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🔥Professional Certification in Digital Marketing (India Only) - https://www.simplilearn.com/professional-digital-marketing-program?utm_campaign=eDq9GHU84nQ&utm_medium=Lives&utm_source=Youtube

This video on Search Engine Marketing Full Course 2026 by Simplilearn, explains how paid advertising works on search engines like Google. You’ll learn Google Ads basics, keyword targeting, ad creation, bidding strategies, and campaign optimization. The course also covers conversion tracking, performance analysis, and ROI-focused marketing. With real-world examples, you’ll understand how businesses drive traffic and sales using paid search. Perfect for beginners, marketers, and professionals looking to master performance marketing.

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➡️ About Digital Marketing Specialist

Experience live and interactive learning with this comprehensive digital marketing masters program. Learn to leverage ChatGPT and other generative AI tools for digital marketing. Additionally, you will receive a Meta Certified Digital Marketing Associate exam in the program.

Key Features:
✅ Key Features
✅ Industry recognised Digital Marketing Specialist certificate from Simplilearn
✅ Live classes delivered by digital marketing industry experts
✅ Learn to Leverage ChatGPT and the latest generative AI tools for digital marketing
✅ Free Meta Certified Digital Marketing Associate exam voucher worth $99
✅ Learn 35+ digital marketing tools
✅ 5 Capstone problem statements and 15+ course-end projects
✅ 10+ case studies from brands like Adidas, KFC, Nike, Intel, etc.

Skills Covered:
✅ Website Creation
✅ Behavioral Marketing
✅ Keyword Research
✅ Search Engine Optimization
✅ Search Engine Marketing
✅ Campaign Management
✅ Social Media Marketing
✅ Mobile Marketing
✅ Content Marketing
✅ Content Strategy
✅ Strategies for Paid Campaigns
✅ Analytics ROI amp Evaluation
✅ AI Automation amp Emerging Technology
✅ Gen Artificial Intelligence AI Tools

👉 Learn More At: https://www.simplilearn.com/master-in-digital-marketing?utm_campaign=eDq9GHU84nQ&utm_medium=Lives&utm_source=Youtube

🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

In this comprehensive hands-on workshop, Jon Krohn and Ed Donner introduce AI agents, including multi-agent systems. All the essential theory of agentic AI is provided alongside hands-on code demos in Python that feature the critical agent frameworks: MCP, CrewAI and the OpenAI Agents SDK. From design considerations through to practical implementation tips, by completing all four modules in this video, you will have all the knowledge and skills needed to develop and deploy your own multi-agent teams.

The four modules are:
1. Defining Agents (with OpenAI Agents SDK)
2. Designing Agents (with CrewAI)
3. Deploying Agents (with MCP)
4. The Future of Agents

The accompanying slides and code are available in GitHub: github.com/ed-donner/action

Jon Krohn is co-founder and CEO of Y Carrot, a software firm that specializes in designing and implementing agentic AI systems for enterprises. He also serves as ML Practice Fellow at Lightning AI and is the host of SuperDataScience, the data science industry’s most listened-to podcast. He wrote the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He lectures at leading universities (Columbia, NYU), keynotes at top global conferences (Web Summit, Collision) and presents content on Bloomberg TV that’s viewed by millions. Jon holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.

Ed Donner is renowned for his teaching on LLMs and agentic AI via O'Reilly and Udemy. He’s the co-founder and CTO of Nebula.io, the platform that uses generative AI and other forms of machine learning to source, understand, engage and manage talent. Previously, Ed was the founder and CEO of AI startup untapt, an Accenture Fintech Innovation Lab company that was acquired in 2020. Before that, Ed was a Managing Director at JPMorgan Chase, leading a team of 300 software engineers in Risk Technology across three continents, the culmination of a 15-year technology career on Wall Street. Ed holds a patent for a deep learning matching engine issued in 2023 and a master's in physics from Oxford.

This high-quality footage was shot live during a packed house at the Open Data Science Conference (ODSC) East in Boston on May 14th, 2025. The director of photography was Lucie McCormick and media editor was Mario Pombo.

