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This video on PMP Certification Course By Simplilearn is led by experts who will help you prepare and pass the PMP exam by Project Management Institute(PMI). This course covers the best practices in project management covered in the 7th Edition of the PMBOK Guide and is aligned with the latest PMP Examination Content Outline. The PMP certification is a globally recognized professional certification in project management. Simplilearn's PMP Certification Training has many features that make it the ideal course to help you prepare for the PMP certification exam. You'll receive 35 contact hours, which is a requirement for taking the PMP exam. You'll have access to digital materials from PMI, which is the governing body for PMP certification.

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#ProjectManagement #ProjectManager #LearnProjectManagement #ProjectManagementTraining #SimplilearmPMP #ProjectManagementCourse #Simplilearn

✅ About PMP Certification Training Course
Simplilearn's PMP training course covers core topics essential for a project management professional. It includes topics such as emerging trends, new technologies and practices, and core competencies required from a project manager. With an emphasis on strategic and business knowledge, the course also highlights the role of a project manager.

✅Key Features
- 35 contact hours
- Access to Digital materials from PMI
- 8 simulation test papers (180 questions each)
- Experiential learning through case studies

✅ Eligibility Criteria
The PMP certification is an essential professional requirement for senior project manager roles across all industries. This course is best suited for: Project Managers, Associate/Assistant Project Managers, Team Leads/Team Managers, Project Executives/Project Engineers, Software Developers, and Any professional aspiring to be a Project Manager.

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In this What Are Transformers In Artificial Intelligence tutorial, we'll delve into the world of Transformers in AI. We'll unravel the intricate workings of these powerful models, from their foundational architecture to their applications in natural language processing, computer vision, and beyond. By the end, you'll grasp not only how Transformers operate but also why they've become a cornerstone in modern AI research and development. So, buckle up as we embark on this enlightening exploration through the realm of Transformers in AI.

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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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Correction: At 30:42 I write "X = Y". They're not equal, what I meant to say is "X and Y are identically distributed".

The variance is a measure of how spread out a distribution is. In order to estimate the variance, one takes a sample of n points from the distribution, and calculate the average square deviation from the mean.
However, this doesn't give a good estimate of the variance of the distribution. The best estimate, however, is obtained when dividing by n-1 instead of n.
WHY!?!?!?!?!?!?!?
In this video, we dig deeper into why the variance calculation should be divided by n-1 instead of by n. For this, we use an alternate definition of the variance, which doesn't use the mean in its calculation.

*[0:00] Introduction and Bessel's Correction*
- Introducing Bessel's Correction and why we divide by \( n-1 \) instead of \( n \) to estimate variance.

*[0:12] Introduction to Variance Calculation*
- Explaining the premise of calculating variance and introducing the concept of estimating variance using a sample instead of the entire population.

*[1:01] Definition of Variance*
- Defining variance as a measure of how much values deviate from the mean and outlining the basic steps of variance calculation.

*[1:52] Introduction to Bessel's Correction*
- Discussing why we divide by \( n-1 \) when calculating variance and introducing Bessel's Correction.

*[2:35] Challenges of Bessel's Correction*
- Sharing personal challenges in understanding the rationale behind Bessel's Correction and discussing my research process on the topic.

*[3:20] Alternative Definition of Variance*
- Presenting an alternative definition of variance to aid in understanding Bessel's Correction and expressing curiosity about its presence in the literature.

*[4:45] Quick Recap of Mean and Variance*
- Briefly revisiting the concepts of mean and variance, demonstrating how they are calculated with examples, and explaining how variance reflects different distributions.

*[7:05] Sample Mean and Variance Estimation*
- Explaining the challenges of estimating the mean and variance of a distribution using a sample and discussing why sample variance is not a good estimate.

*[8:49] Bessel's Correction and Why \( n-1 \) is Used*
- Explaining how Bessel's Correction provides a better estimate of variance and why we divide by \( n-1 \) instead of \( n \). Emphasizing the importance of making a correct variance estimate.

*[10:51] Why Better Estimation Matters?*
- Discussing why the original estimate is poor and why making a better estimate is crucial. Explaining the significance of sample mean as a good estimate.

*[13:02] Issues with Variance Estimation*
- Illustrating the problems with variance estimation and demonstrating with examples why using the correct mean is essential for accurate estimates. Explaining the accuracy of estimates made using \( n-1 \).

*[15:04] Introduction to Correcting the Estimate*
- Discussing the underestimated variance and the need for correction in estimation.

*[15:57] Adjusting the Variance Formula*
- Explaining the adjustment in the variance formula by changing the denominator from \( n \) to \( n - 1 \).

