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Movie : Tevar (तेवर मूवी)
Starring : Arjun Kapoor, Sonakshi Sinha, Manoj Bajpayee, Shruti Haasan
Director : Amit Ravindernath Sharma
Producer : Sanjay Kapoor
Music : Sajid–Wajid
Lyrics : Shantanu Shrivastav
Label: DRJ Records

Tevar (transl. Attitude) is a 2015 Indian Hindi-language action film directed by debutante Amit Ravindernath Sharma and produced by Boney Kapoor, Sanjay Kapoor, Sunil Lulla, Naresh Agarwal and Sunil Manchanda, based on a script by Sharma and Shantanu Shrivastav. An official remake of the 2003 Telugu film Okkadu, the film stars Arjun Kapoor and Sonakshi Sinha while Manoj Bajpayee plays the primary antagonist. Shruti Haasan makes a special appearance in a special dance number, while crooning another song on the soundtrack.[3] The film was released worldwide on 9 January 2015


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The Chat GPT API is now available for developers to start using. Finally you can integrate Chat GPT into your Apps!

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This video on Google Analytics full course will help you to understand the google analytics setups, various features of google analytics, google analytics 4 and much more!
Google Analytics is a web analytics service offered by Google that enables website owners and marketers to track and analyze various aspects of their website's performance and user behavior. It provides a wide range of features and functionalities, including tracking website traffic, analyzing audience demographics and behavior, monitoring conversions and e-commerce data, and customizing reports. By integrating a tracking code into their website, users can gather valuable insights to optimize their online presence and marketing strategies. It's important to consult the official Google Analytics documentation for the latest updates on its capabilities as Google regularly enhances its products and services. As per the sources, in April 2022, Google Analytics was used by 73% of the 10,000 most popular websites across the globe and around 28.1 million websites use google analytics for their business growth.


00:00 Google Analytics Full Course
02:09 Google Analytics Vs Universal Analytics
10:26 Google Analytics
2:11:20 Google Tag Manager
3:00:37 How to rank #1 on Google

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🔥 Caltech Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/artificial-intelligence-masters-program-training-course?utm_campaign=20July2023ChatGPTFullCourse&utm_medium=Descriptionff&utm_source=youtube
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ChatGPT, an extraordinary creation of artificial intelligence. Born from the brilliance of OpenAI's GPT-3.5 architecture, ChatGPT is designed to engage, educate, and entertain. With a vast expanse of knowledge and the ability to adapt to individual preferences, ChatGPT is here to provide valuable insights, answer questions, and spark captivating conversations. From exploring various subjects to assisting with creative endeavors, ChatGPT is ready to embark on a journey of discovery with you. Whether you are a marketing expert, cybersecurity expert, a developer or a blogger, ChatGPT is all you need!
This full course on chatgpt, will help you to excel in it in digital marketing domain, cybersecurity, ui ux, content creation, seo. With this we will also put a light on how to use ChatGPT to make money!

00:00 Introduction to ChatGPT
06:16 How does ChatGPT work?
11:32 What is GPT 4?
15:11 Automate Excel using ChatGPT
01:27:57 How to use ChatGPT for SEO?
02:17:28 Chat GPT for Content Creation
02:26:21 Master SEO Content Using ChatGPT
02:28:08 ChatGPT for Marketing
02:38:50 How to Build a Website Using ChatGPT
03:20:50 How To Use ChatGPT For Web Development
03:36:57 Chat GPT for Programming
03:45:17 Can ChatGPT Solve LeetCode Problems?
05:11:49 Create a Telegram Bot Using ChatGPT
05:50:30 How To Integrate ChatGPT With Whatsapp
Automate WhatsApp using ChatGPT
06:06:14 Build a Game using Python and ChatGPT
06:38:44 ChatGPT in Cybersecurity
07:00:29 ChatGPT for UI/UX Design
07:47:42 Top 10 Chat GPT Use Cases
08:13:46 6 Reasons Why ChatGPT Can Make Your Business
08:23:19 9 Ways to Make Money Online Using ChatGPT
08:43:48 Bing vs Bard vs ChatGPT : Comparison
09:08:34 Will ChatGPT and Bard Take Over SEO?

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Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME's excellence and IBM's industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions.

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What’s up gadget-heads and welcome to another episode of TechJoint! Today, we’re bringing you the 10 TERRIFYING Military Robots That Really Exist! For the most part, robots exist to do good things. We’ve built them to vacuum our floors, work on assembly lines and even play fetch with our dogs. But because the military has always been at the cutting edge of technology, it should come as no surprise that some of the most advanced robots in the world are being built with military applications in mind. From tiny spy robots to pure death machines, let’s look at some of these most terrifying military robots. Before we begin, make sure you hit that subscribe button and turn on notifications so you never miss out on the latest and greatest tech.