Two bonus goodies below:

Follow this link to get Ed's 17-hour "Complete Agentic AI Engineering Course" on Udemy at a discounted rate (as well as coupon links to all of his Udemy courses): https://edwarddonner.com/2025/....05/28/connecting-my-

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ดอกซ่อนชู้ (The Last Memory) เป็นโศกนาฏกรรมความรักที่เหตุการณ์เกิดขึ้นในช่วงหลังการเปลี่ยนแปลงระบอบการปกครองได้ 3 ปี มาจนถึงปี พ.ศ. 2487 ซึ่งเป็นยุคสงครามโลกครั้งที่ 2 หรือสงครามมหาเอเชียบูรพา ส่องสะท้อนให้เห็นถึงสภาพสังคมและวัฒนธรรมไทยในยุคที่บ้านเมืองไทยกำลังก้าวเข้าสู่ความศิวิไลตามแบบตะวันตก หากแต่คำนิยมเก่า ๆ ยังมีอิทธิพลต่อวิถีชีวิตคนไทยอยู่มาก สิทธิของชายและหญิงยังแตกต่างกันอย่างชัดเจน ผู้ชายสามารถมีภรรยาจำนวนมากได้โดยชอบธรรม ซ้ำยังได้รับการเชิดชูบูชาจากคนในสังคม ในขณะนี้ผู้หญิงนั้นยังถูกลิดรอนสิทธิ หากผู้หญิงคนใดมีชู้จะถูกลงโทษอย่างหนัก และตกเป็นจำเลยของสังคมไปโดยปริยาย

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🔥Intellipaat AI Deep Learning course: https://intellipaat.com/artifi....cial-intelligence-ma
In this deep learning interview questions and answers you will learn the latest and top questions asked by companies for deep learning interview. This deep learning interview questions & answers video covers all kinds of questions starting from basic to advanced questions so that you can get benefited.
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📝Following questions are covered in this deep learning video:
00:00 - Deep Learning Interview Questions And Answers
00:59 - What is the Difference between Machine Learning and Deep Learning?
02:16 - What is Perceptron?
03:26 - How is Deep Learning better than Machine Learning?
04:29 - What are some of the most used applications of Deep Learning?
05:27 - What is the meaning of Over fitting?
06:47 - What are Activation functions?
08:00 - Why is Fourier transform used in Deep Learning?
08:55 - What are the steps involved in training a perceptron in Deep learning?
09:47 - What is the use of the loss function?
10:30 - What are some of the Deep Learning Frameworks or tools that you have used?
11:49 - What is the use of the swish function?
12:38 - What are auto encoders?
13:41 - What are the steps to be followed to use the gradient descent algorithm?
14:57 - Differentiate between a single layer perceptron and a multi-layer perceptron
16:00 - What is data normalization in Deep Learning?
16:54 - What is forward propagation?
17:44 - What is back propagation?
18:40 - What are Hyper parameters in Deep Learning?
19:19 - How can hyper parameters be trained in neural networks?
21:38 - What is the meaning of dropout in Deep Learning?
22:42 - What are Tensors?
23:44 - What is the meaning of model capacity in Deep Learning?
24:33 - What is Boltzmann Machine?
25:25 - What are some of the advantages of using TensorFlow?
26:27 - What is the computational graph in Deep Learning?
27:40 - What is a CNN?
28:25 - What are the various layers present in a CNN?
30:19 - What is an RNN in Deep Learning?
31:15 - What is a Vanishing gradient when using RNNs?
32:11 - What is exploding gradient descent in Deep Learning?
33:10 - What is the use of LSTM?
34:04 - Where are autoencoders used?
35:05 - What are the types of auto encoders?
35:35 - What is a restricted Boltzmann Machine?
36:30 - What are some of the limitations of Deep Learning?
38:04 - What are the variants of gradient descent?
39:33 - Why is mini-batch gradient descent so popular?
40:35 - What are deep autoencoders?
41:47 - Why is the leaky ReLu function used in Deep Learning?
42:35 - What are some of the examples if the supervised learning algorithms in Deep Learning?
43:25 - What are some of the examples of unsupervised learning algorithms in Deep Learning?
43:56 - Can we initialize the weights of a network to start from zero?
45:00 - What is the meaning of valid padding and same padding in CNN?
46:16 - What are some of the applications of transfer learning in Deep Learning?
47:25 - How is the transformer architecture better than RNNs in Deep Learning?
48:41 - What are the steps involved in the working of an LSTM network?
50:07 - What are the elements in TensorFlow that are programmable?
50:43 - What is the meaning of bagging and boosting in Deep Learning?
51:52 - What are generative adversarial networks (GANs)?
53:00 - Have you earned any sort of Certification to improve your learning and implementation process?
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This lecture, by DeepMind Research Scientist Felix Hill, first discusses the motivation for modelling language with ANNs: language is highly contextual, typically non-compositional and relies on reconciling many competing sources of information. This section also covers Elman's Finding Structure in Time and simple recurrent networks, the importance of context and transformers. In the second part, he explores unsupervised and representation learning for language from Word2Vec to BERT. Finally, Felix discusses situated language understanding, grounding and embodied language learning.