*[16:22] Calculation Illustration*
- Demonstrating the calculation process of variance with the adjusted formula using examples.

*[16:57] Better Estimate with Bessel's Correction*
- Discussing how the corrected estimate provides a more accurate variance estimation.

*[18:24] New Method for Variance Calculation*
- Introducing a new method for calculating variance without explicitly calculating the mean.

*[20:06] Understanding the Relation between Variance and Variance*
- Explaining the relationship between variance and variance, and how they are related mathematically.

*[21:52] Demonstrating a Bad Calculation*
- Illustrating a flawed method for calculating variance and explaining the need for correction.

*[23:37] The Role of Bessel's Correction*
- Explaining why removing unnecessary zeros in variance calculation leads to better estimates, equivalent to Bessel's Correction.

*[25:08] Summary of Estimation Methods*
- Summarizing the difference between the flawed and corrected estimation methods for variance.

*[26:02] Importance of Bessel's Correction*
- Emphasizing the significance of Bessel's Correction for accurate variance estimation, especially with smaller sample sizes.

*[30:19] Mathematical Proof of Variance Relationship*
- Providing two proofs of the relationship between variance and variance, highlighting their equivalence.

*[35:24] Acknowledgments and Conclusion*

Thanks @mkan543 for the summary!

Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt

A friendly explanation of how computer recognize images, based on Convolutional Neural Networks.
All the math required is knowing how to add and subtract 1's. (Bonus if you know calculus, but not needed.)
For a brush up on Neural Networks, check out this video: https://www.youtube.com/watch?v=BR9h47Jtqyw

For a code implementation, check out this repo:
https://github.com/luisguiserr....ano/manning/tree/mas

0:00 Introduction
0:22 Simple World
1:05 Keyboard
1:33 Image recognition software
4:39 Image Recognition Classifier
6:12 Artificial Intelligence
8:47 Gradient Descent
10:26 Slightly More Complex World
11:47 Previous Knowledge
24:27 Convolutional Neural Network
28:27 Advanced World

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

A brief introduction into the concepts behind deep learning.

Github repo:
https://github.com/mlberkeley/intro-dl-workshop

Sebastian's books: https://sebastianraschka.com/books/
The lecture slides are available at: https://github.com/rasbt/stat4....53-deep-learning-ss2

Introduces the main ideas behind Convolutional Neural Networks (CNNs). The topics are:

Challenges of Image Classification
Convolutional Neural Network Basics
CNN Architectures
What a CNN Can See
CNNs in PyTorch

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Video on Supply Chain Management will enable you to comprehend supply chain management in detail. This video will equip you with a good understanding of What Is Supply Chain Management using Apple INC's case study. The case study will emphasize the iPhone 13 pro's supply delays and production cut. You will also learn about the reasons behind the production cut and how a sleek supply chain management strategy can be created.

Please share your feedback below and don't forget to take the quiz at 04:44!

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#SupplyChainManagement #WhatIsSupplyChainManagement #SupplyChainManagementExplained #SupplyChainManagementLecture #Simplilearn

✅What Is Supply Chain Management?
The term supply chain is a connected network of individuals, organizations, resources, activities, technologies involved in the manufacturing and sales of a product or service. Companies develop supply chains so that they can reduce their costs and remain competitive in the business landscape.

✅What Are The Stages of Supply Chain Management?
Five stages altogether formulate a supply chain management strategy. You will now discover them in brief:

⏩ Planning Operational Strategy: The main focus at this stage is to focus on designing a strategy that yields maximum profit.

⏩ Sourcing: Following the planning, the next phase is to develop or source. At this stage, you are primarily concerned with developing strong relationships with raw material suppliers.

⏩ Manufacturing (Making): The products are created, manufactured, tested, packaged, and synchronized for delivery at this step. The supply chain managers are tasked with arranging all manufacturing, testing, packaging, and delivery preparation activities.

⏩ Delivery: This phase deals with product sales and distribution of products across all retail stores. It is a logistics phase, where consumer orders are approved, and product delivery is set into motion.

⏩ Returns: This last phase controls the return of defective or damaged products by consumers. This level of the supply chain is frequently a source of contention for many businesses. Supply chain planners must devise a responsive and adaptable network for receiving damaged, faulty, and additional items from consumers and expediting the return procedure for customers concerned with supplied products.

✅Why Supply Chain Management Is Important?
Supply chain management is critical for any organization. The following are some of the advantages that a well-managed supply chain can provide:
-An adequate supply chain can enhance product quality by regulating manufacturing processes.
-A well-managed supply chain can lower the risk of recalls and litigation by enhancing customer satisfaction.
-Control over shipping methods will remove the possibility of inventory overstock.
-Helps organizations in enhancing their profit margins.
-It can help attain a wide range of organizational objectives.


