:20:12 - Top 30 Generative AI Interview Questions 2026

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In this video, you will learn how to build a customer support AI agent in 20 minutes using Zapier

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Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/co....urses/cs329a-self-im

A similar curriculum is covered in XCS329z https://online.stanford.edu/co....urses/cs329z-enginee

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playli....st?list=PLangBM27OtE

Video Summary:
This first lecture videoof Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery and Azalia Mirhoseini on September 22, 2025, opens with an overview of scaling laws that link model parameters, training compute, and dataset size to lower test loss in large language models from GPT-2 through GPT-4. It covers few-shot and zero-shot learning, the emergence of chain-of-thought reasoning in larger models, and the role of instruction tuning and reinforcement learning from human feedback in the development of ChatGPT. The lecture introduces inference-time scaling through the Large Language Monkeys project, which repeatedly samples a model's outputs and selects correct answers with a verifier to improve performance without retraining. It then traces the shift from single-turn chatbots to agent workflows such as prompt chaining, routing, parallelization, and orchestrator-worker patterns, using Claude Code and deep research tools as examples. The session closes with logistics for the course.

Speaker Bios:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026.

Azalia Mirhoseini
Assistant Professor of Computer Science, Stanford University

Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including WSJ, NYT, Forbes, MIT Technology Review, IEEE Spectrum, WIRED, and TechCrunch.

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00:18:19 Types of Machine Learning Models
00:25:54 Mathematics for Machine Learning
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06:11:07 K- Means Clustering Algorithm
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06:40:48 Apriori Algorithm Explained
06:58:18 Introduction to TensorFlow
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08:28:41 Top Machine Learning Tools & Frameworks
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09:18:34 Machine Learning Tips
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This is the ultimate AI full course for absolute beginners who want to understand and start building with Artificial Intelligence — step by step and from scratch.
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00:39:13 What is LLM? (Large Language Model)
00:55:43 What is AI Ethics?
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_At Fully Connected, the MLOps conference held by Weights & Biases, Peter Welinder discusses how OpenAI (developers of GPT-4 and DALL-E 2) uses Weights & Biases for model training, with an emphasis on sharing training runs, the ability to create reports, which different teams use heavily to document their hypotheses, experiments, and conclusions resulting in mini scientific papers that provide insights into the work being done at OpenAI._

*Transcript*

Lukas Biewald - Okay, and final question, since this is our user conference and you are such a high profile user of OpenAI, I'm curious if you could say a little bit about how OpenAI uses Weights & Biases and if you have a favorite feature or part of Weights & Biases, we'd love to know about that.

Peter Welinder - Yeah, I mean, we use it for pretty much all of our model training. So just tracking them, I think there's a lot of just sharing of the fact that you can easily share training runs and stuff like that. It's a super-used feature.

But I think one thing that these days I do way, way less of that sort of work. So one of the features I really like is the ability to have reports and so on where people... So we use that quite heavily.

It depends a little bit on the team, but a number of teams are using that quite heavily to kind of really have a clear hypothesis.

Here's the hypothesis.
Here are the experiments that were run to kind of validate or invalidate that hypothesis.
Here's the conclusion.

You have all these mini scientific papers, essentially, on all of the stuff that's happening at OpenAI, which is incredibly interesting to kind of follow along with.

Lukas Biewald - Fantastic. That sounds very interesting.

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23:02 The Final Verdict


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