Download the slides here:
https://storage.googleapis.com..../deepmind-media/UCLx

Find out more about how DeepMind increases access to science here:
https://deepmind.com/about#access_to_science

Speaker Bio:

Felix Hill is a Research Scientist working on grounded language understanding, and has been at DeepMind for almost 4 years. He studied pure maths as an undergrad, then got very interested in linguistics and psychology after reading the PDP books by McClelland and Rumelhart, so started graduate school at the University of Cambridge, and ended up in the NLP group. To satisfy his interest in artificial neural networks, he visited Yoshua Bengio's lab in 2013 and started a series of collaborations with Kyunghyun Cho and Yoshua applying neural nets to text processing. This led to some of the first work on transfer learning with sentence representations (and a neural crossword solver). He also interned at FAIR in NYC with Jason Weston. At DeepMind, he's worked on developing agents that can understand language in the context of interactive 3D worlds, together with problems relating to mathematical and analogical reasoning.

About the lecture series:

The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Over the past decade, Deep Learning has evolved as the leading artificial intelligence paradigm providing us with the ability to learn complex functions from raw data at unprecedented accuracy and scale. Deep Learning has been applied to problems in object recognition, speech recognition, speech synthesis, forecasting, scientific computing, control and many more. The resulting applications are touching all of our lives in areas such as healthcare and medical research, human-computer interaction, communication, transport, conservation, manufacturing and many other fields of human endeavour. In recognition of this huge impact, the 2019 Turing Award, the highest honour in computing, was awarded to pioneers of Deep Learning.

In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation.

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✅ About Professional Certification In Product Management Program

The Professional Certification in Product Management by SP Jain School Of Global Management prepares you to become a Product Manager via an exhaustive curriculum covering all aspects of product management. This program educates you on the latest industry trends, advancements and related industry applications from Top Product Experts via Live Sessions.

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OpenAI announced GPT-4 yesterday and it's a beast. It can now handle 25K words of context, allowing it to provide more relevant code solutions for programmers.

#tech #chatgpt #thecodereport

💬 Chat with Me on Discord

https://discord.gg/fireship

🔗 Resources

GPT-4 Overview https://openai.com/product/gpt-4
GPT-4 Paper https://paperswithcode.com/pap....er/gpt-4-technical-r
ChatGPT API Tutorial https://youtu.be/e2uvhJ7r1UQ
5 AI Business Ideas https://youtu.be/Aa83vRghue4


🔥 Get More Content - Upgrade to PRO

Upgrade at https://fireship.io/pro
Use code YT25 for 25% off PRO access

🎨 My Editor Settings

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🔖 Topics Covered

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Watch as one of the creators behind Ameca, the lifelike robot that went viral on social media, shows off more of its lifelike behavior at CES 2022. Then we take a stroll through memory lane back to 2019 when the creator of Sophia discusses his thoughts on the future of robotics and AI.

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This Deep Learning tutorial will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input.


Below topics are explained in this Deep Learning Tutorial:
Start (0:00)
1. What is Deep Learning? ( 02:25 )
2. Why do we need Deep Learning? ( 03:42 )
3. Applications of Deep Learning ( 04:55 )
4. What is Neural Network? ( 11:32 )
5. Activation Functions ( 15:50 )
6. Working of Neural Network ( 26:14 )

Check Out Simplilearn Deep Learning Course here: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=What-is-Deep-Learning-FbxTVRfQFuI&utm_medium=Tutorials&utm_source=youtube

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We've partnered with Purdue University and collaborated with IBM to offer you the unique Post Graduate Program in AI and Machine Learning. Learn more about it here - https://www.simplilearn.com/ai-and-machine-learning-post-graduate-certificate-program-purdue?utm_campaign=What-is-Deep-Learning-FbxTVRfQFuI&utm_medium=Tutorials&utm_source=youtube

➡️ 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.

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Пульсирующая черная дыра в центре отдаленной галактики проливает свет как на черные дыры в целом, так и на образование галактик. Как быстро черные дыры вращаются, и как это вращение меняется на протяжении всей их жизни?