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Unpacking how large language models work under the hood
Early view of the next chapter for patrons: https://3b1b.co/early-attention
Special thanks to these supporters: https://3b1b.co/lessons/gpt#thanks

To contribute edits to the subtitles, visit https://translate.3blue1brown.com/

Other recommended resources on the topic.

Richard Turner's introduction is one of the best starting places:
https://arxiv.org/pdf/2304.10557.pdf

Coding a GPT with Andrej Karpathy
https://youtu.be/kCc8FmEb1nY

Introduction to self-attention by John Hewitt
https://web.stanford.edu/class..../cs224n/readings/cs2

History of language models by Brit Cruise:
https://youtu.be/OFS90-FX6pg

Paper about examples like the “woman - man” one presented here:
https://arxiv.org/pdf/1301.3781.pdf

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

Timestamps

0:00 - Predict, sample, repeat
3:03 - Inside a transformer
6:36 - Chapter layout
7:20 - The premise of Deep Learning
12:27 - Word embeddings
18:25 - Embeddings beyond words
20:22 - Unembedding
22:22 - Softmax with temperature
26:03 - Up next

------------------
These animations are largely made using a custom Python library, manim. See the FAQ comments here:
https://3b1b.co/faq#manim
https://github.com/3b1b/manim
https://github.com/ManimCommunity/manim/

All code for specific videos is visible here:
https://github.com/3b1b/videos/

The music is by Vincent Rubinetti.
https://www.vincentrubinetti.com
https://vincerubinetti.bandcam....p.com/album/the-musi
https://open.spotify.com/album..../1dVyjwS8FBqXhRunaG5

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

3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly.

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#ArtificialIntelligenceFullCourse #ArtificialIntelligenceCourse #AICourse #AIFullCourse #AITutorial #AITutorialForBeginners #intellipaat

Welcome to Intellipaat's Artificial Intelligence Full Course 2024— an in-depth guide designed for absolute beginners looking to master AI and machine learning! This course covers everything you need to kickstart your journey in AI, from foundational concepts in artificial intelligence and machine learning to hands-on tutorials. Throughout this AI course for beginners, you'll explore different types of machine learning, Artificial Neural Networks, Deep Learning Fundamentals, Keras Library for AI, RNNs, GPU in Deep Learning, etc. to excel in today’s AI-driven world. Our course is ideal for anyone who wants to learn artificial intelligence from scratch and gain practical knowledge for real-world AI projects.

📖Below are the topics covered in this 'Artificial Intelligence Full Course 2024' video:
00:00:00 - Introduction to Artificial Intelligence Course
00:03:29 - What is Artificial Intelligence
00:07:17 - Why Artificial Intelligence
00:57:05 - Machine Learning Types
01:07:35 - Introduction to Deep Learning
02:45:16 - Benefits of Using Artificial Neural Network
02:50:26 - Deep Learning Frameworks
03:02:57 - Data Handling with NumPy
03:20:28 - Introduction to TensorFlow
05:23:00 - Understanding Epoch
05:50:38 - Introduction to Keras
06:43:35 - Predefined Neural Network Layers
08:43:11 - Problem With a Fully Connected Network
08:52:56 - Convolutional Neural Network
10:13:25 - Rectified Linear Units
10:45:45 - Artificial Intelligence Interview Questions

✅ Is AI hard to study?
Studying AI can be challenging, as it involves complex topics like programming, mathematics (especially statistics and linear algebra), and algorithms. However, with structured learning paths and accessible resources, it’s possible for beginners to grasp these concepts over time. Consistent practice and hands-on projects help make the learning process easier and more practical. Ultimately, while AI may seem difficult, determination and incremental learning make it achievable.

✅ What is an AI course?
An AI course is a structured learning program designed to teach the fundamentals and advanced concepts of artificial intelligence. It typically covers topics like machine learning, neural networks, data analysis, and programming with Python or other languages. Many AI courses include practical projects, giving learners hands-on experience in building AI models. These courses help individuals gain the skills needed for AI-related careers, such as data science or machine learning engineering.

➡️ About the Course
This Artificial Intelligence course, in collaboration with IITM Pravartak, is specially designed to help learners upgrade their skills in artificial intelligence, deep learning, and generative AI.