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Поддержать проект можно по ссылкам:
Если вы в России: https://boosty.to/vertdider
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Доказательство гипотезы Коллатца — возможно, самая простая задача, с которой до сих пор никто не справился. Понять её суть может даже школьник, а вот над решением бьются величайшие математики уже почти сотню лет. Что это за гипотеза и чем она примечательна, расскажет Дерек Маллер в новом видео.

Перевод: Алексей Лоскутов
Редактура: Алексей Малов
Научная редактура: Михаил Столповский, Александр Пушкин
Озвучка: Дмитрий Чепусов, Юрий Катарманов
Монтаж звука: Андрей Фокин
Монтаж видео: Джон Исмаилов
Обложка: Андрей Гавриков

Спасибо за поддержку на https://www.patreon.com/VertDider
Озвучки Vert Dider выходят с вашей помощью:

Maria Zvereva, Pavel Dunaev, Nick Denizhenko, Oleksii Leonov, Yegor Barakovskiy, Mikhail Stolpovskiy, Дмитрий Глазнев, Кирилл Л, Anton Borkovski, Ilya, smiler, Антон Малинин, Roman Inflianskas, Maxim Syunikov, Oleg Zingilevskiy, Serega Beltser, Rashid Nasibulin, opperatius, Alena, Vitaliy Vachynyuk, Ника Калмыкова, Евгений Сельменев, Alex Afalex, Anton Kudashov, Sergii Cherepanov, Владимир Кашутин, Pavel Parpura, Anton Bolotov, Yevhen, Sergei W, Kosoy, Михаил Панькин, Katoto Chan, Anton Makiievskyi, Nikolay Chutskov, Arthur Skobara, 666.13FM, Andrew Rumyantsev, Александр Зуев, Marianna Sumina, Мария Баранова, Maxim Arkushin, Илья Соловьёв, Дмитрий, Oleg Kovalov, Vadim Cpp, Artem Amirbekov, Dzmitryi Halyava, Nastya, POD666, Aleksei Shabalin, Timophey Popov, Лиля Малкова, Natalie Kulinicheva, Ilya Afanasyev, Marat Bakirov, Ирина Завтонова, Anastasia, Andrew Rumak, Andrey Istomin, Марина Малинкина, Ирина Анатольевна Чулкова, Pavel Golovin, Vadim Velicodnii, Юрий Медведев, Анна Троссман, Serj Skidan, L Marchenko, Nadia Kilgishova, Andrey Rusanov, Inna Klymenko, Dmitriy Lashtaba, Ilya Alexeevsky, Alexander Balynskiy, Максим Иванов, Alexander, Maria Tronina, Alexander Gorodok, Ivan Iakimov, Alexander Zimin, Otto Kester, Виталий Пастушенко, Claudia Barzaeva, Lirin Alex, Konstantin Pesyakov, Denis Titusov, Viktoria, Alexandr Globov, Olga Podolskaya, F23D24, Пугачёв Пётр, Alex Katkov, Irina Shakhverdova, Dmitriy Omelyansky, Aleksey Sazonov, Vladimir Goshev, Vladyslav Sokolenko, Anton Novozhenin, Spartak Kagramanyan, Александр Фалалеев, Evgeny Vrublevsky, angel

#VertDider #Veritasium

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Источник: https://youtu.be/094y1Z2wpJg

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Чем старше мы становимся, тем быстрее пролетают года. Но почему так происходит? И можем ли мы как-то замедлить ход времени?

Перевод: Алексей Лоскутов
Редактура: Анна Лысикова
Озвучка: Вадим Казанцев, Юрий Катарманов, Екатерина Муравицкая
Монтаж звука: Андрей Фокин
Монтаж видео: Джон Исмаилов
Обложка: Эльдар Нургалиев

Спасибо за поддержку на https://www.patreon.com/VertDider.
Озвучки Vert Dider выходят с вашей помощью:

Pavel Dunaev, Nick Denizhenko, Yegor Barakovskiy, Oleksii, Ігор Дорохов, Mikhail Stolpovskiy, Роман Чурин, Aiorraro, Евгений Сельменев, Alex Afalex, Sergei W, Виталий Алешко, Luba Karpenko, Владимир Кашутин, Pavel Parpura, Anton Bolotov, Yevhen, Katoto Chan, Kosoy, Данил Рягузов, Михаил Панькин, Anton Makiievskyi, Максим 'Sheridan' Горлов, Sultan Ishankulov, Александр Яковлев, Mike Sviblov, Andrey Rusanov, Inna Klymenko, Dima Chumakov, Dmitriy Lashtaba, Olesya Bolobova, Ilya Alexeevsky, Alexander Balynskiy, Максим Иванов, Ihar Marozau, Мария Соозарь, Ольга Канкулова, Alexander, Viktor Lova, Maria Tronina, Sergii Cherepanov, Alexander Gorodok, Ivan Iakimov, Alexander Zimin, Евгений Миф, Виталий Пастушенко, Serj Kravchuk, Claudia Barzaeva, Pandaben, Monza UA, Lirin Alex, Konstantin Pesyakov, Denis Titusov, Alterien, Viktoria, Alexandr Globov, Oleg Streltsov, Olga Podolskaya, Alexey Kukushkin, F23D24, Olga Shistareva, Kamerton_440, Alex Katkov, Alexey, Evgeny Vrublevsky, Irina Shakhverdova, Vladimir Grachev, Dmitriy Omelyansky, Maria Burtseva, Aleksey Sazonov, Vladimir Goshev, Vladyslav Sokolenko, Дмитрий Мисюра, Yuliia Zinko, Anton Novozhenin, Spartak Kagramanyan, Александр Фалалеев

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© https://www.youtube.com/user/1veritasium
Источник: https://youtu.be/aIx2N-viNwY

It’s been over a year since I last made a list like this so I decided to give it an update. I also decided to make this an annual list. The top 100 songs that I believe the most people in the world will recognize. I looked at charts, streams, sales, and overall pop culture relevance but at the end of the day, this is all just my opinion (which makes this list even more correct).

No Christmas songs, children’s songs, classical music, or film scores were included as that would take up 90% of the list and make it boring.

Spotify playlist: https://open.spotify.com/playlist/50ozhW6x8mS6r64G4Dru0c?si=5g86KH9nSOmyBnrC8UkbRA&pi=u-G6q8mcW6Sxab
Thumbnail: https://youtube.com/@Queen?si=vQYckgvP5U9G_4_E

Top 100 Most Recognizable Songs of All Time (2024 version)

#music #songs #top100

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

Not only have I built hundreds of AI agents myself, I've seen other people build thousands of AI agents for every use case under the sun. The people who are the most successful are the ones who don't overcomplicate it - and I want that to be you too.

It's easy to think building AI agents is super complicated, but honestly you can learn 90% of what you need to know (and what to focus on) from this video. No matter how you're building your agents, I'll show you here what you need to think about, and more importantly what you shouldn't worry about when you're first creating your agent.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Platforms mentioned in this video (most are open source!):

- Synk for automated AI-native app security:
https://snyk.plug.dev/xgmYQhO
- Guardrails AI for agent guardrails:
https://github.com/guardrails-ai/guardrails
- Pydantic AI for the AI Agent framework:
https://ai.pydantic.dev/
- Mem0 for long term memory:
https://mem0.ai
- Langfuse for agent observability:
https://langfuse.com/
- Docker for deployment:
https://www.docker.com/

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- If you want to master AI coding assistants and learn how to build systems for reliable and repeatable results, check out the new Agentic Coding Course in Dynamous:
https://dynamous.ai/agentic-coding-course

- Here is the repo for the super simple AI Agent we built in this video:
https://github.com/coleam00/ot....tomator-agents/tree/

- Here is the system prompt template I covered:
https://docs.google.com/docume....nt/d/1-OB4ZMg20pIRVm

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Thanks to Snyk for working with me on the security portion of this video! It's been a pleasure and I really do believe Snyk is on the forefront of software/agent/AI coding security especially with their new MCP server:
https://docs.snyk.io/integrati....ons/snyk-studio-agen

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

00:00 - Master the First 90% of Building AI Agents
01:38 - The 4 Core Components of AI Agents
03:06 - The First 3 Steps of Building an Agent
04:09 - Building a Basic AI Agent Together Live
09:17 - Choosing Your LLM
10:34 - Crafting Your System Prompt
12:20 - Creating Your Tools (Agent Capabilities)
14:26 - AI Agent Security
15:32 - Guardrails AI
16:45 - Snyk MCP Server
19:45 - Managing Agent Context (Memory)
22:05 - Mem0 for Long Term Agent Memory
23:53 - Agent Observability (with Langfuse)
26:28 - Agent Deployment (with Docker)
28:41 - Final Thoughts

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Join me as I push the limits of what is possible with AI. I'll be uploading videos weekly - at least every Wednesday at 7:00 PM CDT!

MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
** New 2025 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

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