Перевод: Юлия Яковлева
Редактура: Алексей Лоскутов
Научная редактура: Кирилл Циберкин
Озвучка: Дмитрий Чепусов
Монтаж звука: Андрей Фокин
Монтаж видео: Джон Исмаилов
Обложка: Андрей Гавриков

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Озвучки Vert Dider выходят с вашей помощью:

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This Machine Learning vs Deep Learning vs Artificial Intelligence video will help you understand the differences between ML, DL, and AI, and how they are related to each other. The tutorial video will also cover what Machine Learning, Deep Learning, and Artificial Intelligence entail, how they work with the help of examples, and whether they really are all that different.

This Machine Learning Vs Deep Learning Vs Artificial Intelligence video will explain the topics listed below:
Start (0:00)
1. Artificial Intelligence example ( 00:29 )
2. Machine Learning example ( 01:29 )
3. Deep Learning example ( 01:44 )
4. Human vs Artificial Intelligence ( 03:34 )
5. How Machine Learning works ( 06:11 )
6. How Deep Learning works ( 07:09 )
7. AI vs Machine Learning vs Deep Learning ( 12:33 )
8. AI with Machine Learning and Deep Learning ( 13:05 )
9. Real-life examples ( 15:29 )
10. Types of Artificial Intelligence ( 17:50 )
11. Types of Machine Learning ( 20:32 )
12. Comparing Machine Learning and Deep Learning ( 22:46 )
13. A glimpse into the future ( 25:46 )

Subscribe to our channel for more Machine Learning & AI Tutorials: https://www.youtube.com/user/S....implilearn?sub_confi

📚ML vs DL vs AI Article: https://bit.ly/3fh6HIP

Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube


You can also go through the Slides here: https://goo.gl/cdQ7uy

#SimplilearnMachineLearning #SimplilearnAI #SimplilearnDeepLearning #Artificialintelligence #MachineLearningTutorial

➡️ 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:




👉 Learn More At:

🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=Description&utm_source=youtube

🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

"🔥Introduction to Generative AI: https://www.edureka.co/introduction-generative-ai?

🔥Google I/O 2024, the annual developer conference held by Google, delivered a multitude of groundbreaking announcements and innovative advancements that are set to shape the future of technology. Held virtually and in-person, this year's event was a blend of immersive experiences, keynote presentations, and hands-on demos that captivated the global tech community. Do watch this video to know the key highlights from Google I/O 2024.

✅ 00:00 - Google I/O 2024
✅ 01:36 - GOOGLE WORKSPACE WITH GEMINI 1.5 Pro
✅ 02:14 - GOOGLE PHOTOS
✅ 03:24 - AI AGENTS
✅ 03:41 - PROJECT ASTRA
✅ 04:31 - Gemini 1.5 Flash
✅ 05:03 - VEO
✅ 05:16 - Google Search with AI
✅ 05:56 - Gems in Gemini 1.5 pro
✅ 06:25 - AI IN ANDROID
✅ 06:47 - Gemma Open Models
✅ 07:14 - Responsible AI


"
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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

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📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN
📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

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

What is Generative AI?

Generative AI is a branch of artificial intelligence that focuses on producing or creating fresh content, such as images, text, audio, or even videos, that closely resembles data generated by humans. In contrast to conventional AI models that learn to identify patterns or make decisions based on existing data (discriminative models), generative AI models are trained to produce new data that mirrors the original training data.
- - - - - - - - - - - - - -
What is Prompt Engineering?‎
Prompt engineering involves optimizing artificial intelligence engineering for multiple purposes. It includes refining large language models (LLMs) using specific prompts and recommended outputs. Additionally, it focuses on enhancing input to different generative AI services to make text or images. With advancements in generative AI tools, prompt engineering becomes crucial for generating diverse content, such as robotic process automation bots, 3D assets, scripts, robot instructions, and various digital artifacts.
How can I get started with Generative AI?
- - - - - - - - - - - - - -
To get started with Introduction to Generative AI, you can:
Enroll in the Introduction to Generative AI Learning Path course.
Read research papers and books on Generative AI.
Experiment with open-source code repositories and frameworks.
Join online communities and forums to learn from others and seek advice.

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

MIT 3.020 Thermodynamics of Materials, Spring 2021
Instructor: Rafael Jaramillo

View the complete course: https://ocw.mit.edu/courses/3-....020-thermodynamics-o
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

This first class session introduces entropy and spontaneous processes, molecular interactions, and enthalpy, as well as the scope and use of thermodynamics.