➡️ Key Features - (Course Features)
👉🏼 Learn AI from eminent IIT Madras Faculty and top industry experts
👉🏼 Master & get hands on experience on Deep Learning, Computer Vision, TensorFlow, Neural Networks, Generative AI, etc.
👉🏼 2 days campus immersion at IIT Madras Research Park
👉🏼 3 Guaranteed interviews upon movement to the placement pool
👉🏼 Earn the Certification in Data Science & Artificial Intelligence from IITM Pravartak (A Technology Innovation Hub of IIT Madras)

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✅ Programming Essentials For Artificial Intelligence
✅ Data Transformation & Management Using SQL
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✅ Generative AI Fundamentals
✅ Natural Language Processing
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✅ Reinforcement Learning
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Unlock the secrets of AI model fine-tuning in this easy-to-follow guide! Learn how to:

• Customize AI responses without complex coding
• Create your own dataset for personalized results
• Fine-tune Mistral using MLX on Apple Silicon
• Implement your fine-tuned model with Ollama

Discover why fine-tuning isn't as daunting as it seems, and how you can tweak AI models to match your unique style. Perfect for beginners and those intimidated by traditional Python notebook tutorials.

Don't miss this opportunity to level up your AI skills and create models that truly understand you!

#AITutorial #MachineLearning #FineTuning #MistralAI #AppleSilicon
t

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00:00 - AI is Amazing
00:26 - Two approaches to tweaking models
00:36 - What is fine tuning
00:57 - Why is it hard to get started
01:12 - The biggest problem
01:39 - Just 3 steps
01:52 - The hardest part
02:09 - Start with step 1
02:27 - How to figure out what to do
03:51 - My first fine tune
04:44 - What to put where
04:58 - Move on to the next step
05:31 - Huggingface login
05:48 - The mlx command
06:43 - The results
06:56 - Define the new model
07:32 - A couple of gotchas

Read the full list of tips: http://builder.io/blog/claude-code

MIT 8.04 Quantum Physics I, Spring 2013
View the complete course: http://ocw.mit.edu/8-04S13
Instructor: Allan Adams

In this lecture, Prof. Adams introduces wave functions as the fundamental quantity in describing quantum systems. Basic properties of wavefunctions are covered. Uncertainty and superposition are reiterated in the language of wavefunctions.

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

🔥Agentic AI Training Course - Master AI Agents: https://www.edureka.co/agentic-ai-training-course
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Explore the future of artificial intelligence with Agentic AI! In this video, we dive into the exciting developments and advancements that are set to shape the industry in 2026. We will learn what Agentic AI is and how it goes beyond traditional AI by acting with purpose and autonomy. You’ll discover 5 powerful secrets behind Agentic AI—from multi-agent collaboration to real-world tool integration and ethical guardrails. By the end, you’ll understand how Agentic AI is transforming industries and why it’s the future of intelligent systems.

Join us as we discuss the latest trends, innovations, and predictions for Agentic AI in 2026. Whether you're an AI enthusiast, a business leader, or simply curious about the future of technology, this video is for you. Stay ahead of the curve and discover what's next for Agentic AI!

00:00:00 Introduction
00:01:28 What is Agentic AI?
00:12:20 Agentic AI vs Generative AI
00:33:33 Alexa+ Powered by Generative AI
00:44:47 Agentic AI Roadmap
00:51:15 Introduction to Artificial Intelligence
01:10:03 Introduction to Deep Learning
02:27:26 Artificial Neural Network
02:59:26 Transformers Explained Using Generative AI
03:07:05 Transformers Neural Networks Explained
03:14:29 What are Large Language Models?
03:35:28 What is Multimodal AI?
03:52:46 LLM vs SLM
03:58:14 What is LangChain?
04:15:29 Langchain Agents Explained
04:22:44 What is RAG?
04:45:32 LLMOps: The Future of AI Development
04:51:55 Prompt Engineering
05:06:09 Natural Language Processing (NLP) & Text Mining using NLTK
05:44:52 KNN Algorithm using Python
06:03:11 Alibaba’s Qwen 2.5-Max Just Beat GPT-4 & DeepSeek?
06:07:35 DeepSeek Training Cost: How China Built AI for Less
06:13:04 DeepSeek vs OpenAI: Who Wins the AI Race?
06:25:36 Deep Learning Interview Questions

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What is Agentic AI?
Agentic AI refers to artificial intelligence systems that can autonomously make decisions, take actions, and pursue goals with minimal human intervention. Unlike traditional AI, which adheres to predetermined rules, agentic AI may dynamically adapt to new situations. It is widely utilized in robotics, virtual assistants, self-driving cars, and sophisticated decision-making processes.

What are the prerequisites for this Agentic AI Training Course?
In order to complete this course successfully, participants need to have a basic understanding of the Python programming language, machine learning, deep learning, natural language processing, generative AI, and prompt engineering concepts.

For more information, please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: +18885487823(toll-free).

#agenticai #aiagents #agenticaicourse #generativeai