License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
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We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

This course, from Rola Dali, PhD, provides a comprehensive overview of agentic AI, defining agents as software entities that use LLMs to perceive environments, make decisions, and execute actions to achieve specific goals. It explores the critical distinction between static workflows and dynamic agentic systems, emphasizing how LLMs serve as a reasoning "brain" to decompose tasks at runtime. Through practical Python demonstrations, the course covers essential components like system prompts, tools, and memory, while also comparing architectural patterns such as Supervisor and Swarm. Finally, the session addresses the future of technology by discussing emerging interoperability protocols like MCP and the shifting paradigms of software development in an AI-driven world.

Slides and Labs: https://github.com/rdali/ML105_Agents

Profile: https://www.linkedin.com/in/roladali/

❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

⭐️ Contents ⭐️
- 0:00:00 Introduction and Speaker Background
- 0:01:15 A Brief History of Artificial Intelligence (1940s–Present)
- 0:05:43 Traditional Machine Learning vs. Generative AI
- 0:06:35 The Three Pillars of AI: Algorithms, Data, and Compute
- 0:11:08 Specific Tasks vs. General Task Execution
- 0:14:41 Defining Agency and the Spectrum of Autonomy
- 0:18:00 Agentic Milestone Timeline (2017–2026)
- 0:20:31 What is a Generative AI Agent?
- 0:23:04 Agents vs. Workflows: Dynamic Flow vs. Static Paths
- 0:26:18 Pros and Cons of Agentic Systems
- 0:29:59 Patterns and Anti-patterns: When to Use Agents
- 0:32:36 The Core Components of an Agent
- 0:34:55 Choosing the Right LLM for Your Agent
- 0:37:38 Crafting Identity with System Prompts
- 0:39:00 Understanding Memory: Intrinsic, Short-term, and Long-term
- 0:41:26 Enhancing Capabilities with Tools and Actions
- 0:43:09 Hands-on Implementation: From Single LLM Call to Python Agent
- 0:52:18 Adding Memory and History to Your Custom Agent
- 0:54:53 Building Agents with Frameworks (LangChain)
- 0:57:17 The Evolving Landscape of Models and Frameworks
- 1:00:15 Agentic Architectural Patterns: Supervisor vs. Swarm
- 1:01:41 Case Study: Single Agent vs. Supervisor Architecture
- 1:04:48 Deep Dive: Swarm Architecture Performance
- 1:06:08 When to Choose Multi-agent Systems
- 1:09:05 Interface Protocols: MCP, A2A, and AGUI
- 1:12:06 How to Evaluate Agentic Systems (LLM vs. System vs. App)
- 1:13:53 Evaluation Methods: Code-based, LLM-as-a-Judge, and Human
- 1:15:25 Current Challenges: Hallucinations, Cost, and Debugging
- 1:18:15 Real-world Incidents and the AI Incident Database
- 1:21:28 Career Impact: Which Jobs are Most at Risk?
- 1:23:41 Software 3.0: The Evolution of Development Paradigms
- 1:29:00 Weathering the Storm: Strategies for the Future
- 1:33:40 Beyond LLMs: World Models and the Future of AMI
- 1:37:15 Recommended Resources and Closing Thoughts

🎉 Thanks to our Champion and Sponsor supporters:
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In this step-by-step tutorial, I'll show you how to create a powerful AI agent using Microsoft Copilot Studio in Microsoft Copilot 365, with absolutely no code required! This is Part 1 of our AI agent series with Microsoft Copilot Studio.

We'll build "PoochPal," a friendly AI assistant for our demo dog grooming business, "Pooch Haven Spa." You'll learn how to train your bot with your own website data and documents, customize its personality, and set rules for how it responds to customers.

➡️ Watch Part 2: https://youtu.be/sBapFKSwJ6Q
➡️ Learn more about M365 Copilot: https://youtu.be/j5xQn8Pi_Gw

In this video, you'll learn:
🔹 The difference between Copilot Studio in M365 and the full Copilot Studio.
🔹 How to create your first AI agent with a simple prompt.
🔹 How to add knowledge sources like websites and documents (PDF, Word).
🔹 How to customize your agent's icon, tone, and instructions.
🔹 How to test your AI bot to ensure it's accurate and helpful

0:00 Intro: Build Your Own AI Bot
0:45 Accessing Microsoft 365 Copilot
1:31 Difference between Copilot Studio in M365 vs. Copilot Studio
2:03 Create Your First AI Agent with a Simple Prompt
3:20 Add a custom icon to your agent
4:10 Customize Your Agent's Personality & Rules
5:02 Add Knowledge Sources (Websites & Documents)
6:17 Adjust agent capabilities and suggested prompts
6:48 Test Your AI Bot for Accuracy
7:56 Publish and share your agent